Author

Karl Gemayel

Georgia Institute of Technology

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2 Genetics· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 4 Organism:bacteria (other)· 3 Research Focus:gene regulatory network inference· 3 Assay:whole-genome sequencing· 3 Research Focus:variant calling· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Approach:deep learning· 1

Frequent Co-Authors

Alexandre LomsadzeiD· 3 shared Mark Yu. BorodovskyiD· 3 shared Agnieszka Dobrowolska· 1 shared Alice Del Vecchio· 1 shared Andrew AnighoroiD· 1 shared Avelino Javer· 1 shared Cameron Field· 1 shared Chantriolnt-Andreas KapouraniiD· 1 shared

Activity by Year

2018-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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3 shared papersBioinformatics

Alexandre LomsadzeORCID

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

A DNA foundation model predicts osteoporosis risk genes without proximity bias

Cristian Regep, Chantriolnt-Andreas Kapourani, Emre Sofyalı, Agnieszka Dobrowolska, Giannis Loukas, Andrew Anighoro, Eleonora Canale, Torsten Gross, Marco P. Licciardello, Revant Gupta, Sorina Maciuca, Tariq Desai, Alice Del Vecchio, Cameron Field, Karl Gemayel, , Lindsay Edwards

Rosalind, a DNA foundation model, predicts variant-gene regulatory relationships without proximity bias, identifying distal genes as more impactful in osteoporosis.

bioRxiv · Mar 12, 2026 · PreprintGenetics

MetaGeneMark-2: Improved Gene Prediction in Metagenomes

Karl Gemayel, Alexandre Lomsadze, Mark Yu. Borodovsky

Gene caller that models GC-dependent ribosome binding and leaderless-promoter signals and detects TGA stop-codon reassignment, reported as more accurate than existing metagenomic gene finders.

bioRxiv · Jul 25, 2022 · PreprintBioinformatics

StartLink+: Prediction of Gene Starts in Prokaryotic Genomes by an Algorithm Integrating Independent Sources of Evidence

Karl Gemayel, Alexandre Lomsadze, Mark Yu. Borodovsky

StartLink+ improves gene start prediction in prokaryotic genomes by integrating ab initio and multiple sequence alignment methods, achieving 98-99% accuracy on verified genes.

bioRxiv · Oct 25, 2020 · PreprintBioinformatics

Improved Prokaryotic Gene Prediction Yields Insights into Transcription and Translation Mechanisms on Whole Genome Scale

Alexandre Lomsadze, Karl Gemayel, Shiyuyun Tang, Mark Yu. Borodovsky

The paper introduces GeneMarkS-2, an improved gene prediction algorithm that identifies diverse gene expression signals and reveals insights into prokaryotic transcription and translation mechanisms across genomes.

Genome Research · Jul 1, 2018Peer-reviewedGenomics