Abstract for Oxford talk (eur26+ox)

TITLE: AI in Structural Bioinformatics: ABSTRACT : This talk covers AI methods in structural bioinformatics, with a focus on modeling protein flexibility and disorder. It introduces DreamFold, an AI "world model" that learns folding pathways in latent space, replacing a slower classical sampling approach (discard-and-restart). It presents machine-learning improvements to Kohn-Sham Hamiltonian estimation for faster DFT calculations on larger molecules. It also shows that ensembles of sequence-based deep learning models outperform individual predictors and 3D docking for drug screening. Finally, it addresses protein aggregation in disease (e.g., AD) via liquid-liquid phase separation (LLPS), using LLM embeddings and graph neural networks to predict LLPS-prone regions, intrinsically disordered regions (IDRs), and the effects of specific mutations.

Fwd: Mark Gerstein's talk at the CRG (July 21st 2026)

TITLE: Topics in Neurogenomics: Using AI to Study Endophenotypes & then Having it Take Over ABSTRACT: This talk explores the use of AI to study and eventually drive neurogenomics research. It covers linking wearable-derived "digital phenotypes" to genotype for conditions like ADHD, showing this approach uncovers SNPs missed by traditional case-control GWAS. It then examines single-cell data (e.g., PsychENCODE's 388-brain dataset) to build cell-type-specific regulatory and cell-to-cell communication networks, integrating them into deep learning models that predict disease from genotype and suggest drug targets. Finally, it turns to having AI do the science itself — automatically generating bioinformatics code, coordinating multi-agent LLM systems for unified reasoning, and considering the future risks of AI scientists in biomedicine. eur26+CRG

Abstract for my talk at UB (eur26+UB)

TITLE: The Changing Challenges in Human Genome Analysis ABSTRACT: This talk covers four areas: the EN-TEx resource for haplotype-aware genome analysis, classical genome annotation (pseudogene epigenetics across tissues), linking genetic variants to function via an allele-specific event catalog and a predictive transformer model, and new challenges in genomics -- privacy-preserving methods for handling population-scale disease genome data (homomorphic encryption, private federated learning, and leakage measurement). REFS: https://papers.gersteinlab.org/papers/NoisyFlow https://papers.gersteinlab.org/papers/HEPRS https://papers.gersteinlab.org/papers/epiPgene https://papers.gersteinlab.org/papers/Entex

Business Meeting Request 1191946

Dear Sir/Ma - I an Ericka, a Financial Consultant in Oman. I work with different private investors that can invest in your projects and ongoing project or a stratup. If you are intrested, i would provide you with more information. Looking forward to speaking with you Best Regards Ericka

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