Method Of Machine Learning, Employing Bayesian Latent Class Inference: Combining Multiple Genomic Feature Detection Algorithms To Produce An Integrated Genomic Feature Set With Specificity, Sensitivity And Accuracy
- Austin TX, US Brandi CANTAREL - Austin TX, US Justin REESE - Austin TX, US Daniel B. WEAVER - Austin TX, US
Assignee:
GENFORMATIC, LLC - Austin TX
International Classification:
G06F 19/24 G06N 99/00 G06N 7/00
US Classification:
706 12, 706 52
Abstract:
BAYSIC (BAYesian System for Integrated Combination) combines sets of genomic and other biological data features to optimize selected data feature attributes, for example, detecting genome variants including single nucleotide variants (SNVs) and small insertion/deletions in genomes. The present disclosure presents one possible embodiment employing BAYSIC to combine single nucleotide variants detected by several distinct variant calling methods into an integrated SNV call set that is more accurate than any single SNV calling method or any ad hoc method of combining call sets. BAYSIC is a, tested and validated method using unsupervised machine learning, employing Bayesian latent class inference to combine variant sets produced by different packages.
Lawrence Berkeley National Laboratory
Computational Biologist
Georgetown University Aug 2008 - Sep 2012
Affiliate Research Professor and Managing Director, Reese Consulting, Ltd
Texas A&M University 2004 - 2007
Postdoctoral Researcher
University of Virginia 1997 - 2004
Research Associate
University of Georgia 1995 - 1997
Research Associate
Education:
University of Virginia 1997 - 2004
Doctorates, Doctor of Philosophy
The University of Georgia 1995 - 1997
Master of Science, Masters, Immunology
Penn State Erie, The Behrend College - Electrical Engineering
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Justin Reese
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University of Virginia - Computational Biology
Justin Reese
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Anthrocon - Director
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Justin Reese
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MTSU - CIM
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