
Research in #Biomedical #DataScience & #Bioinformatics #CompBio. @MarkGerstein AT @YaleMBB @YaleMed
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/…
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…
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
🔐 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…
🚀 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…
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…
4/4 ⚡ WANet + WALoss ⇒ 18 % faster SCF convergence & 1 000 × energy-error reduction vs. SOTA. One model, many properties—HOMO/LUMO, dipoles, electron densities—all from a single predicted Hamiltonian.
2/4 🧩 We introduce Wavefunction-Alignment Loss + WANet, slashing SCF iterations while keeping ab-initio precision for molecules 3× larger than training data.
1/4 🚀 New #ICLR2025 SPOTLIGHT ALERT
Gerstein Lab presents “Enhancing the Scalability & Applicability of Kohn-Sham Hamiltonians”—led by @_YunyangLI & Z Xia & L Huang & J Zhang & @MarkGerstein. Joint work with @MSFTResearch.
Our new paper in @jbi_journal describes the iDASH-winning method for efficient blockchain storage of biomedical data. We cut gas costs by 60% and sped up retrieval 500x with low-level Solidity optimization.
By Eric Ni, Elizabeth Knight, @MarkGerstein
doi.org/10.1016/j.jbi.2025.1…
New @BiophysJ paper by Alan Ianeselli, Joe Howard and @MarkGerstein . A Molecular Dynamics algorithm to rapidly compute protein folding pathways and identify folding intermediates for targeted drug discovery! doi.org/10.1016/j.bpj.2025.0…
New @PhysRevA paper by Gaoyuan Wang, Jonathan Warrell, Prashant Emani and @MarkGerstein ! Check out our new model QVAE, a fully quantum variational autoencoder with latent regularization: journals.aps.org/pra/abstrac…
New @PLOSONE paper by Xiao Zhou, Sanchita Kedia, Ran Meng, and @MarkGerstein. Our deep learning framework analyzes fMRI scans for early Alzheimer's Disease detection, achieving 92.8% accuracy with a focus on model interpretability: doi.org/10.1371/journal.pone…
New @CellRepPhysSci paper by Gaoyuan Wang, Jonathan Warrell, Suchen Zheng and @MarkGerstein! Check out our GP-GNN framework that jointly learns graph structure and node representation, with the two optimization procedures dynamically informing each other: cell.com/cell-reports-physic…














