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: https://t.co/dz0P7ddEWP https://t.co/WT4QDUGJl4

📚 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! https://t.co/E9Xu5sOqQn

3/3 The test yielded a p-value of 4.91 × 10⁻⁸, which is far below the conventional significance threshold of 0.05. This indicates a statistically significant deviation in personality type distribution within the lab.

2/3 In contrast, within the Gerstein Lab, there are 26 Analysts, 20 Diplomats, 6 Sentinels, and 4 Explorers. A chi-square goodness-of-fit test was conducted to evaluate whether the MBTI distribution in the lab significantly differs from that of the general population.

1/3 Based on a survey of 22,678,145 individuals in the United States, the distribution of MBTI personality types in the general population is as follows: Analysts account for 16.72%, Diplomats for 44.43%, Sentinels for 23.91%, and Explorers for 14.93%.

🧠 At our recent Gerstein Lab roster meeting, we took a detour into… personality science! Turns out we’re INT Central 🧪 📌 70% Introverts 📌 83% Intuitives 📌 57% Thinkers Analysts (INTP, INTJ) dominate, far more than the U.S. baseline. #MBTI #INTP #INTJ

@_YunyangLI @MarkGerstein @MSFTResearch You can read more here:

@_YunyangLI @MarkGerstein @MSFTResearch 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.

@_YunyangLI @MarkGerstein @MSFTResearch 3/4 🗂️ First release of PubChemQH—50 k large-molecule Hamiltonians (40–100 atoms) for robust benchmarking, generated by 128 GPUs for one month of processing, which motivates a scaling challenge which we refer to as SAD.

@_YunyangLI @MarkGerstein @MSFTResearch 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 https://t.co/6FIUus3Qhh

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! https://t.co/waxhx6qqo5

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: https://t.co/EwYbtwVAHd

I am very excited to share that our(@GersteinLab) paper was recently published in Cell! 🎉☺️ https://t.co/NMfc4JwrhQ had a lot of fun creating two versions of covert art for our paper: which one do you like more?

🚨Alert: Funded postdoctoral position available in our lab!

Excited to share our new paper in @CellCellPress on Digital Phenotyping from Wearable Biosensors using AI to characterize Psychiatric Disorders & identify Genetic Associations (led by @JasonJLiu & @beaborsari) https://t.co/8noL0y9wqp

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: https://t.co/Ro5WzyJUyZ

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: https://t.co/E3S9yIlXzq