Fwd: Quantum for Life Sciences

for a title, how about : " Applications of Quantum Computing in Biomedical Data Science " For a short abstract, how about : " My talk will cover various applications of quantum computing (QC) in biomedical data science. In particular, I will discuss : (1) How can we use QC for ML in defined biomedical problems. (2) The use of quantum representations for achieving biomedical privacy. (3) Fitting QC into larger biomedical workflows. The talk will include material from the following papers: https://arxiv.org/abs/2509.12465 https://journals.aps.org/pra/abstract/10.1103/PhysRevA.111.042416 https://academic.oup.com/bioinformatics/article/39/1/btac789/6881079 https://www.nature.com/articles/s41592-020-01004-3 " == i0ismb26

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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