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You searched for +publisher:"Virginia Tech" +contributor:("Heath, Lenwood S."). Showing records 1 – 30 of 80 total matches.

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1. 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, 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 December 09, 2019. 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. 09 Dec 2019.

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 2019 Dec 09]. 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

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

Degree: MS, Computer Science, 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

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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 December 09, 2019. http://hdl.handle.net/10919/85044.

MLA Handbook (7th Edition):

Wagner, Mitchell James. “Reconstructing Signaling Pathways Using Regular-Language Constrained Paths.” 2018. Web. 09 Dec 2019.

Vancouver:

Wagner MJ. Reconstructing Signaling Pathways Using Regular-Language Constrained Paths. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2019 Dec 09]. 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

3. Maxwell, Evan Kyle. Graph Mining Algorithms for Memory Leak Diagnosis and Biological Database Clustering.

Degree: MS, Computer Science, 2010, Virginia Tech

 Large graph-based datasets are common to many applications because of the additional structure provided to data by graphs. Patterns extracted from graphs must adhere to… (more)

Subjects/Keywords: graph mining; graph clustering; multipartite cliques; memory leak detection; bioinformatics

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APA (6th Edition):

Maxwell, E. K. (2010). Graph Mining Algorithms for Memory Leak Diagnosis and Biological Database Clustering. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/34008

Chicago Manual of Style (16th Edition):

Maxwell, Evan Kyle. “Graph Mining Algorithms for Memory Leak Diagnosis and Biological Database Clustering.” 2010. Masters Thesis, Virginia Tech. Accessed December 09, 2019. http://hdl.handle.net/10919/34008.

MLA Handbook (7th Edition):

Maxwell, Evan Kyle. “Graph Mining Algorithms for Memory Leak Diagnosis and Biological Database Clustering.” 2010. Web. 09 Dec 2019.

Vancouver:

Maxwell EK. Graph Mining Algorithms for Memory Leak Diagnosis and Biological Database Clustering. [Internet] [Masters thesis]. Virginia Tech; 2010. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/10919/34008.

Council of Science Editors:

Maxwell EK. Graph Mining Algorithms for Memory Leak Diagnosis and Biological Database Clustering. [Masters Thesis]. Virginia Tech; 2010. Available from: http://hdl.handle.net/10919/34008


Virginia Tech

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

Degree: MS, Computer Science, 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 December 09, 2019. http://hdl.handle.net/10919/52973.

MLA Handbook (7th Edition):

Maji, Nabanita. “An Interactive Tutorial for NP-Completeness.” 2015. Web. 09 Dec 2019.

Vancouver:

Maji N. An Interactive Tutorial for NP-Completeness. [Internet] [Masters thesis]. Virginia Tech; 2015. [cited 2019 Dec 09]. 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

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

Degree: MS, Electrical and Computer 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 December 09, 2019. 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. 09 Dec 2019.

Vancouver:

Aggarwal D. Inferring Signal Transduction Pathways from Gene Expression Data using Prior Knowledge. [Internet] [Masters thesis]. Virginia Tech; 2015. [cited 2019 Dec 09]. 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

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

Degree: MS, Computer Science, 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 December 09, 2019. 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. 09 Dec 2019.

Vancouver:

Senthil R. IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks. [Internet] [Masters thesis]. Virginia Tech; 2016. [cited 2019 Dec 09]. 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


Virginia Tech

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

Degree: MS, Computer Science, 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 December 09, 2019. http://hdl.handle.net/10919/23889.

MLA Handbook (7th Edition):

Paul, Ann Molly. “QBank: A Web-Based Dynamic Problem Authoring Tool.” 2013. Web. 09 Dec 2019.

Vancouver:

Paul AM. QBank: A Web-Based Dynamic Problem Authoring Tool. [Internet] [Masters thesis]. Virginia Tech; 2013. [cited 2019 Dec 09]. 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

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

Degree: MS, Computer Science, 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 December 09, 2019. http://hdl.handle.net/10919/84470.

MLA Handbook (7th Edition):

Robertson, Jeffrey Alan. “Entropy Measurements and Ball Cover Construction for Biological Sequences.” 2018. Web. 09 Dec 2019.

Vancouver:

Robertson JA. Entropy Measurements and Ball Cover Construction for Biological Sequences. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2019 Dec 09]. 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


Virginia Tech

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

Degree: MS, Computer Science, 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 December 09, 2019. http://hdl.handle.net/10919/84999.

MLA Handbook (7th Edition):

Yang, Yanshen. “MCAT: Motif Combining and Association Tool.” 2018. Web. 09 Dec 2019.

Vancouver:

Yang Y. MCAT: Motif Combining and Association Tool. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2019 Dec 09]. 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

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

Degree: MS, Computer Science, 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 December 09, 2019. http://hdl.handle.net/10919/86518.

MLA Handbook (7th Edition):

Nayyar, Krati. “Input Sensitive Analysis of a Minimum Metric Bipartite Matching Algorithm.” 2017. Web. 09 Dec 2019.

Vancouver:

Nayyar K. Input Sensitive Analysis of a Minimum Metric Bipartite Matching Algorithm. [Internet] [Masters thesis]. Virginia Tech; 2017. [cited 2019 Dec 09]. 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


Virginia Tech

11. 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 December 09, 2019. http://hdl.handle.net/10919/31591.

MLA Handbook (7th Edition):

Parikh, Nidhi Kiranbhai. “Generating Random Graphs with Tunable Clustering Coefficient.” 2011. Web. 09 Dec 2019.

Vancouver:

Parikh NK. Generating Random Graphs with Tunable Clustering Coefficient. [Internet] [Masters thesis]. Virginia Tech; 2011. [cited 2019 Dec 09]. 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

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

Degree: PhD, Animal and Poultry Sciences, 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 December 09, 2019. http://hdl.handle.net/10919/72969.

MLA Handbook (7th Edition):

Vijayan, Vinaya. “Understanding and Improving Identification of Somatic Variants.” 2016. Web. 09 Dec 2019.

Vancouver:

Vijayan V. Understanding and Improving Identification of Somatic Variants. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Dec 09]. 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 December 09, 2019. 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. 09 Dec 2019.

Vancouver:

Hasan MS. Identifying and Analyzing Indel Variants in the Human Genome Using Computational Approaches. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2019 Dec 09]. 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. Krishnan, Siddharth. Seeing the Forest for the Trees: New approaches to Characterizing and Forecasting Cascades.

Degree: PhD, Computer Science, 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 December 09, 2019. 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. 09 Dec 2019.

Vancouver:

Krishnan S. Seeing the Forest for the Trees: New approaches to Characterizing and Forecasting Cascades. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2019 Dec 09]. 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

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

Degree: PhD, Animal and Poultry Sciences, 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 December 09, 2019. http://hdl.handle.net/10919/64205.

MLA Handbook (7th Edition):

Dang, Ha Xuan. “Mold Allergomics: Comparative and Machine Learning Approaches.” 2014. Web. 09 Dec 2019.

Vancouver:

Dang HX. Mold Allergomics: Comparative and Machine Learning Approaches. [Internet] [Doctoral dissertation]. Virginia Tech; 2014. [cited 2019 Dec 09]. 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

16. 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 December 09, 2019. http://hdl.handle.net/10919/28091.

MLA Handbook (7th Edition):

Yang, Kuan. “Ancestral Genome Reconstruction in Bacteria.” 2012. Web. 09 Dec 2019.

Vancouver:

Yang K. Ancestral Genome Reconstruction in Bacteria. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2019 Dec 09]. 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

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 December 09, 2019. 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. 09 Dec 2019.

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 2019 Dec 09]. 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. Altarawy, Doaa Abdelsalam Ahmed Mohamed. DeTangle: A Framework for Interactive Prediction and Visualization of Gene Regulatory Networks.

Degree: PhD, Computer Science, 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 December 09, 2019. 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. 09 Dec 2019.

Vancouver:

Altarawy DAAM. DeTangle: A Framework for Interactive Prediction and Visualization of Gene Regulatory Networks. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2019 Dec 09]. 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

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

Degree: PhD, Animal and Poultry Sciences, 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 December 09, 2019. 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. 09 Dec 2019.

Vancouver:

Modise T. Genomic Instability and Gene Dosage Obscures Clues to Virulence Mechanisms of F. tularensis species. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Dec 09]. 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. Aghamirzaie, Delasa. Isoform-Specific Expression During Embryo Development in Arabidopsis and Soybean.

Degree: PhD, Animal and Poultry Sciences, 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 December 09, 2019. http://hdl.handle.net/10919/73054.

MLA Handbook (7th Edition):

Aghamirzaie, Delasa. “Isoform-Specific Expression During Embryo Development in Arabidopsis and Soybean.” 2016. Web. 09 Dec 2019.

Vancouver:

Aghamirzaie D. Isoform-Specific Expression During Embryo Development in Arabidopsis and Soybean. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Dec 09]. 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

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

Degree: PhD, Computer Science, 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. M. (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 Mostafa. “Identifying Splicing Regulatory Elements with de Bruijn Graphs.” 2015. Doctoral Dissertation, Virginia Tech. Accessed December 09, 2019. http://hdl.handle.net/10919/73366.

MLA Handbook (7th Edition):

Badr, Eman Mostafa. “Identifying Splicing Regulatory Elements with de Bruijn Graphs.” 2015. Web. 09 Dec 2019.

Vancouver:

Badr EM. Identifying Splicing Regulatory Elements with de Bruijn Graphs. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/10919/73366.

Council of Science Editors:

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


Virginia Tech

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

Degree: PhD, Computer Science, 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 December 09, 2019. 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. 09 Dec 2019.

Vancouver:

Liu M. Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2019 Dec 09]. 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


Virginia Tech

23. Eid, Fatma Elzahraa Sobhy. Predicting the Interactions of Viral and Human Proteins.

Degree: PhD, Computer Science, 2017, Virginia Tech

 The world has proven unprepared for deadly viral outbreaks. Designing antiviral drugs and strategies requires a firm understanding of the interactions taken place between the… (more)

Subjects/Keywords: Protein-Protein Interaction; Virus; Machine Learning; Zika Virus

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APA (6th Edition):

Eid, F. E. S. (2017). Predicting the Interactions of Viral and Human Proteins. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/77581

Chicago Manual of Style (16th Edition):

Eid, Fatma Elzahraa Sobhy. “Predicting the Interactions of Viral and Human Proteins.” 2017. Doctoral Dissertation, Virginia Tech. Accessed December 09, 2019. http://hdl.handle.net/10919/77581.

MLA Handbook (7th Edition):

Eid, Fatma Elzahraa Sobhy. “Predicting the Interactions of Viral and Human Proteins.” 2017. Web. 09 Dec 2019.

Vancouver:

Eid FES. Predicting the Interactions of Viral and Human Proteins. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/10919/77581.

Council of Science Editors:

Eid FES. Predicting the Interactions of Viral and Human Proteins. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/77581


Virginia Tech

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

Degree: PhD, Animal and Poultry Sciences, 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 December 09, 2019. 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. 09 Dec 2019.

Vancouver:

Jiang X. Genomics and Transcriptomics Analysis of the Asian Malaria Mosquito Anopheles stephensi. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Dec 09]. 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

25. 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 December 09, 2019. http://hdl.handle.net/10919/26318.

MLA Handbook (7th Edition):

Belal, Nahla Ahmed. “Two Problems in Computational Genomics.” 2011. Web. 09 Dec 2019.

Vancouver:

Belal NA. Two Problems in Computational Genomics. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2019 Dec 09]. 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

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

Degree: PhD, Computer Science, 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 December 09, 2019. http://hdl.handle.net/10919/86264.

MLA Handbook (7th Edition):

Elmarakeby, Haitham Abdulrahman. “Deep Learning for Biological Problems.” 2017. Web. 09 Dec 2019.

Vancouver:

Elmarakeby HA. Deep Learning for Biological Problems. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2019 Dec 09]. 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


Virginia Tech

27. 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 December 09, 2019. http://hdl.handle.net/10919/93512.

MLA Handbook (7th Edition):

Song, Qi. “Developing machine learning tools to understand transcriptional regulation in plants.” 2019. Web. 09 Dec 2019.

Vancouver:

Song Q. Developing machine learning tools to understand transcriptional regulation in plants. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2019 Dec 09]. 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


Virginia Tech

28. 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 December 09, 2019. 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. 09 Dec 2019.

Vancouver:

Arango Argoty GA. Computational Tools for Annotating Antibiotic Resistance in Metagenomic Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2019 Dec 09]. 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

29. 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 December 09, 2019. 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. 09 Dec 2019.

Vancouver:

Torkey HA. Machine Learning Approaches for Identifying microRNA Targets and Conserved Protein Complexes. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2019 Dec 09]. 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

30. Bhuiyan, Md Hasanuzzaman. Parallel Algorithms for Switching Edges and Generating Random Graphs from Given Degree Sequences using HPC Platforms.

Degree: PhD, Computer Science, 2017, Virginia Tech

 Networks (or graphs) are an effective abstraction for representing many real-world complex systems. Analyzing various structural properties of and dynamics on such networks reveal valuable… (more)

Subjects/Keywords: Network Science; Parallel Algorithms; High Performance Computing; Edge Switch; Random Networks

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APA (6th Edition):

Bhuiyan, M. H. (2017). Parallel Algorithms for Switching Edges and Generating Random Graphs from Given Degree Sequences using HPC Platforms. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/80299

Chicago Manual of Style (16th Edition):

Bhuiyan, Md Hasanuzzaman. “Parallel Algorithms for Switching Edges and Generating Random Graphs from Given Degree Sequences using HPC Platforms.” 2017. Doctoral Dissertation, Virginia Tech. Accessed December 09, 2019. http://hdl.handle.net/10919/80299.

MLA Handbook (7th Edition):

Bhuiyan, Md Hasanuzzaman. “Parallel Algorithms for Switching Edges and Generating Random Graphs from Given Degree Sequences using HPC Platforms.” 2017. Web. 09 Dec 2019.

Vancouver:

Bhuiyan MH. Parallel Algorithms for Switching Edges and Generating Random Graphs from Given Degree Sequences using HPC Platforms. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/10919/80299.

Council of Science Editors:

Bhuiyan MH. Parallel Algorithms for Switching Edges and Generating Random Graphs from Given Degree Sequences using HPC Platforms. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/80299

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