Research in #Biomedical #DataScience & #Bioinformatics #CompBio. @MarkGerstein AT @YaleMBB @YaleMed
Yale University
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New study conducted by our lab in collaboration with #PsychENCODE has made significant discoveries linking genetic variants to genes and cell types in human brain. #psychencode24
For more details, refer to our original thread: nitter.net/MarkGerstein/status/17…
news.yale.edu/2024/05/23/tra…
New paper on single-cell genomics & regulatory networks for 388 human brains just out in @ScienceMagazine. Neat stuff on single-cell QTLs, cell-to-cell communication, & DL models simulating drug effects (science.org/doi/10.1126/scie…) #PsychENCODE24
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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-artic…
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Gerstein Lab | Yale retweeted
Posting my talk today at @UniBarcelona on the 25th anniversary of the human genome (hosted by @beaborsari) lectures.gersteinlab.org/sum… Lots of new slides on variant impact models & pseudogene epigenetics across tissues
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Gerstein Lab | Yale retweeted
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/sum… Has lots of slides on case studies for LLMs in brain genomics & comparisons with more classic approaches
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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.
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Gerstein Lab | Yale retweeted
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)
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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/s41467-0…
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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
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By Jonathan Warrell, Michael Gancz, Hussein Mohsen, Prashant Emani & @MarkGerstein
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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
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🔐 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-method…
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🚀 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/bioinformati…
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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=ls5L…
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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.
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🆕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/pnas.24…
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By @RanMeng_m, William Zhu, Christopher Cameron, Pengyu Ni, Xiao Zhou, Tselmeg Ulammandakh, and @MarkGerstein.
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By @RanMeng_m, William Zhu, Christopher Cameron, Pengyu Ni, Xiao Zhou, Tselmeg Ulammandakh, and @MarkGerstein.
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📚 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)
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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/s41467-025-6…
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New @NatureComms paper led by @beaborsari & Mor Frank. Also thanks to co-authors Eve Wattenberg, @KeXU0828, @Susannaliu99, @XuezhuYu & @MarkGerstein!
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