NLM DIR Seminar Schedule
UPCOMING SEMINARS
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July 3, 2025 Matthew Diller
Using Ontologies to Make Knowledge Computable -
July 15, 2025 Noam Rotenberg
Cell phenotypes in the biomedical literature: a systematic analysis and the NLM CellLink text mining corpus
RECENT SEMINARS
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July 3, 2025 Matthew Diller
Using Ontologies to Make Knowledge Computable -
July 1, 2025 Yoshitaka Inoue
Graph-Aware Interpretable Drug Response Prediction and LLM-Driven Multi-Agent Drug-Target Interaction Prediction -
June 10, 2025 Aleksandra Foerster
Interactions at pre-bonding distances and bond formation for open p-shell atoms: a step toward biomolecular interaction modeling using electrostatics -
June 3, 2025 MG Hirsch
Interactions among subclones and immunity controls melanoma progression -
May 29, 2025 Harutyun Sahakyan
In silico evolution of globular protein folds from random sequences
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.