NLM DIR Seminar Schedule
UPCOMING SEMINARS
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May 12, 2026 John Bridgers
A bi-partition function algorithm to evaluate inferred subclonal structures in single-cell sequencing data -
May 14, 2026 Brandon Colelough
TBD -
May 19, 2026 Leann Lindsey
Are Genomic Language Models Learning? Insights from Tokenization Analysis and Prophage Detection in Bacterial Genomes -
May 26, 2026 Harutyun Saakyan
TBD -
May 27, 2026 Brian Abraham
Cis-Regulatory Organization and Transcription Factor Control of Cell Identity and Disease
RECENT SEMINARS
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May 5, 2026 Benjamin Hou
Machine Learning for Craniofacial Malocclusion Prediction -
April 28, 2026 Niccolo Marini
From Unimodal Datasets to Multimodal Foundation Models: Synthetic Clinical Notes for Dermatology AI -
April 21, 2026 Yoshitaka Inoue
Drug Response Prediction: Generalization using Graph Neural Networks & Reasoning over Predictions using LLMs -
April 16, 2026 Matthew Diller
Analyzing Similarity in Common Data Elements in the NIH CDE Repository via Semantic Clustering -
April 7, 2026 Henry Secaira Morocho
Toward a systematic method of database enrichment for reference-based metagenomics
Scheduled Seminars on March 10, 2026
In-person: Building 38A/B2N14 NCBI Library or Meeting Link
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
Gene set analysis plays a central role in genomic discovery, yet conventional approaches often yield results that are difficult to interpret, lack transparency, and fail to account for biological context. As datasets grow in scale and complexity, the gap between statistical output and actionable biological insight continues to widen. In this presentation, I will introduce a new paradigm that integrates large language models into gene set analysis to bridge this gap. I will present three complementary frameworks. GeneAgent mitigates hallucination and enhances explanation robustness, enabling more reliable biological interpretation. Gene-R1 fine-tunes small language models to support privacy-preserving analysis and local deployment, making advanced genomic reasoning accessible in secure environments. cGSA introduces contextual awareness to functional prioritization, improving biological relevance and interpretability.