TITLE: AI Methods for Biomedicine: Using Classical, DL & Quantum
Approaches to Address Brain Disorders
ABSTRACT:
This talk surveys a spectrum of AI approaches for biomedicine—from
classical methods to current deep-learning techniques and emerging
extensions involving agents and quantum computing. It focuses on
modeling gene-regulatory and cell-communication networks to interpret
brain disease, describing how cell-type–specific networks can be
constructed from single-cell QTLs and co-expression data and then
embedded into deep-learning frameworks to predict disease from
genotype, prioritize pathways and genes, and model perturbations for
drug-target discovery. The talk then highlights new LLM applications,
including automatic code generation and benchmarking (Biocoder),
collaborative multi-expert clinical reasoning (MedAgents), and
end-to-end single-cell analysis workflows (CellForge). Finally, it
addresses genomic-privacy challenges—first through classical
approaches such as homomorphic encryption and hidden Markov models,
and then through a quantum-computing framework that enables
distributed genomic analysis without exposing individual-level data.
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i0mad25
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