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A sign that society is falling.

Thats an eye opener statistics. Is there data of active users for Hive and inleo?

In sum, we provide a generalizable and accurate model for transcription together with catalogues of gene regulation and transcription factor interactions, all with cell type specificity.

transcription factor–transcription factor interaction that explains the functional significance of a leukaemia risk predisposing germline mutation.

In fetal erythroblasts, we identified distal (greater than 1 Mbp) regulatory regions that were missed by previous models, and, in B cells, we identified a lymphocyte-specific

and found that it outperforms current models in predicting lentivirus-based massively parallel reporter assay readout.

We evaluated its performance in prediction of regulatory activity, inference of regulatory elements and regulators, and identification of physical interactions between transcription factors

and uncovers universal and cell-type-specific transcription factor interaction networks.

GET also shows remarkable adaptability across new sequencing platforms and assays, enabling regulatory inference across a broad range of cell types and conditions,

Relying exclusively on chromatin accessibility data and sequence information, GET achieves experimental-level accuracy in predicting gene expression even in previously unseen cell types.

Here we introduce GET (general expression transformer), an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types.

Computational models of transcription lack generalizability to accurately extrapolate to unseen cell types and conditions.

Transcriptional regulation, which involves a complex interplay between regulatory sequences and proteins, directs all biological processes.

David M. Owens Teresa Palomero, Eric P. Xing (Mohamed bin Zayed University of Artificial Intelligence and Carnegie Mellon University), and Raul Rabadan.

New York, NY), Jiayu Su, Romella Sagatelian, Adolfo A. Ferrando (Columbia and Regeneron, Tarrytown, NY), Alberto Ciccia, Yanyan Lan (Tsinghua University, Beijing, China),

AI could reveal “dark matter” in genome