scFAN: Predicting transcription factor binding in single cells through deep learning
scFAN is a deep learning model that predicts the probability of a TF binding at a given genomic region, with inputs of ATAC-seq, DNA sequence, and DNA mapability data from that region.
Fu, Laiyi, Lihua Zhang, Emmanuel Dollinger, Qinke Peng, Qing Nie, and Xiaohui Xie. "Predicting transcription factor binding in single cells through deep learning." Science Advances 6, no. 51 (2020): eaba9031.
10.1126/sciadv.aba9031
BRAINWORKS
BRAINWORKS is a web platform, developed at the NIH, that structures the scientific literature from PubMed and NIH RePORTER as a dynamic and interactive knowledge graph.
Announcements:
Enhancing Semantic Interoperability in Environmental Health Sciences Research
Enhancing Semantic Interoperability in Environmental Health Sciences Research
NIEHS/NASEM Workshop: Geospatial Technologies Inform Precision Environmental Health Decisions
The NIEHS and the National Academies of Sciences, Engineering, and Medicine invite you to attend a virtual workshop titled "Leveraging Advances in Remote Geospatial Technologies to Inform Precision Environmental Health Decisions."
When: April 14-15, 2021
Request for Information (RFI): Inviting Comments and Suggestions to Advance and Strengthen Racial Equity, Diversity, and Inclusion in the Biomedical Research Workforce and Advance Health Disparities and Health Equity Research
This Notice is a Request for Information (RFI) inviting feedback on the approaches NIH can take to advance racial equity, diversity, and inclusion within all facets of the biomedical research workforce, and expand research to eliminate or lessen health disparities and inequities.
Review of this entire RFI notice is encouraged to ensure a comprehensive response is prepared and to have a full understanding of how your response will be utilized.
16th Annual Multicell Virtual-Tissue Modeling Online Summer School and Hackathon
National Science Foundation - FY22/23 Emerging Frontiers in Research Innovation Solicitation 21-615
Course on multiscale modeling of brain circuits using NetPyNE/NEURON
We are pleased to announce the first NIBIB-funded “Course on multiscale modeling of brain circuits using NetPyNE/NEURON”.