Gerstein Lab | Yale @GersteinLab
Research in #Biomedical #DataScience & #Bioinformatics #CompBio. @MarkGerstein AT @YaleMBB @YaleMed gersteinlab.org Yale University Joined October 2009-
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New paper in Protein Science! Meet DreamFold: a World Model that generates protein folding pathways and reveals druggable intermediate states: onlinelibrary.wiley.com/doi/10.1002/pr…
Excited to see our work featured by @YaleMed! We introduce MedicalAgentsBench, a benchmark for evaluating how multi-agent LLMs compare with reasoning models—and how they can complement each other in clinical decision-making: medicine.yale.edu/news-article/h…
Posting my talk today at @UniBarcelona on the 25th anniversary of the human genome (hosted by @beaborsari) lectures.gersteinlab.org/summary/Changi… Lots of new slides on variant impact models & pseudogene epigenetics across tissues
Posting my talk today for tutorial IP2 at @iscb's #ISMB2026: Large Language Models & Agentic AI for Biomedical Informatics (organized by @XiangruTang) lectures.gersteinlab.org/summary/LLMs-n… Has lots of slides on case studies for LLMs in brain genomics & comparisons with more classic approaches
During our recent lab roster meeting, we asked about everyone's AI chatbot preferences (n=69, multi-select): 🥇 Claude — 49 (71%) 🥈 ChatGPT — 43 (62%) 🥉 Gemini — 14 (20%) Takeaway: Mostly Claude + ChatGPT co-usage, with Gemini as a distant third and a long tail of niche tools.
Post term, cleaned up my biomed. data sci. course website. gersteinlab.org/courses/3520 Now in its 28th year. New thing this year was #AI integration: the students merged (with color coding) the manual & AI summaries of each lecture. Also, lots of new posted videos & PDFs (esp. on DL)
New @naturecomms paper by @katerbowie @MarkGerstein @jordan_peccia @H2O_Hannah. We study how disinfection shapes microbes in hospital sink drain biofilms. Biofilms regrew in 4 days, enriched for carbapenem-resistant bacteria and multidrug efflux pump genes nature.com/articles/s4146…
By Jonathan Warrell, Michael Gancz, Hussein Mohsen, Prashant Emani & @MarkGerstein
In our @NatMachIntell paper, we introduce a framework to analyse interpretability in deep learning by drawing on a formal notion of model semantics from the philosophy of science. We illustrate our framework with examples from biomedicine. Read here: rdcu.be/e9uYh
Article link: doi.org/10.1101/gr.280…
Curious how pseudogenes are transcriptionally regulated? Our new @genomeresearch paper shows processed pseudogenes break the rules: they’re transcribed without classic epigenetic marks, linked to enhancers, and enriched for YY1 motifs. Study co-led by @YunzheJ and @beaborsari
🔐 New open-access paper in Cell Reports Methods! We show that fully homomorphic encryption enables privacy-preserving polygenic risk scores (PRS), allowing secure computation directly on encrypted genomes with near-zero accuracy loss. 📄 cell.com/cell-reports-m…
🚀 New paper in Bioinformatics! Our #ASTRO work led by @dingyao_zhang introduces "ASTRO: Automated Spatial-Transcriptome whole RNA Output", an automated pipeline optimized for whole-transcriptome spatial analysis, especially in challenging FFPE samples. 🔗 doi.org/10.1093/bioinf…
@NeurIPSConf @_YunyangLI This work is a close collaboration with colleagues previously at Microsoft Research (@MSFTResearch) and currently at Ubiquant and various other places. Huge thanks to them for the ideas and computational resources.
Our @NeurIPSConf work led by @_YunyangLI “E2Former: An Efficient and Equivariant Transformer with Linear-Scaling Tensor Products” was selected as a spotlight (with score ranked ~17 / 21k submissions). Poster: Thur Dec 4, Exhibit Hall CDE #5512 Online: openreview.net/forum?id=ls5L4…
By @RanMeng_m, William Zhu, Christopher Cameron, Pengyu Ni, Xiao Zhou, Tselmeg Ulammandakh, and @MarkGerstein.
🆕Our new PNAS study bridges histology and genomics! Using deep learning and imageQTL analysis, we show how tissue images reflect gene expression and aging — making histology more interpretable with AI. pnas.org/doi/10.1073/pn…
By @RanMeng_m, William Zhu, Christopher Cameron, Pengyu Ni, Xiao Zhou, Tselmeg Ulammandakh, and @MarkGerstein.
📚 Yale students have returned to campus, so time for a roster meeting! We again made our Nobel Prize predictions (given how accurate we were last year 😉) 🥇Our top prediction is Habener & Knudsen (GLP-1) with 28.5% of the vote! 🥈 In second is Rothberg & David Klenerman (NGS)
New @NatureComms paper led by @beaborsari & Mor Frank. Also thanks to co-authors Eve Wattenberg, @KeXU0828, @Susannaliu99, @XuezhuYu & @MarkGerstein!
Curious how your favorite gene changes when and how during a biological process? Want to dive into the kinetics of chromatin + gene expression? Meet chronODE, our new tool to model multi-omic time-series with logistic equations + ML! doi.org/10.1038/s41467…
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