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You searched for +publisher:"Virginia Tech" +contributor:("Heath, Lenwood S."). Showing records 1 – 30 of 83 total matches.

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1. Nayyar, Krati. Input Sensitive Analysis of a Minimum Metric Bipartite Matching Algorithm.

Degree: MS, Computer Science and Applications, 2017, Virginia Tech

 In various business and military settings, there is an expectation of on-demand delivery of supplies and services. Typically, several delivery vehicles (also called servers) carry… (more)

Subjects/Keywords: online algorithms; weighted matching; competitive ratio; input sensitive

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APA (6th Edition):

Nayyar, K. (2017). Input Sensitive Analysis of a Minimum Metric Bipartite Matching Algorithm. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/86518

Chicago Manual of Style (16th Edition):

Nayyar, Krati. “Input Sensitive Analysis of a Minimum Metric Bipartite Matching Algorithm.” 2017. Masters Thesis, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/86518.

MLA Handbook (7th Edition):

Nayyar, Krati. “Input Sensitive Analysis of a Minimum Metric Bipartite Matching Algorithm.” 2017. Web. 28 Feb 2021.

Vancouver:

Nayyar K. Input Sensitive Analysis of a Minimum Metric Bipartite Matching Algorithm. [Internet] [Masters thesis]. Virginia Tech; 2017. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/86518.

Council of Science Editors:

Nayyar K. Input Sensitive Analysis of a Minimum Metric Bipartite Matching Algorithm. [Masters Thesis]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/86518

2. Robertson, Jeffrey Alan. Entropy Measurements and Ball Cover Construction for Biological Sequences.

Degree: MS, Computer Science and Applications, 2018, Virginia Tech

 As improving technology is making it easier to select or engineer DNA sequences that produce dangerous proteins, it is important to be able to predict… (more)

Subjects/Keywords: Bioinformatics; Entropy Scaling; Sequence Search; BLAST

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APA (6th Edition):

Robertson, J. A. (2018). Entropy Measurements and Ball Cover Construction for Biological Sequences. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/84470

Chicago Manual of Style (16th Edition):

Robertson, Jeffrey Alan. “Entropy Measurements and Ball Cover Construction for Biological Sequences.” 2018. Masters Thesis, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/84470.

MLA Handbook (7th Edition):

Robertson, Jeffrey Alan. “Entropy Measurements and Ball Cover Construction for Biological Sequences.” 2018. Web. 28 Feb 2021.

Vancouver:

Robertson JA. Entropy Measurements and Ball Cover Construction for Biological Sequences. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/84470.

Council of Science Editors:

Robertson JA. Entropy Measurements and Ball Cover Construction for Biological Sequences. [Masters Thesis]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/84470

3. Ni, Ying. A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis Using Time Series Gene Expression Data.

Degree: MS, Computer Science and Applications, 2016, Virginia Tech

 Gene regulatory networks (GRNs) provide a natural representation of relationships between regulators and target genes. Though inferring GRN is a challenging task, many methods, including… (more)

Subjects/Keywords: Network inference; signal transduction pathways; gene expression; support vector machines

…professors at Virginia Tech. Further details of the experiments can be found in [67]. In… 

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APA (6th Edition):

Ni, Y. (2016). A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis Using Time Series Gene Expression Data. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/81463

Chicago Manual of Style (16th Edition):

Ni, Ying. “A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis Using Time Series Gene Expression Data.” 2016. Masters Thesis, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/81463.

MLA Handbook (7th Edition):

Ni, Ying. “A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis Using Time Series Gene Expression Data.” 2016. Web. 28 Feb 2021.

Vancouver:

Ni Y. A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis Using Time Series Gene Expression Data. [Internet] [Masters thesis]. Virginia Tech; 2016. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/81463.

Council of Science Editors:

Ni Y. A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis Using Time Series Gene Expression Data. [Masters Thesis]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/81463

4. Wagner, Mitchell James. Reconstructing Signaling Pathways Using Regular-Language Constrained Paths.

Degree: MS, Computer Science and Applications, 2018, Virginia Tech

 Signaling pathways are widely studied in systems biology. Several databases catalog our knowledge of these pathways, including the proteins and interactions that comprise them. However,… (more)

Subjects/Keywords: Regular Languages; Shortest Paths; Signaling Networks

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7

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APA (6th Edition):

Wagner, M. J. (2018). Reconstructing Signaling Pathways Using Regular-Language Constrained Paths. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/85044

Chicago Manual of Style (16th Edition):

Wagner, Mitchell James. “Reconstructing Signaling Pathways Using Regular-Language Constrained Paths.” 2018. Masters Thesis, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/85044.

MLA Handbook (7th Edition):

Wagner, Mitchell James. “Reconstructing Signaling Pathways Using Regular-Language Constrained Paths.” 2018. Web. 28 Feb 2021.

Vancouver:

Wagner MJ. Reconstructing Signaling Pathways Using Regular-Language Constrained Paths. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/85044.

Council of Science Editors:

Wagner MJ. Reconstructing Signaling Pathways Using Regular-Language Constrained Paths. [Masters Thesis]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/85044


Virginia Tech

5. Paul, Ann Molly. QBank: A Web-Based Dynamic Problem Authoring Tool.

Degree: MS, Computer Science and Applications, 2013, Virginia Tech

 Widespread accessibility to the Internet and the proliferation of Web 2.0 technologies has led to the growth of online tools for educational content creation, delivery,… (more)

Subjects/Keywords: Formal Problem Definition; Problem Authoring Tool; Question Banking; Computer Education; Parameterized Questions

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APA (6th Edition):

Paul, A. M. (2013). QBank: A Web-Based Dynamic Problem Authoring Tool. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/23889

Chicago Manual of Style (16th Edition):

Paul, Ann Molly. “QBank: A Web-Based Dynamic Problem Authoring Tool.” 2013. Masters Thesis, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/23889.

MLA Handbook (7th Edition):

Paul, Ann Molly. “QBank: A Web-Based Dynamic Problem Authoring Tool.” 2013. Web. 28 Feb 2021.

Vancouver:

Paul AM. QBank: A Web-Based Dynamic Problem Authoring Tool. [Internet] [Masters thesis]. Virginia Tech; 2013. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/23889.

Council of Science Editors:

Paul AM. QBank: A Web-Based Dynamic Problem Authoring Tool. [Masters Thesis]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/23889


Virginia Tech

6. Aggarwal, Deepti. Inferring Signal Transduction Pathways from Gene Expression Data using Prior Knowledge.

Degree: MS, Electrical Engineering, 2015, Virginia Tech

 Plants have developed specific responses to external stimuli such as drought, cold, high salinity in soil, and precipitation in addition to internal developmental stimuli. These… (more)

Subjects/Keywords: Signal Transduction Pathways; Gene Expression; Inference Engine

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APA (6th Edition):

Aggarwal, D. (2015). Inferring Signal Transduction Pathways from Gene Expression Data using Prior Knowledge. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/56601

Chicago Manual of Style (16th Edition):

Aggarwal, Deepti. “Inferring Signal Transduction Pathways from Gene Expression Data using Prior Knowledge.” 2015. Masters Thesis, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/56601.

MLA Handbook (7th Edition):

Aggarwal, Deepti. “Inferring Signal Transduction Pathways from Gene Expression Data using Prior Knowledge.” 2015. Web. 28 Feb 2021.

Vancouver:

Aggarwal D. Inferring Signal Transduction Pathways from Gene Expression Data using Prior Knowledge. [Internet] [Masters thesis]. Virginia Tech; 2015. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/56601.

Council of Science Editors:

Aggarwal D. Inferring Signal Transduction Pathways from Gene Expression Data using Prior Knowledge. [Masters Thesis]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/56601


Virginia Tech

7. Ye, Jiacheng. Computing Exact Bottleneck Distance on Random Point Sets.

Degree: MS, Computer Science and Applications, 2020, Virginia Tech

 Consider the problem of matching taxis to an equal number of requests. While matching them, one objective is to minimize the largest distance between a… (more)

Subjects/Keywords: bipartite graph; bottleneck matching

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APA (6th Edition):

Ye, J. (2020). Computing Exact Bottleneck Distance on Random Point Sets. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/98669

Chicago Manual of Style (16th Edition):

Ye, Jiacheng. “Computing Exact Bottleneck Distance on Random Point Sets.” 2020. Masters Thesis, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/98669.

MLA Handbook (7th Edition):

Ye, Jiacheng. “Computing Exact Bottleneck Distance on Random Point Sets.” 2020. Web. 28 Feb 2021.

Vancouver:

Ye J. Computing Exact Bottleneck Distance on Random Point Sets. [Internet] [Masters thesis]. Virginia Tech; 2020. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/98669.

Council of Science Editors:

Ye J. Computing Exact Bottleneck Distance on Random Point Sets. [Masters Thesis]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/98669


Virginia Tech

8. Maji, Nabanita. An Interactive Tutorial for NP-Completeness.

Degree: MS, Computer Science and Applications, 2015, Virginia Tech

 A Theory of Algorithms course is essential to any Computer Science curriculum at both the undergraduate and graduate levels. It is also considered to be… (more)

Subjects/Keywords: NP Completeness; Complexity Theory; Reductions; Algorithm Visualization; Computer Science Education; Automated Assessment

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APA (6th Edition):

Maji, N. (2015). An Interactive Tutorial for NP-Completeness. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/52973

Chicago Manual of Style (16th Edition):

Maji, Nabanita. “An Interactive Tutorial for NP-Completeness.” 2015. Masters Thesis, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/52973.

MLA Handbook (7th Edition):

Maji, Nabanita. “An Interactive Tutorial for NP-Completeness.” 2015. Web. 28 Feb 2021.

Vancouver:

Maji N. An Interactive Tutorial for NP-Completeness. [Internet] [Masters thesis]. Virginia Tech; 2015. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/52973.

Council of Science Editors:

Maji N. An Interactive Tutorial for NP-Completeness. [Masters Thesis]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/52973


Virginia Tech

9. Parikh, Nidhi Kiranbhai. Generating Random Graphs with Tunable Clustering Coefficient.

Degree: MS, Computer Science, 2011, Virginia Tech

 Most real-world networks exhibit a high clustering coefficientâ the probability that two neighbors of a node are also neighbors of each other. We propose four… (more)

Subjects/Keywords: Clustering coefficient; complex networks; random graphs; algorithms

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APA (6th Edition):

Parikh, N. K. (2011). Generating Random Graphs with Tunable Clustering Coefficient. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/31591

Chicago Manual of Style (16th Edition):

Parikh, Nidhi Kiranbhai. “Generating Random Graphs with Tunable Clustering Coefficient.” 2011. Masters Thesis, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/31591.

MLA Handbook (7th Edition):

Parikh, Nidhi Kiranbhai. “Generating Random Graphs with Tunable Clustering Coefficient.” 2011. Web. 28 Feb 2021.

Vancouver:

Parikh NK. Generating Random Graphs with Tunable Clustering Coefficient. [Internet] [Masters thesis]. Virginia Tech; 2011. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/31591.

Council of Science Editors:

Parikh NK. Generating Random Graphs with Tunable Clustering Coefficient. [Masters Thesis]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/31591


Virginia Tech

10. Yang, Yanshen. MCAT: Motif Combining and Association Tool.

Degree: MS, Computer Science and Applications, 2018, Virginia Tech

 De novo motif discovery in biological sequences is an important and computationally challenging problem. A myriad of algorithms have been developed to solve this problem… (more)

Subjects/Keywords: Motif finding

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APA (6th Edition):

Yang, Y. (2018). MCAT: Motif Combining and Association Tool. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/84999

Chicago Manual of Style (16th Edition):

Yang, Yanshen. “MCAT: Motif Combining and Association Tool.” 2018. Masters Thesis, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/84999.

MLA Handbook (7th Edition):

Yang, Yanshen. “MCAT: Motif Combining and Association Tool.” 2018. Web. 28 Feb 2021.

Vancouver:

Yang Y. MCAT: Motif Combining and Association Tool. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/84999.

Council of Science Editors:

Yang Y. MCAT: Motif Combining and Association Tool. [Masters Thesis]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/84999


Virginia Tech

11. Senthil, Rathna. IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks.

Degree: MS, Computer Science and Applications, 2016, Virginia Tech

 Complex systems in areas such as biology, physics, social science, and technology are extensively modeled as networks due to the rich set of tools available… (more)

Subjects/Keywords: Overlapping Community Detection; Complex Networks; Local Expansion

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APA (6th Edition):

Senthil, R. (2016). IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/65160

Chicago Manual of Style (16th Edition):

Senthil, Rathna. “IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks.” 2016. Masters Thesis, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/65160.

MLA Handbook (7th Edition):

Senthil, Rathna. “IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks.” 2016. Web. 28 Feb 2021.

Vancouver:

Senthil R. IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks. [Internet] [Masters thesis]. Virginia Tech; 2016. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/65160.

Council of Science Editors:

Senthil R. IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks. [Masters Thesis]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/65160

12. Vijayan, Vinaya. Understanding and Improving Identification of Somatic Variants.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2016, Virginia Tech

 It is important to understand the entire spectrum of somatic variants to gain more insight into mutations that occur in different cancers for development of… (more)

Subjects/Keywords: Somatic variants; Somatic variant callers; Somatic point mutations; Short tandem repeat variation; Lung squamous cell carcinoma

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APA (6th Edition):

Vijayan, V. (2016). Understanding and Improving Identification of Somatic Variants. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/72969

Chicago Manual of Style (16th Edition):

Vijayan, Vinaya. “Understanding and Improving Identification of Somatic Variants.” 2016. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/72969.

MLA Handbook (7th Edition):

Vijayan, Vinaya. “Understanding and Improving Identification of Somatic Variants.” 2016. Web. 28 Feb 2021.

Vancouver:

Vijayan V. Understanding and Improving Identification of Somatic Variants. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/72969.

Council of Science Editors:

Vijayan V. Understanding and Improving Identification of Somatic Variants. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/72969

13. Hasan, Mohammad Shabbir. Identifying and Analyzing Indel Variants in the Human Genome Using Computational Approaches.

Degree: PhD, Computer Science and Applications, 2019, Virginia Tech

 Insertion and deletion (indel), a common form of genetic variation in the human genome, is associated with genetic diseases and cancer. However, indels are heavily… (more)

Subjects/Keywords: Genetic Variants; Indel; Somatic Mutation; Next Generation Sequencing

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APA (6th Edition):

Hasan, M. S. (2019). Identifying and Analyzing Indel Variants in the Human Genome Using Computational Approaches. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/90797

Chicago Manual of Style (16th Edition):

Hasan, Mohammad Shabbir. “Identifying and Analyzing Indel Variants in the Human Genome Using Computational Approaches.” 2019. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/90797.

MLA Handbook (7th Edition):

Hasan, Mohammad Shabbir. “Identifying and Analyzing Indel Variants in the Human Genome Using Computational Approaches.” 2019. Web. 28 Feb 2021.

Vancouver:

Hasan MS. Identifying and Analyzing Indel Variants in the Human Genome Using Computational Approaches. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/90797.

Council of Science Editors:

Hasan MS. Identifying and Analyzing Indel Variants in the Human Genome Using Computational Approaches. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/90797


Virginia Tech

14. Torkey, Hanaa A. Machine Learning Approaches for Identifying microRNA Targets and Conserved Protein Complexes.

Degree: PhD, Computer Science and Applications, 2017, Virginia Tech

 Much research has been directed toward understanding the roles of essential components in the cell, such as proteins, microRNAs, and genes. This dissertation focuses on… (more)

Subjects/Keywords: microRNA target; machine learning; network alignment; protein complex.

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APA (6th Edition):

Torkey, H. A. (2017). Machine Learning Approaches for Identifying microRNA Targets and Conserved Protein Complexes. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/77536

Chicago Manual of Style (16th Edition):

Torkey, Hanaa A. “Machine Learning Approaches for Identifying microRNA Targets and Conserved Protein Complexes.” 2017. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/77536.

MLA Handbook (7th Edition):

Torkey, Hanaa A. “Machine Learning Approaches for Identifying microRNA Targets and Conserved Protein Complexes.” 2017. Web. 28 Feb 2021.

Vancouver:

Torkey HA. Machine Learning Approaches for Identifying microRNA Targets and Conserved Protein Complexes. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/77536.

Council of Science Editors:

Torkey HA. Machine Learning Approaches for Identifying microRNA Targets and Conserved Protein Complexes. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/77536


Virginia Tech

15. Lahn, Nathaniel Adam. A Separator-Based Framework for Graph Matching Problems.

Degree: PhD, Computer Science and Applications, 2020, Virginia Tech

 Assume we are given a list of objects, and a list of compatible pairs of these objects. A matching consists of a chosen subset of… (more)

Subjects/Keywords: Matching; graphs; graph separators; combinatorial optimization

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APA (6th Edition):

Lahn, N. A. (2020). A Separator-Based Framework for Graph Matching Problems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/98618

Chicago Manual of Style (16th Edition):

Lahn, Nathaniel Adam. “A Separator-Based Framework for Graph Matching Problems.” 2020. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/98618.

MLA Handbook (7th Edition):

Lahn, Nathaniel Adam. “A Separator-Based Framework for Graph Matching Problems.” 2020. Web. 28 Feb 2021.

Vancouver:

Lahn NA. A Separator-Based Framework for Graph Matching Problems. [Internet] [Doctoral dissertation]. Virginia Tech; 2020. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/98618.

Council of Science Editors:

Lahn NA. A Separator-Based Framework for Graph Matching Problems. [Doctoral Dissertation]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/98618


Virginia Tech

16. Altarawy, Doaa Abdelsalam Ahmed Mohamed. DeTangle: A Framework for Interactive Prediction and Visualization of Gene Regulatory Networks.

Degree: PhD, Computer Science and Applications, 2017, Virginia Tech

 With the abundance of biological data, computational prediction of gene regulatory networks (GRNs) from gene expression data has become more feasible. Although incorporating other prior… (more)

Subjects/Keywords: Gene regulation; prior knowledge; gene regulatory network inference; visualization; machine learning

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APA (6th Edition):

Altarawy, D. A. A. M. (2017). DeTangle: A Framework for Interactive Prediction and Visualization of Gene Regulatory Networks. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/85504

Chicago Manual of Style (16th Edition):

Altarawy, Doaa Abdelsalam Ahmed Mohamed. “DeTangle: A Framework for Interactive Prediction and Visualization of Gene Regulatory Networks.” 2017. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/85504.

MLA Handbook (7th Edition):

Altarawy, Doaa Abdelsalam Ahmed Mohamed. “DeTangle: A Framework for Interactive Prediction and Visualization of Gene Regulatory Networks.” 2017. Web. 28 Feb 2021.

Vancouver:

Altarawy DAAM. DeTangle: A Framework for Interactive Prediction and Visualization of Gene Regulatory Networks. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/85504.

Council of Science Editors:

Altarawy DAAM. DeTangle: A Framework for Interactive Prediction and Visualization of Gene Regulatory Networks. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/85504


Virginia Tech

17. Kakumanu, Akshay. Effects of Drought on Gene Expression in Maize Reproductive and Leaf Meristem Tissues as Revealed by Deep Sequencing.

Degree: MSin Life Sciences, Plant Pathology, Physiology, and Weed Science, 2012, Virginia Tech

 Drought is a major environmental stress factor that poses a serious threat to food security. The effects of drought on early reproductive tissue at 1-2… (more)

Subjects/Keywords: Drought; Illumina; RNA-Ser; Maize; Ovaries; Leaf Meristem

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APA (6th Edition):

Kakumanu, A. (2012). Effects of Drought on Gene Expression in Maize Reproductive and Leaf Meristem Tissues as Revealed by Deep Sequencing. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/33907

Chicago Manual of Style (16th Edition):

Kakumanu, Akshay. “Effects of Drought on Gene Expression in Maize Reproductive and Leaf Meristem Tissues as Revealed by Deep Sequencing.” 2012. Masters Thesis, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/33907.

MLA Handbook (7th Edition):

Kakumanu, Akshay. “Effects of Drought on Gene Expression in Maize Reproductive and Leaf Meristem Tissues as Revealed by Deep Sequencing.” 2012. Web. 28 Feb 2021.

Vancouver:

Kakumanu A. Effects of Drought on Gene Expression in Maize Reproductive and Leaf Meristem Tissues as Revealed by Deep Sequencing. [Internet] [Masters thesis]. Virginia Tech; 2012. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/33907.

Council of Science Editors:

Kakumanu A. Effects of Drought on Gene Expression in Maize Reproductive and Leaf Meristem Tissues as Revealed by Deep Sequencing. [Masters Thesis]. Virginia Tech; 2012. Available from: http://hdl.handle.net/10919/33907


Virginia Tech

18. Belal, Nahla Ahmed. Two Problems in Computational Genomics.

Degree: PhD, Computer Science, 2011, Virginia Tech

 This work addresses two novel problems in the field of computational genomics. The first is whole genome alignment and the second is inferring horizontal gene… (more)

Subjects/Keywords: horizontal gene transfer; Two Problems in Computational Genomics; whole genome alignment; dynamic programming; Graph theory; biology and genetics; graph algorithms; partial order sets

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APA (6th Edition):

Belal, N. A. (2011). Two Problems in Computational Genomics. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/26318

Chicago Manual of Style (16th Edition):

Belal, Nahla Ahmed. “Two Problems in Computational Genomics.” 2011. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/26318.

MLA Handbook (7th Edition):

Belal, Nahla Ahmed. “Two Problems in Computational Genomics.” 2011. Web. 28 Feb 2021.

Vancouver:

Belal NA. Two Problems in Computational Genomics. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/26318.

Council of Science Editors:

Belal NA. Two Problems in Computational Genomics. [Doctoral Dissertation]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/26318


Virginia Tech

19. Modise, Thero. Genomic Instability and Gene Dosage Obscures Clues to Virulence Mechanisms of F. tularensis species.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2016, Virginia Tech

 The pathogen Francisella tularensis subsp. tularensis has been classified as a Center for Disease Control (CDC) select agent. However, little is still known of what… (more)

Subjects/Keywords: Francisella tularensis; genome instability; vaccine; transposase; inversion; duplica-tion; deletion; RNAseq; DNAseq; Assembly; Gene Dosage; Pathogen

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APA (6th Edition):

Modise, T. (2016). Genomic Instability and Gene Dosage Obscures Clues to Virulence Mechanisms of F. tularensis species. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/72885

Chicago Manual of Style (16th Edition):

Modise, Thero. “Genomic Instability and Gene Dosage Obscures Clues to Virulence Mechanisms of F. tularensis species.” 2016. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/72885.

MLA Handbook (7th Edition):

Modise, Thero. “Genomic Instability and Gene Dosage Obscures Clues to Virulence Mechanisms of F. tularensis species.” 2016. Web. 28 Feb 2021.

Vancouver:

Modise T. Genomic Instability and Gene Dosage Obscures Clues to Virulence Mechanisms of F. tularensis species. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/72885.

Council of Science Editors:

Modise T. Genomic Instability and Gene Dosage Obscures Clues to Virulence Mechanisms of F. tularensis species. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/72885


Virginia Tech

20. Yang, Kuan. Ancestral Genome Reconstruction in Bacteria.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2012, Virginia Tech

 The rapid accumulation of numerous sequenced genomes has provided a golden opportunity for ancestral state reconstruction studies, especially in the whole genome reconstruction area. However,… (more)

Subjects/Keywords: homology; genomics; NGP; phylogenetics; genome evolution simulation; ancestral genome reconstruction

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APA (6th Edition):

Yang, K. (2012). Ancestral Genome Reconstruction in Bacteria. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/28091

Chicago Manual of Style (16th Edition):

Yang, Kuan. “Ancestral Genome Reconstruction in Bacteria.” 2012. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/28091.

MLA Handbook (7th Edition):

Yang, Kuan. “Ancestral Genome Reconstruction in Bacteria.” 2012. Web. 28 Feb 2021.

Vancouver:

Yang K. Ancestral Genome Reconstruction in Bacteria. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/28091.

Council of Science Editors:

Yang K. Ancestral Genome Reconstruction in Bacteria. [Doctoral Dissertation]. Virginia Tech; 2012. Available from: http://hdl.handle.net/10919/28091


Virginia Tech

21. Ahmadian, Mansooreh. Hybrid Modeling and Simulation of Stochastic Effects on Biochemical Regulatory Networks.

Degree: PhD, Computer Science and Applications, 2020, Virginia Tech

 Cell cycle is a process in which a growing cell replicates its DNA and divides into two cells. Progression through the cell cycle is regulated… (more)

Subjects/Keywords: Cell Cycle Modeling; Hybrid Stochastic Modeling; Cell size control; Parameter estimation; Neural network; Theory-guided machine learning

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APA (6th Edition):

Ahmadian, M. (2020). Hybrid Modeling and Simulation of Stochastic Effects on Biochemical Regulatory Networks. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/99481

Chicago Manual of Style (16th Edition):

Ahmadian, Mansooreh. “Hybrid Modeling and Simulation of Stochastic Effects on Biochemical Regulatory Networks.” 2020. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/99481.

MLA Handbook (7th Edition):

Ahmadian, Mansooreh. “Hybrid Modeling and Simulation of Stochastic Effects on Biochemical Regulatory Networks.” 2020. Web. 28 Feb 2021.

Vancouver:

Ahmadian M. Hybrid Modeling and Simulation of Stochastic Effects on Biochemical Regulatory Networks. [Internet] [Doctoral dissertation]. Virginia Tech; 2020. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/99481.

Council of Science Editors:

Ahmadian M. Hybrid Modeling and Simulation of Stochastic Effects on Biochemical Regulatory Networks. [Doctoral Dissertation]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/99481


Virginia Tech

22. Badr, Eman. Identifying Splicing Regulatory Elements with de Bruijn Graphs.

Degree: PhD, Computer Science and Applications, 2015, Virginia Tech

 Splicing regulatory elements (SREs) are short, degenerate sequences on pre-mRNA molecules that enhance or inhibit the splicing process via the binding of splicing factors, proteins… (more)

Subjects/Keywords: Alternative splicing; de Bruijn graphs; algorithms; graph mining; splicing regulatory elements

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APA (6th Edition):

Badr, E. (2015). Identifying Splicing Regulatory Elements with de Bruijn Graphs. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/73366

Chicago Manual of Style (16th Edition):

Badr, Eman. “Identifying Splicing Regulatory Elements with de Bruijn Graphs.” 2015. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/73366.

MLA Handbook (7th Edition):

Badr, Eman. “Identifying Splicing Regulatory Elements with de Bruijn Graphs.” 2015. Web. 28 Feb 2021.

Vancouver:

Badr E. Identifying Splicing Regulatory Elements with de Bruijn Graphs. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/73366.

Council of Science Editors:

Badr E. Identifying Splicing Regulatory Elements with de Bruijn Graphs. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/73366

23. Krishnan, Siddharth. Seeing the Forest for the Trees: New approaches to Characterizing and Forecasting Cascades.

Degree: PhD, Computer Science and Applications, 2018, Virginia Tech

 Cascades are a popular construct to observe and study information propagation (or diffusion) in social media such as Twitter and are defined using notions of… (more)

Subjects/Keywords: Information cascades; Forecasting

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APA (6th Edition):

Krishnan, S. (2018). Seeing the Forest for the Trees: New approaches to Characterizing and Forecasting Cascades. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83362

Chicago Manual of Style (16th Edition):

Krishnan, Siddharth. “Seeing the Forest for the Trees: New approaches to Characterizing and Forecasting Cascades.” 2018. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/83362.

MLA Handbook (7th Edition):

Krishnan, Siddharth. “Seeing the Forest for the Trees: New approaches to Characterizing and Forecasting Cascades.” 2018. Web. 28 Feb 2021.

Vancouver:

Krishnan S. Seeing the Forest for the Trees: New approaches to Characterizing and Forecasting Cascades. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/83362.

Council of Science Editors:

Krishnan S. Seeing the Forest for the Trees: New approaches to Characterizing and Forecasting Cascades. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/83362


Virginia Tech

24. Arango Argoty, Gustavo Alonso. Computational Tools for Annotating Antibiotic Resistance in Metagenomic Data.

Degree: PhD, Computer Science and Applications, 2019, Virginia Tech

 Antimicrobial resistance (AMR) is one of the biggest threats to human public health. It has been estimated that the number of deaths caused by AMR… (more)

Subjects/Keywords: bioinformatics; metagenomics; antibiotic resistance; machine learning

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APA (6th Edition):

Arango Argoty, G. A. (2019). Computational Tools for Annotating Antibiotic Resistance in Metagenomic Data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/88987

Chicago Manual of Style (16th Edition):

Arango Argoty, Gustavo Alonso. “Computational Tools for Annotating Antibiotic Resistance in Metagenomic Data.” 2019. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/88987.

MLA Handbook (7th Edition):

Arango Argoty, Gustavo Alonso. “Computational Tools for Annotating Antibiotic Resistance in Metagenomic Data.” 2019. Web. 28 Feb 2021.

Vancouver:

Arango Argoty GA. Computational Tools for Annotating Antibiotic Resistance in Metagenomic Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/88987.

Council of Science Editors:

Arango Argoty GA. Computational Tools for Annotating Antibiotic Resistance in Metagenomic Data. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/88987


Virginia Tech

25. Jiang, Xiaofang. Genomics and Transcriptomics Analysis of the Asian Malaria Mosquito Anopheles stephensi.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2016, Virginia Tech

 Anopheles stephensi is a potent vector of malaria throughout the Indian subcontinent and Middle East. An. stephensi is emerging as a model for molecular and… (more)

Subjects/Keywords: genomic; comparative transcriptomes; dosage compensation; sex-specific expression Iso-Seq; trans-splicing

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APA (6th Edition):

Jiang, X. (2016). Genomics and Transcriptomics Analysis of the Asian Malaria Mosquito Anopheles stephensi. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/79959

Chicago Manual of Style (16th Edition):

Jiang, Xiaofang. “Genomics and Transcriptomics Analysis of the Asian Malaria Mosquito Anopheles stephensi.” 2016. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/79959.

MLA Handbook (7th Edition):

Jiang, Xiaofang. “Genomics and Transcriptomics Analysis of the Asian Malaria Mosquito Anopheles stephensi.” 2016. Web. 28 Feb 2021.

Vancouver:

Jiang X. Genomics and Transcriptomics Analysis of the Asian Malaria Mosquito Anopheles stephensi. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/79959.

Council of Science Editors:

Jiang X. Genomics and Transcriptomics Analysis of the Asian Malaria Mosquito Anopheles stephensi. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/79959


Virginia Tech

26. Aghamirzaie, Delasa. Isoform-Specific Expression During Embryo Development in Arabidopsis and Soybean.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2016, Virginia Tech

 Almost every precursor mRNA (pre-mRNA) in a eukaryotic organism undergoes splicing, in some cases resulting in the formation of more than one splice variant, a… (more)

Subjects/Keywords: Alternative splicing; data analysis; bioinformatics; transcriptomics; RNA-Seq; noncoding RNAs; machine learning; computational biology

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APA (6th Edition):

Aghamirzaie, D. (2016). Isoform-Specific Expression During Embryo Development in Arabidopsis and Soybean. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/73054

Chicago Manual of Style (16th Edition):

Aghamirzaie, Delasa. “Isoform-Specific Expression During Embryo Development in Arabidopsis and Soybean.” 2016. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/73054.

MLA Handbook (7th Edition):

Aghamirzaie, Delasa. “Isoform-Specific Expression During Embryo Development in Arabidopsis and Soybean.” 2016. Web. 28 Feb 2021.

Vancouver:

Aghamirzaie D. Isoform-Specific Expression During Embryo Development in Arabidopsis and Soybean. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/73054.

Council of Science Editors:

Aghamirzaie D. Isoform-Specific Expression During Embryo Development in Arabidopsis and Soybean. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/73054


Virginia Tech

27. Dang, Ha Xuan. Mold Allergomics: Comparative and Machine Learning Approaches.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2014, Virginia Tech

 Fungi are one of the major organisms that cause allergic disease in human. A number of proteins from fungi have been found to be allergenic… (more)

Subjects/Keywords: Fungal genomics; comparative genomics; allergy; allergen prediction

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APA (6th Edition):

Dang, H. X. (2014). Mold Allergomics: Comparative and Machine Learning Approaches. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/64205

Chicago Manual of Style (16th Edition):

Dang, Ha Xuan. “Mold Allergomics: Comparative and Machine Learning Approaches.” 2014. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/64205.

MLA Handbook (7th Edition):

Dang, Ha Xuan. “Mold Allergomics: Comparative and Machine Learning Approaches.” 2014. Web. 28 Feb 2021.

Vancouver:

Dang HX. Mold Allergomics: Comparative and Machine Learning Approaches. [Internet] [Doctoral dissertation]. Virginia Tech; 2014. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/64205.

Council of Science Editors:

Dang HX. Mold Allergomics: Comparative and Machine Learning Approaches. [Doctoral Dissertation]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/64205


Virginia Tech

28. Liu, Mingming. Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models.

Degree: PhD, Computer Science and Applications, 2015, Virginia Tech

 With the development of sequencing technologies, more and more sequence variants are available for investigation. Different types of variants in the human genome have been… (more)

Subjects/Keywords: Genetic variation; Indel; SNP; Hidden Markov Model

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APA (6th Edition):

Liu, M. (2015). Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/73703

Chicago Manual of Style (16th Edition):

Liu, Mingming. “Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models.” 2015. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/73703.

MLA Handbook (7th Edition):

Liu, Mingming. “Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models.” 2015. Web. 28 Feb 2021.

Vancouver:

Liu M. Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/73703.

Council of Science Editors:

Liu M. Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/73703

29. Song, Qi. Developing machine learning tools to understand transcriptional regulation in plants.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2019, Virginia Tech

 Abiotic stresses constitute a major category of stresses that negatively impact plant growth and development. It is important to understand how plants cope with environmental… (more)

Subjects/Keywords: regulatory network; machine learning; genomics

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APA (6th Edition):

Song, Q. (2019). Developing machine learning tools to understand transcriptional regulation in plants. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/93512

Chicago Manual of Style (16th Edition):

Song, Qi. “Developing machine learning tools to understand transcriptional regulation in plants.” 2019. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/93512.

MLA Handbook (7th Edition):

Song, Qi. “Developing machine learning tools to understand transcriptional regulation in plants.” 2019. Web. 28 Feb 2021.

Vancouver:

Song Q. Developing machine learning tools to understand transcriptional regulation in plants. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/93512.

Council of Science Editors:

Song Q. Developing machine learning tools to understand transcriptional regulation in plants. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/93512

30. Elmarakeby, Haitham Abdulrahman. Deep Learning for Biological Problems.

Degree: PhD, Computer Science and Applications, 2017, Virginia Tech

 The last decade has witnessed a tremendous increase in the amount of available biological data. Different technologies for measuring the genome, epigenome, transcriptome, proteome, metabolome,… (more)

Subjects/Keywords: Machine Learning; Computational Biology; Deep Learning; Cancer; Drug Response

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APA (6th Edition):

Elmarakeby, H. A. (2017). Deep Learning for Biological Problems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/86264

Chicago Manual of Style (16th Edition):

Elmarakeby, Haitham Abdulrahman. “Deep Learning for Biological Problems.” 2017. Doctoral Dissertation, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/86264.

MLA Handbook (7th Edition):

Elmarakeby, Haitham Abdulrahman. “Deep Learning for Biological Problems.” 2017. Web. 28 Feb 2021.

Vancouver:

Elmarakeby HA. Deep Learning for Biological Problems. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/86264.

Council of Science Editors:

Elmarakeby HA. Deep Learning for Biological Problems. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/86264

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