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Pharmacogenomics: Identification of New Drug Targets

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Interpreting Omics Data Analysis with Large Language Models for Disease Target and Drug Discovery.

Zixi Xu, Weihang Chen, Wuyu Ren

    Biorxiv : the Preprint Server for Biology
    |May 18, 2026
    PubMed
    Summary

    This study introduces a novel framework integrating large language models (LLMs) with omics data for biomedical target discovery. It enhances disease mechanism retrieval and prioritizes drug targets with reliable, auditable evidence.

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    Area of Science:

    • Biomedical Informatics
    • Computational Biology
    • Drug Discovery

    Background:

    • Synthesizing prior knowledge is crucial for interpreting omics data in disease target identification.
    • Large language models (LLMs) can retrieve disease mechanisms but lack specificity for prioritization without quantitative evidence.

    Purpose of the Study:

    • To develop a provenance-aware Text-to-Target framework coupling LLM retrieval with omics data analysis.
    • To enable reliable and auditable disease target and drug prioritization.

    Main Methods:

    • Schema-constrained multi-model LLM retrieval integrated with numeric omics data analysis.
    • A modality-aware fusion step partitions candidates (anchors, hubs, nodes) for staged hypothesis generation.
    • Topology constraints and provenance closure ensure end-to-end auditability.

    Main Results:

    • In pancreatic ductal adenocarcinoma (PDAC), the framework identified a 75-gene universe and 23 strategies with DepMap support.
    • For Alzheimer's disease (AD), it yielded a 34-gene universe and 14 strategies, showing significant enrichment with CRISPRbrain registry data.
    • Final strategies maintained full provenance, linking retrieval artifacts to validation outputs.

    Conclusions:

    • The Text-to-Target framework offers a transferable architecture for omics-driven biological discovery.
    • It balances LLM's broad mechanistic search with omics data's quantitative constraints for interpretable prioritization.
    • The approach provides a reproducible basis for dual-disease target prioritization and continuous evidence-refresh loops.