Author
Erich J. Baker
Baylor UniversityORCID 0000-0002-7798-5704Unclaimed ORCID profile · follow to get notified of new preprints
Biology Disciplines
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2017-2021 · peak 2/yrCo-Author Collaboration Network
10 top collaborators · 24 mutual co-author links5 papers
Recent·Most cited
Finding human gene-disease associations using a Network Enhanced Similarity Search (NESS) of multi-species heterogeneous functional genomics data
Timothy Reynolds, Jason A. Bubier, Michael Allen Langston, Elissa J. Chesler, Erich J. Baker
This paper introduces NESS, a tool that improves gene-disease association discovery by leveraging multi-species functional genomics data and network analysis to overcome data sparsity and bias.
bioRxiv · Mar 11, 2020 · PreprintBioinformatics
Systems genetic discovery of host-microbiome interactions reveals mechanisms of microbial involvement in disease
Jason A. Bubier, Vivek M. Philip, Christopher Quince, James H. Campbell, Yanjiao Zhou, Tatiana A. Vishnivetskaya, Suman Duvvuru, Rachael Hageman Blair, Juliet Ndukum, Kevin D. Donohue, Charles Phillips, Carmen M. Foster, David J. Mellert, George M. Weinstock, Cymbeline T. Culiat, , Elissa J. Chesler
The paper identifies host-microbiome interactions using systems genetics in mice, revealing mechanisms of microbial involvement in disease.
Genetics · Jan 2, 2020Peer-reviewedGenetics
Integration of heterogeneous functional genomics data in gerontology research identifies genes and pathway underlying aging across species
Jason A. Bubier, George L. Sutphin, Timothy Reynolds, Ron Korstanje, Axis Fuksman-Kumpa, Erich J. Baker, Michael Allen Langston, Elissa J. Chesler
The paper integrates heterogeneous functional genomics data across species to identify genes and pathways underlying aging, finding Cd63 as a key gene with lifespan-extending effects in C. elegans.
PLoS ONE · Apr 12, 2019Peer-reviewedBioinformatics
Protein classification using modified n-gram and skip-gram models
S. M. Ashiqul Islam, Benjamin J. Heil, Christopher Michel Kearney, Erich J. Baker
This study introduces a modified N-Gram and Skip-Gram model for protein classification from primary sequence data, showing improved accuracy over existing methods.
Bioinformatics · Dec 21, 2017Peer-reviewedBioinformatics