Herbert Wertheim College of Engineering
Kahveci Lab
Led by Tamer Kahveci · Computer and Information Science and Engineering
What we study
The Kahveci Lab develops computer tools to analyze biological data, focusing on graph algorithms, machine learning, and biological networks. They work with high-dimensional omics data and spatial transcriptomics.
What working here is like
- Computational
- No organisms (data only)
- Coding required
- Papers
- 5
Recent papers, explained
- CoDER: Consistent Drug Efficacy Ranking
2026 · IEEE Journal of Biomedical and Health Informatics
Ranks drugs by their consistent effectiveness across different tissues.
- SPACT: A clustering-driven multi-modal framework for survival prediction using genomic and histopathology data
2026 · Medical Image Analysis
Show 3 more papersShow fewer
- STORM: spatial transcriptomics optimization by resolution via matrix factorization
2026 · Briefings in Bioinformatics
Improves spatial gene expression data using machine learning to reveal clearer patterns.
- MOSAIC: MOtif Set With Maximal InfluenCe on Network
2026 · IEEE Transactions on Computational Biology and Bioinformatics
Studies how groups of network motifs influence biological networks.
- MAGE: an efficient algorithm for identifying Maximal Age-indexed GEne sets
2026 · Briefings in Bioinformatics
Develops an algorithm to find gene sets that change most with age in biological networks.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
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