Author

Kumar Thurimella

Broad InstituteORCID 0000-0002-0819-4378Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:database / resource· 2 Organism:human· 2 Assay:metagenomics· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:cell-cell communication· 1 Research Focus:clustering· 1 Approach:deep learning· 1

Frequent Co-Authors

Catherine LozuponeiD· 2 shared Ahmed M. MohamediD· 1 shared Chenhao LiiD· 1 shared Damian Rafal PlichtaiD· 1 shared Daniel B. GrahamiD· 1 shared John D. SterrettiD· 1 shared Katrina Doenges· 1 shared Kevin D. QuinniD· 1 shared

Activity by Year

2019-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 15 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioinformatics

Catherine LozuponeORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Protein Language Models Uncover Carbohydrate-Active Enzyme Function in Metagenomic

Kumar Thurimella, Ahmed M. Mohamed, Chenhao Li, Tommi Vatanen, Daniel B. Graham, Róisı́n M. Owens, Sabina Leanti La Rosa, Damian Rafal Plichta, Sergio Bacallado, Ramnik J. Xavier

CAZyLingua uses protein language models to annotate carbohydrate-active enzymes in metagenomic data, outperforming traditional methods and revealing new annotations in disease-related datasets.

BMC Bioinformatics · Nov 26, 2025Peer-reviewedBioinformatics

SCNIC: Sparse Correlation Network Investigation for Compositional Data

Michael Shaffer, Kumar Thurimella, John D. Sterrett, Catherine Lozupone

SCNIC generates correlation networks and identifies modules of correlated features in compositional data, increasing statistical power and revealing microbial relationships.

Molecular Ecology Resources · Aug 24, 2022Peer-reviewedBioinformatics

AMON: Annotation of metabolite origins via networks to better integrate microbiome and metabolome data

M. Shaffer, Kumar Thurimella, Kevin D. Quinn, Katrina Doenges, Xuhong Zhang, Samantha S. Bokatzian, Nichole A. Reisdorph, Catherine Lozupone

AMON identifies the origin of metabolites as host, microbial, or environmental to improve integration of microbiome and metabolome data.

BMC Bioinformatics · Nov 28, 2019Peer-reviewedBioinformatics