Author

Chenkai Li

BC Cancer AgencyORCID 0000-0002-8748-0099Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:differential expression· 2 Organism:human· 2 Assay:protein sequence· 2 Assay:bulk RNA-seq· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1

Frequent Co-Authors

İnanç BiroliD· 4 shared Caren C. HelbingiD· 2 shared Chen YangiD· 2 shared Darcy SutherlandiD· 2 shared Linda HoangiD· 2 shared René L. WarreniD· 2 shared Ali SalehiiD· 1 shared Alison M. ElliottiD· 1 shared

Activity by Year

2020-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 34 mutual co-author links

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4 shared papersBioinformatics

İnanç BirolORCID

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4 papers

Recent·Most cited

Mining the UniProtKB/Swiss-Prot database forantimicrobial peptides

Chenkai Li, Darcy Sutherland, Ali Salehi, Amelia Richter, Diana Lin, Sambina Islam Aninta, Hossein Ebrahimikondori, Anat Yanai, Lauren Coombe, René L. Warren, Monica Kotkoff, Linda Hoang, Caren C. Helbing, İnanç Birol

The paper identifies novel antimicrobial peptides from the UniProtKB/Swiss-Prot database using AMPlify, prioritizing 40 for chicken applications and testing 38 for antimicrobial activity.

Protein Science · Mar 18, 2025Peer-reviewedBioinformatics

AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens

Chenkai Li, Darcy Sutherland, S. Austin Hammond, Chen Yang, Figali Taho, Lauren C. Bergman, Simon Houston, René L. Warren, Titus Wong, Linda Hoang, Caroline E. Cameron, Caren C. Helbing, İnanç Birol

AMPlify is a deep learning model that predicts antimicrobial peptides from bullfrog genomes, identifying novel peptides effective against WHO priority pathogens including drug-resistant bacteria.

BMC Genomics · Jan 25, 2022Peer-reviewedBioinformatics

Genome-Wide Sequencing as a First-Tier Screening Test for Short Tandem Repeat Expansions

Indhu‐Shree Rajan‐Babu, Junran J. Peng, Readman Chiu, IMAGINE Study, Patricia H. Birch, Madeline Couse, Colleen Guimond, Anna Lehman, Jill C. Mwenifumbo, Clara van Karnebeek, Jan M. Friedman, CAUSES Study, Shelin Adam, Christèle du Souich, Alison M. Elliott, , İnanç Birol

The paper evaluates STR detection tools and develops a pipeline to identify pathogenic expansions in exome/genome data, finding 27 samples with STR mutations in 306 families.

Genome Medicine · Aug 9, 2021Peer-reviewedGenetics

Terminitor: Cleavage Site Prediction Using Deep Learning Models

Chen Yang, Chenkai Li, Ka Ming Nip, René L Warren, İnanç Birol

Terminitor predicts polyadenylation cleavage sites from RNA-seq data, outperforming existing tools in sensitivity and precision on experimental data.

bioRxiv · Apr 23, 2020 · PreprintBioinformatics