Author

James Hemp

University of UtahORCID 0000-0001-7193-0553Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Bioinformatics· 1 Evolutionary Biology· 1 Microbiology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Organism:bacteria (other)· 2 Research Focus:gene regulatory network inference· 2 Approach:biophysical modeling· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Ranjani MuraliiD· 2 shared Adrián A. Davín· 1 shared Andreas A. KappleriD· 1 shared Anja SpangiD· 1 shared Bastien BoussauiD· 1 shared Ben J. WoodcroftiD· 1 shared Benoit MoreliD· 1 shared Daniel StraubiD· 1 shared

Activity by Year

2019-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 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 papersEvolutionary Biology

Ranjani MuraliORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

MicrobiologybioRxivPreprintMay 24, 2026

Towards understanding nitrate-reducing Fe(II) oxidation in Ferrigenium straubiae: spotlight on old and new key protein candidates

Stefanie Becker, Daniel Straub, James Hemp, Andreas A. Kappler

The paper identifies key proteins and metabolic pathways in Ferrigenium straubiae involved in nitrate-reducing Fe(II) oxidation using transcriptomic and genomic analyses.

1 citation

bacteria (other)bulk RNA-seq

𝕏 PostBluesky

Evolutionary BiologybioRxivPreprintAug 8, 2023

An evolutionary timescale for Bacteria calibrated using the Great Oxidation Event

Adrián A. Davín, Ben J. Woodcroft, Rochelle M. Soo…·Gergely J. Szöllősi

This paper uses machine learning and phylogenetic reconciliation to estimate the evolutionary timeline of Bacteria, linking transitions to aerobic lifestyles to the Great Oxidation Event.

DOI 10.1101/2023.08.08.552427

bacteria (other)machine learning (classical)

𝕏 PostBluesky

BioinformaticsbioRxivPreprintMar 30, 2019

FIND: Identifying Functionally and Structurally Important Features in Protein Sequences with Deep Neural Networks

Ranjani Murali, James Hemp, Victoria J. Orphan, Yonatan Bisk

The paper introduces a deep learning model called FIND to predict protein function from amino acid sequences, revealing structurally and functionally relevant features in proteins.

3 citations

protein sequencedeep learning

𝕏 PostBluesky