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You searched for +publisher:"New Jersey Institute of Technology" +contributor:("Jason T. L. Wang"). Showing records 1 – 30 of 61 total matches.

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New Jersey Institute of Technology

1. Byron, Kevin. Big data analytics in computational biology and bioinformatics.

Degree: PhD, Computer Science, 2017, New Jersey Institute of Technology

  Big data analytics in computational biology and bioinformatics refers to an array of operations including biological pattern discovery, classification, prediction, inference, clustering as well… (more)

Subjects/Keywords: Gene regulatory network; Reverse engineering; Data mining; Noncoding rna; Big data; Pattern discovery; Computer Sciences

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APA (6th Edition):

Byron, K. (2017). Big data analytics in computational biology and bioinformatics. (Doctoral Dissertation). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/dissertations/17

Chicago Manual of Style (16th Edition):

Byron, Kevin. “Big data analytics in computational biology and bioinformatics.” 2017. Doctoral Dissertation, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/dissertations/17.

MLA Handbook (7th Edition):

Byron, Kevin. “Big data analytics in computational biology and bioinformatics.” 2017. Web. 22 Oct 2019.

Vancouver:

Byron K. Big data analytics in computational biology and bioinformatics. [Internet] [Doctoral dissertation]. New Jersey Institute of Technology; 2017. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/dissertations/17.

Council of Science Editors:

Byron K. Big data analytics in computational biology and bioinformatics. [Doctoral Dissertation]. New Jersey Institute of Technology; 2017. Available from: https://digitalcommons.njit.edu/dissertations/17


New Jersey Institute of Technology

2. Turki, Turki Talal. Development and evaluation of machine learning algorithms for biomedical applications.

Degree: PhD, Computer Science, 2017, New Jersey Institute of Technology

  Gene network inference and drug response prediction are two important problems in computational biomedicine. The former helps scientists better understand the functional elements and… (more)

Subjects/Keywords: Machine learning; Data mining; Medical informatics; Bioinformatics; Systems biology; Precision medicine; Computer Sciences

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APA (6th Edition):

Turki, T. T. (2017). Development and evaluation of machine learning algorithms for biomedical applications. (Doctoral Dissertation). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/dissertations/26

Chicago Manual of Style (16th Edition):

Turki, Turki Talal. “Development and evaluation of machine learning algorithms for biomedical applications.” 2017. Doctoral Dissertation, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/dissertations/26.

MLA Handbook (7th Edition):

Turki, Turki Talal. “Development and evaluation of machine learning algorithms for biomedical applications.” 2017. Web. 22 Oct 2019.

Vancouver:

Turki TT. Development and evaluation of machine learning algorithms for biomedical applications. [Internet] [Doctoral dissertation]. New Jersey Institute of Technology; 2017. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/dissertations/26.

Council of Science Editors:

Turki TT. Development and evaluation of machine learning algorithms for biomedical applications. [Doctoral Dissertation]. New Jersey Institute of Technology; 2017. Available from: https://digitalcommons.njit.edu/dissertations/26


New Jersey Institute of Technology

3. Hua, Lei. A data science approach to pattern discovery in complex structures with applications in bioinformatics.

Degree: PhD, Computer Science, 2016, New Jersey Institute of Technology

  Pattern discovery aims to find interesting, non-trivial, implicit, previously unknown and potentially useful patterns in data. This dissertation presents a data science approach for… (more)

Subjects/Keywords: RNA; Secondary structure; Tree pattern; Coaxial helical stacking; Dynamic programming; Pattern discovery; Computer Sciences

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APA (6th Edition):

Hua, L. (2016). A data science approach to pattern discovery in complex structures with applications in bioinformatics. (Doctoral Dissertation). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/dissertations/70

Chicago Manual of Style (16th Edition):

Hua, Lei. “A data science approach to pattern discovery in complex structures with applications in bioinformatics.” 2016. Doctoral Dissertation, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/dissertations/70.

MLA Handbook (7th Edition):

Hua, Lei. “A data science approach to pattern discovery in complex structures with applications in bioinformatics.” 2016. Web. 22 Oct 2019.

Vancouver:

Hua L. A data science approach to pattern discovery in complex structures with applications in bioinformatics. [Internet] [Doctoral dissertation]. New Jersey Institute of Technology; 2016. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/dissertations/70.

Council of Science Editors:

Hua L. A data science approach to pattern discovery in complex structures with applications in bioinformatics. [Doctoral Dissertation]. New Jersey Institute of Technology; 2016. Available from: https://digitalcommons.njit.edu/dissertations/70


New Jersey Institute of Technology

4. Song, Yang. Data mining in computational proteomics and genomics.

Degree: PhD, Computer Science, 2015, New Jersey Institute of Technology

  This dissertation addresses data mining in bioinformatics by investigating two important problems, namely peak detection and structure matching. Peak detection is useful for biological… (more)

Subjects/Keywords: Data mining; Algorithm; Bioinformatics; Time series; RNA Pseudoknot; Computer Sciences

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APA (6th Edition):

Song, Y. (2015). Data mining in computational proteomics and genomics. (Doctoral Dissertation). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/dissertations/125

Chicago Manual of Style (16th Edition):

Song, Yang. “Data mining in computational proteomics and genomics.” 2015. Doctoral Dissertation, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/dissertations/125.

MLA Handbook (7th Edition):

Song, Yang. “Data mining in computational proteomics and genomics.” 2015. Web. 22 Oct 2019.

Vancouver:

Song Y. Data mining in computational proteomics and genomics. [Internet] [Doctoral dissertation]. New Jersey Institute of Technology; 2015. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/dissertations/125.

Council of Science Editors:

Song Y. Data mining in computational proteomics and genomics. [Doctoral Dissertation]. New Jersey Institute of Technology; 2015. Available from: https://digitalcommons.njit.edu/dissertations/125


New Jersey Institute of Technology

5. Wang, Wei. Computational methods for the analysis of next generation sequencing data.

Degree: PhD, Computer Science, 2014, New Jersey Institute of Technology

  Recently, next generation sequencing (NGS) technology has emerged as a powerful approach and dramatically transformed biomedical research in an unprecedented scale. NGS is expected… (more)

Subjects/Keywords: Next generation sequencing; Change-point model; Bioinformatics; Variant detection; Statistical modeliing; Alternate polyadenlyation; Computer Sciences

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APA (6th Edition):

Wang, W. (2014). Computational methods for the analysis of next generation sequencing data. (Doctoral Dissertation). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/dissertations/169

Chicago Manual of Style (16th Edition):

Wang, Wei. “Computational methods for the analysis of next generation sequencing data.” 2014. Doctoral Dissertation, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/dissertations/169.

MLA Handbook (7th Edition):

Wang, Wei. “Computational methods for the analysis of next generation sequencing data.” 2014. Web. 22 Oct 2019.

Vancouver:

Wang W. Computational methods for the analysis of next generation sequencing data. [Internet] [Doctoral dissertation]. New Jersey Institute of Technology; 2014. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/dissertations/169.

Council of Science Editors:

Wang W. Computational methods for the analysis of next generation sequencing data. [Doctoral Dissertation]. New Jersey Institute of Technology; 2014. Available from: https://digitalcommons.njit.edu/dissertations/169


New Jersey Institute of Technology

6. Wang, Yiran. Matrix completion algorithms with applications in biomedicine, e-commerce and social science.

Degree: MSin Computer Science - (M.S.), Computer Science, 2017, New Jersey Institute of Technology

  This thesis investigates matrix completion algorithms with applications in biomedicine, e-commerce and social science. In general, matrix completion algorithms work well for low rank… (more)

Subjects/Keywords: Matrix completion algorithms; Computer Sciences

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APA (6th Edition):

Wang, Y. (2017). Matrix completion algorithms with applications in biomedicine, e-commerce and social science. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/37

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wang, Yiran. “Matrix completion algorithms with applications in biomedicine, e-commerce and social science.” 2017. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/37.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wang, Yiran. “Matrix completion algorithms with applications in biomedicine, e-commerce and social science.” 2017. Web. 22 Oct 2019.

Vancouver:

Wang Y. Matrix completion algorithms with applications in biomedicine, e-commerce and social science. [Internet] [Thesis]. New Jersey Institute of Technology; 2017. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/37.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang Y. Matrix completion algorithms with applications in biomedicine, e-commerce and social science. [Thesis]. New Jersey Institute of Technology; 2017. Available from: https://digitalcommons.njit.edu/theses/37

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

7. Wen, Dongrong. Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis.

Degree: PhD, Computer Science, 2011, New Jersey Institute of Technology

  RNA secondary and tertiary structure motifs play important roles in cells. However, very few web servers are available for RNA motif search and prediction.… (more)

Subjects/Keywords: RNA motif; RNA motif search; Secondary structure; Motif prediction; Tertiary structure; Computer Sciences

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APA (6th Edition):

Wen, D. (2011). Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis. (Doctoral Dissertation). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/dissertations/335

Chicago Manual of Style (16th Edition):

Wen, Dongrong. “Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis.” 2011. Doctoral Dissertation, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/dissertations/335.

MLA Handbook (7th Edition):

Wen, Dongrong. “Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis.” 2011. Web. 22 Oct 2019.

Vancouver:

Wen D. Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis. [Internet] [Doctoral dissertation]. New Jersey Institute of Technology; 2011. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/dissertations/335.

Council of Science Editors:

Wen D. Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis. [Doctoral Dissertation]. New Jersey Institute of Technology; 2011. Available from: https://digitalcommons.njit.edu/dissertations/335


New Jersey Institute of Technology

8. Garg, Paras. Type-1 diabetes risk prediction using multiple kernel learning.

Degree: MSin Bioinformatics - (M.S.), Computer Science, 2010, New Jersey Institute of Technology

  This thesis presents an analysis of multiple kernel learning (MKL) for type-1 diabetes risk prediction. MKL combines different models and representation of data to… (more)

Subjects/Keywords: Diabetes risk prediction; Multiple kernal learning; Bioinformatics; Computer Sciences

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APA (6th Edition):

Garg, P. (2010). Type-1 diabetes risk prediction using multiple kernel learning. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/56

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Garg, Paras. “Type-1 diabetes risk prediction using multiple kernel learning.” 2010. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/56.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Garg, Paras. “Type-1 diabetes risk prediction using multiple kernel learning.” 2010. Web. 22 Oct 2019.

Vancouver:

Garg P. Type-1 diabetes risk prediction using multiple kernel learning. [Internet] [Thesis]. New Jersey Institute of Technology; 2010. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/56.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Garg P. Type-1 diabetes risk prediction using multiple kernel learning. [Thesis]. New Jersey Institute of Technology; 2010. Available from: https://digitalcommons.njit.edu/theses/56

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

9. Malhotra, Ankur. Aminormotiffinder - a graph grammar based tool to effectively search a minor motifs in 3D RNA molecules.

Degree: MSin Bioinformatics - (M.S.), Computer Science, 2010, New Jersey Institute of Technology

  RNA Motifs are three dimensional folds that play important role in RNA folding and its interaction with other molecules. They basically have modular structure… (more)

Subjects/Keywords: RNA Motif identification; Bioinformatics; Computer Sciences

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APA (6th Edition):

Malhotra, A. (2010). Aminormotiffinder - a graph grammar based tool to effectively search a minor motifs in 3D RNA molecules. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/82

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Malhotra, Ankur. “Aminormotiffinder - a graph grammar based tool to effectively search a minor motifs in 3D RNA molecules.” 2010. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/82.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Malhotra, Ankur. “Aminormotiffinder - a graph grammar based tool to effectively search a minor motifs in 3D RNA molecules.” 2010. Web. 22 Oct 2019.

Vancouver:

Malhotra A. Aminormotiffinder - a graph grammar based tool to effectively search a minor motifs in 3D RNA molecules. [Internet] [Thesis]. New Jersey Institute of Technology; 2010. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/82.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Malhotra A. Aminormotiffinder - a graph grammar based tool to effectively search a minor motifs in 3D RNA molecules. [Thesis]. New Jersey Institute of Technology; 2010. Available from: https://digitalcommons.njit.edu/theses/82

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

10. Prasad, Meera. Fast program for sequence alignment using partition function posterior probabilities.

Degree: MSin Bioinformatics - (M.S.), Computer Science, 2011, New Jersey Institute of Technology

  The key requirements of a good sequence alignment tool are high accuracy and fast execution. The existing Probalign program is a highly accurate tool… (more)

Subjects/Keywords: Sequence alignment; Probalign; Partition function posterior probabilities; Bioinformatics; Computer Sciences

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APA (6th Edition):

Prasad, M. (2011). Fast program for sequence alignment using partition function posterior probabilities. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/94

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Prasad, Meera. “Fast program for sequence alignment using partition function posterior probabilities.” 2011. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/94.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Prasad, Meera. “Fast program for sequence alignment using partition function posterior probabilities.” 2011. Web. 22 Oct 2019.

Vancouver:

Prasad M. Fast program for sequence alignment using partition function posterior probabilities. [Internet] [Thesis]. New Jersey Institute of Technology; 2011. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/94.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Prasad M. Fast program for sequence alignment using partition function posterior probabilities. [Thesis]. New Jersey Institute of Technology; 2011. Available from: https://digitalcommons.njit.edu/theses/94

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

11. Singh, Neha. A comparative analysis of machine learning algorithms for genome wide association studies.

Degree: MSin Bioinformatics - (M.S.), Computer Science, 2012, New Jersey Institute of Technology

  Variations present in human genome play a vital role in the emergence of genetic disorders and abnormal traits. Single Nucleotide Polymorphism (SNP) is considered… (more)

Subjects/Keywords: Machine learning algorithms; Genome Wide Association Studies; Comparative analysis; Bioinformatics; Computer Sciences

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APA (6th Edition):

Singh, N. (2012). A comparative analysis of machine learning algorithms for genome wide association studies. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/129

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Singh, Neha. “A comparative analysis of machine learning algorithms for genome wide association studies.” 2012. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/129.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Singh, Neha. “A comparative analysis of machine learning algorithms for genome wide association studies.” 2012. Web. 22 Oct 2019.

Vancouver:

Singh N. A comparative analysis of machine learning algorithms for genome wide association studies. [Internet] [Thesis]. New Jersey Institute of Technology; 2012. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/129.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Singh N. A comparative analysis of machine learning algorithms for genome wide association studies. [Thesis]. New Jersey Institute of Technology; 2012. Available from: https://digitalcommons.njit.edu/theses/129

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

12. Roberts, Andrew. Phenotype prediction and feature selection in genome-wide association studies.

Degree: MSin Bioinformatics - (M.S.), Computer Science, 2012, New Jersey Institute of Technology

  Genome wide association studies (GWAS) search for correlations between single nucleotide polymorphisms (SNPs) in a subject genome and an observed phenotype. GWAS can be… (more)

Subjects/Keywords: Genome wide association studies; Single nucleotide polymorphisms; Phenotype prediction; Bioinformatics; Computer Sciences

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APA (6th Edition):

Roberts, A. (2012). Phenotype prediction and feature selection in genome-wide association studies. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/130

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Roberts, Andrew. “Phenotype prediction and feature selection in genome-wide association studies.” 2012. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/130.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Roberts, Andrew. “Phenotype prediction and feature selection in genome-wide association studies.” 2012. Web. 22 Oct 2019.

Vancouver:

Roberts A. Phenotype prediction and feature selection in genome-wide association studies. [Internet] [Thesis]. New Jersey Institute of Technology; 2012. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/130.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Roberts A. Phenotype prediction and feature selection in genome-wide association studies. [Thesis]. New Jersey Institute of Technology; 2012. Available from: https://digitalcommons.njit.edu/theses/130

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

13. Ramazanoglu, Sinan. Data mining of tetraloop-tetraloop receptors in RNA XML files.

Degree: MSin Bioinformatics - (M.S.), Computer Science, 2012, New Jersey Institute of Technology

  RNA (Ribonucleic acid) Motifs are tertiary structures that play an important role in the folding mechanism of the RNA molecule. The overall function of… (more)

Subjects/Keywords: Data mining; Ribonucleic acid motifs; Tetraloop-tetraloop receptor; Bioinformatics; Computer Sciences

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APA (6th Edition):

Ramazanoglu, S. (2012). Data mining of tetraloop-tetraloop receptors in RNA XML files. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/131

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ramazanoglu, Sinan. “Data mining of tetraloop-tetraloop receptors in RNA XML files.” 2012. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/131.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ramazanoglu, Sinan. “Data mining of tetraloop-tetraloop receptors in RNA XML files.” 2012. Web. 22 Oct 2019.

Vancouver:

Ramazanoglu S. Data mining of tetraloop-tetraloop receptors in RNA XML files. [Internet] [Thesis]. New Jersey Institute of Technology; 2012. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/131.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ramazanoglu S. Data mining of tetraloop-tetraloop receptors in RNA XML files. [Thesis]. New Jersey Institute of Technology; 2012. Available from: https://digitalcommons.njit.edu/theses/131

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

14. Vasavada, Meghana S. Genome wide search for pseudo knotted non-coding RNAs.

Degree: MSin Bioinformatics - (M.S.), Computer Science, 2013, New Jersey Institute of Technology

  Non-coding RNAs (ncRNAs) are the functional RNA molecules that are involved in many biological processes including gene regulation, chromosome replication and RNA modification. Searching… (more)

Subjects/Keywords: Non-coding RNAs; NcRNA detection; Genome wide searching; Bioinformatics; Computer Sciences

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APA (6th Edition):

Vasavada, M. S. (2013). Genome wide search for pseudo knotted non-coding RNAs. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/159

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Vasavada, Meghana S. “Genome wide search for pseudo knotted non-coding RNAs.” 2013. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/159.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Vasavada, Meghana S. “Genome wide search for pseudo knotted non-coding RNAs.” 2013. Web. 22 Oct 2019.

Vancouver:

Vasavada MS. Genome wide search for pseudo knotted non-coding RNAs. [Internet] [Thesis]. New Jersey Institute of Technology; 2013. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/159.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vasavada MS. Genome wide search for pseudo knotted non-coding RNAs. [Thesis]. New Jersey Institute of Technology; 2013. Available from: https://digitalcommons.njit.edu/theses/159

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

15. Ling, Xiao. Polyaseeker: a computational framework for identifying polyadenylation cleavage site from RNA-seq.

Degree: MSin Bioinformatics - (M.S.), Computer Science, 2013, New Jersey Institute of Technology

  Alternative polyadenylation (APA) of mRNA plays a crucial role for post-transcriptional gene regulation. Recently, advances in next generation sequencing technology have made it possible… (more)

Subjects/Keywords: Post-transcriptional gene regulation; Alternative polyadenylation (APA) of mRNA; Bioinformatics; Computer Sciences

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APA (6th Edition):

Ling, X. (2013). Polyaseeker: a computational framework for identifying polyadenylation cleavage site from RNA-seq. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/169

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ling, Xiao. “Polyaseeker: a computational framework for identifying polyadenylation cleavage site from RNA-seq.” 2013. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/169.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ling, Xiao. “Polyaseeker: a computational framework for identifying polyadenylation cleavage site from RNA-seq.” 2013. Web. 22 Oct 2019.

Vancouver:

Ling X. Polyaseeker: a computational framework for identifying polyadenylation cleavage site from RNA-seq. [Internet] [Thesis]. New Jersey Institute of Technology; 2013. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/169.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ling X. Polyaseeker: a computational framework for identifying polyadenylation cleavage site from RNA-seq. [Thesis]. New Jersey Institute of Technology; 2013. Available from: https://digitalcommons.njit.edu/theses/169

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

16. Jadhav, Bharati. Risk prediction with genomic data.

Degree: MSin Bioinformatics - (M.S.), Computer Science, 2014, New Jersey Institute of Technology

  Genome wide association study (GWAS) is widely used with various machine learning algorithms to predict disease risk. This thesis investigates this widely used approach… (more)

Subjects/Keywords: Genome wide association study (GWAS); Disease risk prediction; Single Nucleotide Polymorphism (SNP) genotype data; Whole Exome Wide Association Study (WEWAS); Bioinformatics; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Jadhav, B. (2014). Risk prediction with genomic data. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/199

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Jadhav, Bharati. “Risk prediction with genomic data.” 2014. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/199.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Jadhav, Bharati. “Risk prediction with genomic data.” 2014. Web. 22 Oct 2019.

Vancouver:

Jadhav B. Risk prediction with genomic data. [Internet] [Thesis]. New Jersey Institute of Technology; 2014. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/199.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Jadhav B. Risk prediction with genomic data. [Thesis]. New Jersey Institute of Technology; 2014. Available from: https://digitalcommons.njit.edu/theses/199

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

17. Aljouie, Abdulrhman Fahad M. Rice and mouse quantitative phenotype prediction in genome-wide association studies with support vector regression.

Degree: MSin Bioinformatics - (M.S.), Computer Science, 2014, New Jersey Institute of Technology

  Quantitative phenotypes prediction from genotype data is significant for pathogenesis, crop yields, and immunity tests. The scientific community conducted many studies to find unobserved… (more)

Subjects/Keywords: Quantitative phenotypes prediction; Single nucleotide polymorphisms; Support vector regression; Bioinformatics; Computer Sciences

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APA (6th Edition):

Aljouie, A. F. M. (2014). Rice and mouse quantitative phenotype prediction in genome-wide association studies with support vector regression. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/212

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Aljouie, Abdulrhman Fahad M. “Rice and mouse quantitative phenotype prediction in genome-wide association studies with support vector regression.” 2014. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/212.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Aljouie, Abdulrhman Fahad M. “Rice and mouse quantitative phenotype prediction in genome-wide association studies with support vector regression.” 2014. Web. 22 Oct 2019.

Vancouver:

Aljouie AFM. Rice and mouse quantitative phenotype prediction in genome-wide association studies with support vector regression. [Internet] [Thesis]. New Jersey Institute of Technology; 2014. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/212.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Aljouie AFM. Rice and mouse quantitative phenotype prediction in genome-wide association studies with support vector regression. [Thesis]. New Jersey Institute of Technology; 2014. Available from: https://digitalcommons.njit.edu/theses/212

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

18. Patel, Nihir. Cancer risk prediction with next generation sequencing data using machine learning.

Degree: MSin Bioinformatics - (M.S.), Computer Science, 2014, New Jersey Institute of Technology

  The use of computational biology for next generation sequencing (NGS) analysis is rapidly increasing in genomics research. However, the effectiveness of NGS data to… (more)

Subjects/Keywords: Cancer risk prediction; Next generation sequencing; Machine learning; Bioinformatics; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Patel, N. (2014). Cancer risk prediction with next generation sequencing data using machine learning. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/220

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Patel, Nihir. “Cancer risk prediction with next generation sequencing data using machine learning.” 2014. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/220.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Patel, Nihir. “Cancer risk prediction with next generation sequencing data using machine learning.” 2014. Web. 22 Oct 2019.

Vancouver:

Patel N. Cancer risk prediction with next generation sequencing data using machine learning. [Internet] [Thesis]. New Jersey Institute of Technology; 2014. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/220.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Patel N. Cancer risk prediction with next generation sequencing data using machine learning. [Thesis]. New Jersey Institute of Technology; 2014. Available from: https://digitalcommons.njit.edu/theses/220

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

19. Ghosh, Nandini. Exact genome alignment.

Degree: MSin Bioinformatics - (M.S.), Computer Science, 2015, New Jersey Institute of Technology

  The increase in the volume of genomic data due to the decrease in the cost of whole genome sequencing techniques has opened up new(more)

Subjects/Keywords: Sequence alignment; Whole genome sequences; Bioinformatics; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ghosh, N. (2015). Exact genome alignment. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/232

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ghosh, Nandini. “Exact genome alignment.” 2015. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/232.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ghosh, Nandini. “Exact genome alignment.” 2015. Web. 22 Oct 2019.

Vancouver:

Ghosh N. Exact genome alignment. [Internet] [Thesis]. New Jersey Institute of Technology; 2015. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/232.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ghosh N. Exact genome alignment. [Thesis]. New Jersey Institute of Technology; 2015. Available from: https://digitalcommons.njit.edu/theses/232

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

20. Radia, Nidhi. Unsupervised gene regulatory network inference on microarray data.

Degree: MSin Bioinformatics - (M.S.), Computer Science, 2015, New Jersey Institute of Technology

  Obtaining gene regulatory networks (GRNs) from expression data is a challenging and crucial task. Many computational methods and algorithms have been developed to infer… (more)

Subjects/Keywords: Gene regulatory networks; Microarray data; Bioinformatics; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Radia, N. (2015). Unsupervised gene regulatory network inference on microarray data. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/242

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Radia, Nidhi. “Unsupervised gene regulatory network inference on microarray data.” 2015. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/242.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Radia, Nidhi. “Unsupervised gene regulatory network inference on microarray data.” 2015. Web. 22 Oct 2019.

Vancouver:

Radia N. Unsupervised gene regulatory network inference on microarray data. [Internet] [Thesis]. New Jersey Institute of Technology; 2015. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/242.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Radia N. Unsupervised gene regulatory network inference on microarray data. [Thesis]. New Jersey Institute of Technology; 2015. Available from: https://digitalcommons.njit.edu/theses/242

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

21. Du, Zongxuan. Data analytics with mapreduce in apache spark and hadoop systems.

Degree: MSin Computer Science - (M.S.), Computer Science, 2016, New Jersey Institute of Technology

  MapReduce comes from a traditional problem solving method: separating a big problem and solving each small parts. With the target of computing larger dataset… (more)

Subjects/Keywords: Data analytics; Mapreduce; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Du, Z. (2016). Data analytics with mapreduce in apache spark and hadoop systems. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/269

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Du, Zongxuan. “Data analytics with mapreduce in apache spark and hadoop systems.” 2016. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/269.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Du, Zongxuan. “Data analytics with mapreduce in apache spark and hadoop systems.” 2016. Web. 22 Oct 2019.

Vancouver:

Du Z. Data analytics with mapreduce in apache spark and hadoop systems. [Internet] [Thesis]. New Jersey Institute of Technology; 2016. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/269.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Du Z. Data analytics with mapreduce in apache spark and hadoop systems. [Thesis]. New Jersey Institute of Technology; 2016. Available from: https://digitalcommons.njit.edu/theses/269

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

22. Somoza, Maria E. Gene network understanding and analysis.

Degree: MSin Bioinformatics - (M.S.), Computer Science, 2016, New Jersey Institute of Technology

  Gene regulatory network (GRN) is a collection of regulators that interact with each other in the cell to govern the gene expression levels of… (more)

Subjects/Keywords: Gene regulatory network; Transcriptional gene regulation; Gene co-expression networks; Correlation networks; Bioinformatics; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Somoza, M. E. (2016). Gene network understanding and analysis. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/279

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Somoza, Maria E. “Gene network understanding and analysis.” 2016. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/279.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Somoza, Maria E. “Gene network understanding and analysis.” 2016. Web. 22 Oct 2019.

Vancouver:

Somoza ME. Gene network understanding and analysis. [Internet] [Thesis]. New Jersey Institute of Technology; 2016. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/279.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Somoza ME. Gene network understanding and analysis. [Thesis]. New Jersey Institute of Technology; 2016. Available from: https://digitalcommons.njit.edu/theses/279

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

23. Zha, Shijie. Uusing the KDJ as a trading strategy on biotech companies.

Degree: MSin Bioinformatics - (M.S.), Computer Science, 2016, New Jersey Institute of Technology

  Mean Reversion is the most commonly used model in quantitative trading. This model is associated with several factors, like ma5 and ma10 line. These… (more)

Subjects/Keywords: Trading strategies; Quantitative trading; Biotech companies; Bioinformatics; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zha, S. (2016). Uusing the KDJ as a trading strategy on biotech companies. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/283

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Zha, Shijie. “Uusing the KDJ as a trading strategy on biotech companies.” 2016. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/283.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zha, Shijie. “Uusing the KDJ as a trading strategy on biotech companies.” 2016. Web. 22 Oct 2019.

Vancouver:

Zha S. Uusing the KDJ as a trading strategy on biotech companies. [Internet] [Thesis]. New Jersey Institute of Technology; 2016. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/283.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zha S. Uusing the KDJ as a trading strategy on biotech companies. [Thesis]. New Jersey Institute of Technology; 2016. Available from: https://digitalcommons.njit.edu/theses/283

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

24. Gorski, Kathryn A. Hypoxic and viral contributions to the etiopathogenesis of schizophrenia: a whole transcriptome analysis.

Degree: MSin Bioinformatics - (M.S.), Computer Science, 2018, New Jersey Institute of Technology

  Schizophrenia is a mental illness with a complex and as of yet unclear etiology. It is highly heritable and has a strong polygenic character,… (more)

Subjects/Keywords: Schizophrenia; Schizophrenia pathoetiology; Bioinformatics; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Gorski, K. A. (2018). Hypoxic and viral contributions to the etiopathogenesis of schizophrenia: a whole transcriptome analysis. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/1572

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Gorski, Kathryn A. “Hypoxic and viral contributions to the etiopathogenesis of schizophrenia: a whole transcriptome analysis.” 2018. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/1572.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Gorski, Kathryn A. “Hypoxic and viral contributions to the etiopathogenesis of schizophrenia: a whole transcriptome analysis.” 2018. Web. 22 Oct 2019.

Vancouver:

Gorski KA. Hypoxic and viral contributions to the etiopathogenesis of schizophrenia: a whole transcriptome analysis. [Internet] [Thesis]. New Jersey Institute of Technology; 2018. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/1572.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gorski KA. Hypoxic and viral contributions to the etiopathogenesis of schizophrenia: a whole transcriptome analysis. [Thesis]. New Jersey Institute of Technology; 2018. Available from: https://digitalcommons.njit.edu/theses/1572

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

25. Griesmer, Stephen J. In silico prediction of non-coding RNAs using supervised learning and feature ranking methods.

Degree: MSin Bioinformatics - (M.S.), Computer Science, 2009, New Jersey Institute of Technology

  This thesis presents a novel method, RNAMultifold, for development of a non-coding RNA (ncRNA) classification model based on features derived from folding the consensus… (more)

Subjects/Keywords: Non-coding RNA classification; RNAMultifold; Bioinformatics; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Griesmer, S. J. (2009). In silico prediction of non-coding RNAs using supervised learning and feature ranking methods. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/47

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Griesmer, Stephen J. “In silico prediction of non-coding RNAs using supervised learning and feature ranking methods.” 2009. Thesis, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/theses/47.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Griesmer, Stephen J. “In silico prediction of non-coding RNAs using supervised learning and feature ranking methods.” 2009. Web. 22 Oct 2019.

Vancouver:

Griesmer SJ. In silico prediction of non-coding RNAs using supervised learning and feature ranking methods. [Internet] [Thesis]. New Jersey Institute of Technology; 2009. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/theses/47.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Griesmer SJ. In silico prediction of non-coding RNAs using supervised learning and feature ranking methods. [Thesis]. New Jersey Institute of Technology; 2009. Available from: https://digitalcommons.njit.edu/theses/47

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

26. Ma, Qicheng. Knowledge discovery in biological databases : a neural network approach.

Degree: PhD, Computer and Information Science, 2000, New Jersey Institute of Technology

  Knowledge discovery, in databases, also known as data mining, is aimed to find significant information from a set of data. The knowledge to be… (more)

Subjects/Keywords: data mining; neural network approach; biological databases; Computer Sciences; Databases and Information Systems; Management Information Systems

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ma, Q. (2000). Knowledge discovery in biological databases : a neural network approach. (Doctoral Dissertation). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/dissertations/423

Chicago Manual of Style (16th Edition):

Ma, Qicheng. “Knowledge discovery in biological databases : a neural network approach.” 2000. Doctoral Dissertation, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/dissertations/423.

MLA Handbook (7th Edition):

Ma, Qicheng. “Knowledge discovery in biological databases : a neural network approach.” 2000. Web. 22 Oct 2019.

Vancouver:

Ma Q. Knowledge discovery in biological databases : a neural network approach. [Internet] [Doctoral dissertation]. New Jersey Institute of Technology; 2000. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/dissertations/423.

Council of Science Editors:

Ma Q. Knowledge discovery in biological databases : a neural network approach. [Doctoral Dissertation]. New Jersey Institute of Technology; 2000. Available from: https://digitalcommons.njit.edu/dissertations/423


New Jersey Institute of Technology

27. Wang, Xiong. Information retrieval and mining in high dimensional databases.

Degree: PhD, Computer and Information Science, 2000, New Jersey Institute of Technology

  This dissertation is composed of two parts. In the first part, we present a framework for finding information (more precisely, active patterns) in three… (more)

Subjects/Keywords: Image processing  – Digital techniques.; Computer vision.; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wang, X. (2000). Information retrieval and mining in high dimensional databases. (Doctoral Dissertation). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/dissertations/438

Chicago Manual of Style (16th Edition):

Wang, Xiong. “Information retrieval and mining in high dimensional databases.” 2000. Doctoral Dissertation, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/dissertations/438.

MLA Handbook (7th Edition):

Wang, Xiong. “Information retrieval and mining in high dimensional databases.” 2000. Web. 22 Oct 2019.

Vancouver:

Wang X. Information retrieval and mining in high dimensional databases. [Internet] [Doctoral dissertation]. New Jersey Institute of Technology; 2000. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/dissertations/438.

Council of Science Editors:

Wang X. Information retrieval and mining in high dimensional databases. [Doctoral Dissertation]. New Jersey Institute of Technology; 2000. Available from: https://digitalcommons.njit.edu/dissertations/438


New Jersey Institute of Technology

28. Chang, George Jyh-Shian. WAQS : a web-based approximate query system.

Degree: PhD, Computer and Information Science, 2001, New Jersey Institute of Technology

  The Web is often viewed as a gigantic database holding vast stores of information and provides ubiquitous accessibility to end-users. Since its inception, the… (more)

Subjects/Keywords: Approximate Location Adjacency; Query System; Web; EnviroDaemon; Computer Sciences; Databases and Information Systems; Management Information Systems

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Chang, G. J. (2001). WAQS : a web-based approximate query system. (Doctoral Dissertation). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/dissertations/467

Chicago Manual of Style (16th Edition):

Chang, George Jyh-Shian. “WAQS : a web-based approximate query system.” 2001. Doctoral Dissertation, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/dissertations/467.

MLA Handbook (7th Edition):

Chang, George Jyh-Shian. “WAQS : a web-based approximate query system.” 2001. Web. 22 Oct 2019.

Vancouver:

Chang GJ. WAQS : a web-based approximate query system. [Internet] [Doctoral dissertation]. New Jersey Institute of Technology; 2001. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/dissertations/467.

Council of Science Editors:

Chang GJ. WAQS : a web-based approximate query system. [Doctoral Dissertation]. New Jersey Institute of Technology; 2001. Available from: https://digitalcommons.njit.edu/dissertations/467


New Jersey Institute of Technology

29. Yin, Michael M. Knowledge discovery and modeling in genomic databases.

Degree: PhD, Computer and Information Science, 2002, New Jersey Institute of Technology

  This dissertation research is targeted toward developing effective and accurate methods for identifying gene structures in the genomes of high eukaryotes, such as vertebrate… (more)

Subjects/Keywords: Gene detection; Hidden Markov models; Bioinformatics; Splicing junction; Computational biology; Computer Sciences; Databases and Information Systems; Management Information Systems

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yin, M. M. (2002). Knowledge discovery and modeling in genomic databases. (Doctoral Dissertation). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/dissertations/551

Chicago Manual of Style (16th Edition):

Yin, Michael M. “Knowledge discovery and modeling in genomic databases.” 2002. Doctoral Dissertation, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/dissertations/551.

MLA Handbook (7th Edition):

Yin, Michael M. “Knowledge discovery and modeling in genomic databases.” 2002. Web. 22 Oct 2019.

Vancouver:

Yin MM. Knowledge discovery and modeling in genomic databases. [Internet] [Doctoral dissertation]. New Jersey Institute of Technology; 2002. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/dissertations/551.

Council of Science Editors:

Yin MM. Knowledge discovery and modeling in genomic databases. [Doctoral Dissertation]. New Jersey Institute of Technology; 2002. Available from: https://digitalcommons.njit.edu/dissertations/551


New Jersey Institute of Technology

30. Herbert, Katherine Grace. New techniques for improving biological data quality through information integration.

Degree: PhD, Computer Science, 2004, New Jersey Institute of Technology

  As databases become more pervasive through the biological sciences, various data quality concerns are emerging. Biological databases tend to develop data quality issues regarding… (more)

Subjects/Keywords: Data integration; Data cleaning; Bioinformatics; Data mining; Phylogeny; Databases; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Herbert, K. G. (2004). New techniques for improving biological data quality through information integration. (Doctoral Dissertation). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/dissertations/631

Chicago Manual of Style (16th Edition):

Herbert, Katherine Grace. “New techniques for improving biological data quality through information integration.” 2004. Doctoral Dissertation, New Jersey Institute of Technology. Accessed October 22, 2019. https://digitalcommons.njit.edu/dissertations/631.

MLA Handbook (7th Edition):

Herbert, Katherine Grace. “New techniques for improving biological data quality through information integration.” 2004. Web. 22 Oct 2019.

Vancouver:

Herbert KG. New techniques for improving biological data quality through information integration. [Internet] [Doctoral dissertation]. New Jersey Institute of Technology; 2004. [cited 2019 Oct 22]. Available from: https://digitalcommons.njit.edu/dissertations/631.

Council of Science Editors:

Herbert KG. New techniques for improving biological data quality through information integration. [Doctoral Dissertation]. New Jersey Institute of Technology; 2004. Available from: https://digitalcommons.njit.edu/dissertations/631

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