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You searched for +publisher:"Georgia Tech" +contributor:("Aluru, Srinivas"). Showing records 1 – 9 of 9 total matches.

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Georgia Tech

1. Audano, Peter Anthony. Scalable mapping-free algorithms and K-mer data structures for genomic analysis.

Degree: PhD, Biology, 2016, Georgia Tech

 An organism’s DNA sequence is a virtual cornucopia of information, and sequencing technology is the key to unlocking it. The past few decades have been… (more)

Subjects/Keywords: K-mer; Sequence analysis; Alignment-free; Mapping-free; Variant calling; Kestrel

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APA (6th Edition):

Audano, P. A. (2016). Scalable mapping-free algorithms and K-mer data structures for genomic analysis. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58591

Chicago Manual of Style (16th Edition):

Audano, Peter Anthony. “Scalable mapping-free algorithms and K-mer data structures for genomic analysis.” 2016. Doctoral Dissertation, Georgia Tech. Accessed March 23, 2019. http://hdl.handle.net/1853/58591.

MLA Handbook (7th Edition):

Audano, Peter Anthony. “Scalable mapping-free algorithms and K-mer data structures for genomic analysis.” 2016. Web. 23 Mar 2019.

Vancouver:

Audano PA. Scalable mapping-free algorithms and K-mer data structures for genomic analysis. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/1853/58591.

Council of Science Editors:

Audano PA. Scalable mapping-free algorithms and K-mer data structures for genomic analysis. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/58591


Georgia Tech

2. Roy, Indranil. Algorithmic techniques for the micron automata processor.

Degree: PhD, Computational Science and Engineering, 2015, Georgia Tech

 Our research is the first in-depth study in the use of the Micron Automata Processor, a novel re-configurable streaming co-processor which is purpose-built to execute… (more)

Subjects/Keywords: Automata processing; Bioinformatics; High performance algorithm design

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APA (6th Edition):

Roy, I. (2015). Algorithmic techniques for the micron automata processor. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53845

Chicago Manual of Style (16th Edition):

Roy, Indranil. “Algorithmic techniques for the micron automata processor.” 2015. Doctoral Dissertation, Georgia Tech. Accessed March 23, 2019. http://hdl.handle.net/1853/53845.

MLA Handbook (7th Edition):

Roy, Indranil. “Algorithmic techniques for the micron automata processor.” 2015. Web. 23 Mar 2019.

Vancouver:

Roy I. Algorithmic techniques for the micron automata processor. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/1853/53845.

Council of Science Editors:

Roy I. Algorithmic techniques for the micron automata processor. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53845


Georgia Tech

3. Brough, David. Process-structure linkages with materials knowledge systems.

Degree: PhD, Computational Science and Engineering, 2016, Georgia Tech

 The search for optimal manufacturing process routes that results in the combination of desired properties for any application is a highly dimensional optimization problem due… (more)

Subjects/Keywords: Materials knowledge systems; Multiscale simulations; Machine learning; Data sciences; Phase field

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APA (6th Edition):

Brough, D. (2016). Process-structure linkages with materials knowledge systems. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/56261

Chicago Manual of Style (16th Edition):

Brough, David. “Process-structure linkages with materials knowledge systems.” 2016. Doctoral Dissertation, Georgia Tech. Accessed March 23, 2019. http://hdl.handle.net/1853/56261.

MLA Handbook (7th Edition):

Brough, David. “Process-structure linkages with materials knowledge systems.” 2016. Web. 23 Mar 2019.

Vancouver:

Brough D. Process-structure linkages with materials knowledge systems. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/1853/56261.

Council of Science Editors:

Brough D. Process-structure linkages with materials knowledge systems. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/56261


Georgia Tech

4. Rogers, Emily. A novel method for cluster analysis of RNA structural data.

Degree: PhD, Computational Science and Engineering, 2018, Georgia Tech

 Functional RNA is known to contribute to a host of important biological pathways, with new discoveries being made daily. Because function is dependent on structure,… (more)

Subjects/Keywords: Computational biology; Structural biology; RNA folding; Boltzmann sampling; Cluster analysis; RNA secondary structure prediction

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APA (6th Edition):

Rogers, E. (2018). A novel method for cluster analysis of RNA structural data. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60232

Chicago Manual of Style (16th Edition):

Rogers, Emily. “A novel method for cluster analysis of RNA structural data.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 23, 2019. http://hdl.handle.net/1853/60232.

MLA Handbook (7th Edition):

Rogers, Emily. “A novel method for cluster analysis of RNA structural data.” 2018. Web. 23 Mar 2019.

Vancouver:

Rogers E. A novel method for cluster analysis of RNA structural data. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/1853/60232.

Council of Science Editors:

Rogers E. A novel method for cluster analysis of RNA structural data. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60232


Georgia Tech

5. Nathan, Eisha. Numerical and streaming analyses of centrality measures on graphs.

Degree: PhD, Computational Science and Engineering, 2018, Georgia Tech

 Graph data represent information about entities (vertices) and the relationships or connections between them (edges). In real-world networks today, new data are constantly being produced,… (more)

Subjects/Keywords: Dynamic graphs; Centrality measures

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APA (6th Edition):

Nathan, E. (2018). Numerical and streaming analyses of centrality measures on graphs. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59871

Chicago Manual of Style (16th Edition):

Nathan, Eisha. “Numerical and streaming analyses of centrality measures on graphs.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 23, 2019. http://hdl.handle.net/1853/59871.

MLA Handbook (7th Edition):

Nathan, Eisha. “Numerical and streaming analyses of centrality measures on graphs.” 2018. Web. 23 Mar 2019.

Vancouver:

Nathan E. Numerical and streaming analyses of centrality measures on graphs. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/1853/59871.

Council of Science Editors:

Nathan E. Numerical and streaming analyses of centrality measures on graphs. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59871


Georgia Tech

6. Pan, Tony C. Distributed memory building blocks for massive biological sequence analysis.

Degree: PhD, Computational Science and Engineering, 2018, Georgia Tech

 K-mer indices and de Bruijn graphs are important data structures in bioinformatics with multiple applications ranging from foundational tasks such as error correction, alignment, and… (more)

Subjects/Keywords: High performance computing; Bioinformatics; K-mer index; K-mer counting; De bruijn graph; Next generation sequencing; Parallel algorithms; Distributed memory; Distributed algorithms; SIMD vectorization; Cache friendly algorithms; MPI

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APA (6th Edition):

Pan, T. C. (2018). Distributed memory building blocks for massive biological sequence analysis. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59894

Chicago Manual of Style (16th Edition):

Pan, Tony C. “Distributed memory building blocks for massive biological sequence analysis.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 23, 2019. http://hdl.handle.net/1853/59894.

MLA Handbook (7th Edition):

Pan, Tony C. “Distributed memory building blocks for massive biological sequence analysis.” 2018. Web. 23 Mar 2019.

Vancouver:

Pan TC. Distributed memory building blocks for massive biological sequence analysis. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/1853/59894.

Council of Science Editors:

Pan TC. Distributed memory building blocks for massive biological sequence analysis. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59894


Georgia Tech

7. Green, Oded. High performance computing for irregular algorithms and applications with an emphasis on big data analytics.

Degree: PhD, Computational Science and Engineering, 2014, Georgia Tech

 Irregular algorithms such as graph algorithms, sorting, and sparse matrix multiplication, present numerous programming challenges, including scalability, load balancing, and efficient memory utilization. In this… (more)

Subjects/Keywords: Graph algorithms; Social network analysis; Parallel algorithms; High performance computing; Dynamic data; High performance computing; Big data; Algorithms; Social networks; Visual analytics

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APA (6th Edition):

Green, O. (2014). High performance computing for irregular algorithms and applications with an emphasis on big data analytics. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/51860

Chicago Manual of Style (16th Edition):

Green, Oded. “High performance computing for irregular algorithms and applications with an emphasis on big data analytics.” 2014. Doctoral Dissertation, Georgia Tech. Accessed March 23, 2019. http://hdl.handle.net/1853/51860.

MLA Handbook (7th Edition):

Green, Oded. “High performance computing for irregular algorithms and applications with an emphasis on big data analytics.” 2014. Web. 23 Mar 2019.

Vancouver:

Green O. High performance computing for irregular algorithms and applications with an emphasis on big data analytics. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/1853/51860.

Council of Science Editors:

Green O. High performance computing for irregular algorithms and applications with an emphasis on big data analytics. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/51860


Georgia Tech

8. Zakrzewska, Anita N. Graph analysis of streaming relational data.

Degree: PhD, Computational Science and Engineering, 2018, Georgia Tech

 Graph analysis can be used to study streaming data from a variety of sources, such as social networks, financial transactions, and online communication. The analysis… (more)

Subjects/Keywords: Graph algorithms; Graph analysis; Streaming data; Dynamic graphs; Community detection; Sampling

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APA (6th Edition):

Zakrzewska, A. N. (2018). Graph analysis of streaming relational data. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59917

Chicago Manual of Style (16th Edition):

Zakrzewska, Anita N. “Graph analysis of streaming relational data.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 23, 2019. http://hdl.handle.net/1853/59917.

MLA Handbook (7th Edition):

Zakrzewska, Anita N. “Graph analysis of streaming relational data.” 2018. Web. 23 Mar 2019.

Vancouver:

Zakrzewska AN. Graph analysis of streaming relational data. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/1853/59917.

Council of Science Editors:

Zakrzewska AN. Graph analysis of streaming relational data. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59917

9. Tang, Shiyuyun. Improving algorithms of gene prediction in prokaryotic genomes, metagenomes, and eukaryotic transcriptomes.

Degree: PhD, Biology, 2016, Georgia Tech

 Next-generation sequencing has generated enormous amount of DNA and RNA sequences that potentially carry volumes of genetic information, e.g. protein-coding genes. The thesis is divided… (more)

Subjects/Keywords: Gene prediction; Genome annotation; Prokaryotic genomes; Ribosomal binding site; Hidden Markov models; Adaptive training; Unsupervised self-training; Heuristic models; RNA-Seq; RNA transcripts; Frameshift prediction; Metagenomics

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APA (6th Edition):

Tang, S. (2016). Improving algorithms of gene prediction in prokaryotic genomes, metagenomes, and eukaryotic transcriptomes. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54998

Chicago Manual of Style (16th Edition):

Tang, Shiyuyun. “Improving algorithms of gene prediction in prokaryotic genomes, metagenomes, and eukaryotic transcriptomes.” 2016. Doctoral Dissertation, Georgia Tech. Accessed March 23, 2019. http://hdl.handle.net/1853/54998.

MLA Handbook (7th Edition):

Tang, Shiyuyun. “Improving algorithms of gene prediction in prokaryotic genomes, metagenomes, and eukaryotic transcriptomes.” 2016. Web. 23 Mar 2019.

Vancouver:

Tang S. Improving algorithms of gene prediction in prokaryotic genomes, metagenomes, and eukaryotic transcriptomes. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/1853/54998.

Council of Science Editors:

Tang S. Improving algorithms of gene prediction in prokaryotic genomes, metagenomes, and eukaryotic transcriptomes. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/54998

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