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Bioinformatics methods for protein identification using peptide mass fingerprinting data
(University of Missouri--Columbia, 2009)
Protein identification using mass spectrometry is an important yet partially solved problem in the study of proteomics during the post-genomic era. The major techniques used in mass spectrometry are Peptide Mass Fingerprinting (PMF) and Tandem mass...
Protein transport : bioinformatics methods for understanding protein subcellular localization
(University of Missouri--Columbia, 2018)
an efficient and effective way for studying the protein subcellular localization on the whole-proteome level. Here, we present in this dissertation the bioinformatics methods for studying protein subcellular localization. We reviewed the studies of protein...
Genome-wide microbial phylogeny reconstruction with polytomy identification
(University of Missouri--Columbia, 2010)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Evolutionary comparative genomics is one of the most studied areas in the computational biology. With ever increasing speed of newly sequenced microbial ...
Large-scale soybean genome-wide variation workflow and association analysis using deep learning
(University of Missouri--Columbia, 2019)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] With the advances in next-generation sequencing technology and significant reduction in sequencing costs, it is now possible to sequence large collections ...
Genome scale meta analysis of microarrays for biological inferences
(University of Missouri--Columbia, 2009)
In this present era of high-throughput technologies, meta-analysis is being widely used to integrate multiple similar high throughput studies. Here we propose a novel framework for applying meta-analysis techniques on ...
Applications of deep neural networks to protein structure prediction
(University of Missouri--Columbia, 2018)
Protein secondary structure, backbone torsion angle and other secondary structure features can provide useful information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity ...