NLM DIR Seminar Schedule
UPCOMING SEMINARS
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April 8, 2025 Jaya Srivastava
Leveraging a deep learning model to assess the impact of regulatory variants on traits and diseases -
April 15, 2025 Pascal Mutz
TBD -
April 18, 2025 Valentina Boeva, Department of Computer Science, ETH Zurich
Decoding tumor heterogeneity: computational methods for scRNA-seq and spatial omics -
April 22, 2025 Stanley Liang
TBD -
April 29, 2025 MG Hirsch
TBD
RECENT SEMINARS
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April 1, 2025 Roman Kogay
Horizontal transfer of bacterial operons into eukaryote genomes -
March 25, 2025 Yifan Yang
Adversarial Manipulation and Data Memorization in Large Language Models for Medicine -
March 11, 2025 Sofya Garushyants
Tmn – bacterial anti-phage defense system -
March 4, 2025 Sanasar Babajanyan
Evolution of antivirus defense in prokaryotes depending on the environmental virus load -
Feb. 25, 2025 Zhizheng Wang
GeneAgent: Self-verification Language Agent for Gene Set Analysis using Domain Databases
Scheduled Seminars on Jan. 17, 2025
In-person: Building 38A/B2N14 NCBI Library or Meeting Link
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
Large language models (LLMs) pretrained on massive data have shown their power as foundation models for pervasive tasks in natural language understanding and beyond. This inspired us to develop large cellular models (LCMs) to decipher the transcriptomic language of cells. We have developed LCMs for single-cell transcriptomics toward this goal using two approaches, which produced the two large models scFoundation and scMulan. With pretraining on tens of millions of human scRNA-seq data covering almost all known cell types and states, the models have shown ability of capturing complex context relations among gene expressions and meta attributes of cells. Experiments showed that the pretrained model can achieve state-of-the-art performances in zero-shot manner or with light fine-tuning on a diverse array of single-cell analysis tasks such as data enhancement, drug-response prediction at tissue and single-cell levels, single-cell perturbation prediction, cell type annotation, gene module inference and conditional cell generation.