This federal Project Grant award from the National Institutes of Health (NIH) Office of the Director under the Trans-NIH Research Support program (CFDA 93.310) provides $286,326 to Carnegie Mellon University (CMU) to develop new machine learning methods for integrative analysis and interpretation of spatial transcriptome datasets from multiple NIH Common Fund programs. The key objectives are to: 1) Create a scalable, platform-agnostic framework using pre-trained single-cell RNA-seq models to...
The University of Illinois has been awarded a $436,150 Project Grant from the National Human Genome Research Institute (CFDA 93.172 - Human Genome Research) to develop integrated experimental and statistical tools for ultra-high-throughput spatial transcriptomics. The grant aims to build a next-generation imaging-based single cell transcriptomics platform that can profile millions of cells in tissue volumes over 100 sq mm within a day, enabling large-scale comparative studies and genome research...
This three-year, $2.3 million project grant from the Department of Health and Human Services will support the development of deep learning and imaging-based methods to map biological networks at the single-cell level. Funded under the National Institute of General Medical Sciences' Biomedical Research and Research Training program, the California Institute of Technology will use large-scale image annotation, machine learning techniques, and cloud computing to solve key challenges in spatial...
This $359,490 federal Project Grant award from the National Institute of General Medical Sciences (NIGMS) Biomedical Research and Research Training Program (CFDA 93.859) will support the development of a suite of deep learning methods for "Single-Cell Genetics". The key products and services to be delivered under this 5-year award, which runs from May 1, 2025 to Feb 28, 2030, include: A novel deep learning model to predict gene expression from DNA sequences, enabling comprehensive...
This $2.757 million three-year Project Grant from the National Institutes of Health's Trans-NIH Research Support program will develop a computational framework for analyzing spatially-resolved transcriptomic datasets. The Broad Institute will integrate machine learning techniques such as representation learning, causal inference, image inpainting, and optimal transport to identify biological mechanisms underlying spatial processes in tissue contexts. Specifically, the framework will discern...
This federal Project Grant award of $338,538, provided by the National Institute of Environmental Health Sciences (NIEHS) under the Medical Library Assistance (CFDA 93.879) program, supports research to develop a novel deep learning framework called DeepGene. The goal is to create an unsupervised computational algorithm that can synthesize disparate and large datasets of the mouse brain into the next generation of reference anatomical parcellation/atlas. Specifically, the project aims to: 1)...
This Project Grant award from the National Institute of General Medical Sciences (NIGMS) Biomedical Research and Research Training Program (CFDA 93.859) provides $363,384 to Duke University to develop a suite of computational tools to address key challenges in analyzing in situ spatial transcriptomics data. The project aims to: Develop a framework for optimized cell segmentation that integrates RNA spatial location information with imaging data, using the latest segmentation algorithms. The...
This Project Grant award from the National Institutes of Health (NIH) Office of the Director, under the Trans-NIH Research Support program (CFDA 93.310), provides $315,060 to The Broad Institute, Inc. to develop a cell-type specific atlas of transcription factor-regulatory element connectivity across human tissues. The project aims to integrate single-cell and single-nucleus ATAC-seq data from the GTEx and HuBMAP Common Fund initiatives, and deploy deep learning methods to predict...
The Trustees of Columbia University in the City of New York received a $435,475 Project Grant from the National Human Genome Research Institute (NHGRI) under the "Human Genome Research" (CFDA 93.172) program. The grant funds the development of novel machine learning methods for analyzing spatial transcriptomic data without the need for paired single-cell data. The proposed computational toolbox will enable the characterization of diverse cell states and their spatial dynamics through...
This federal Project Grant award, provided by the National Institute of General Medical Sciences (NIGMS) under CFDA Program 93.859 (Biomedical Research and Research Training), will support the development of innovative computational models to leverage spatially resolved single-cell transcriptomics data. The $198,750 award to the University of Pittsburgh aims to create novel geometric deep learning models that can identify tissue structures and pathologies, and provide the underlying spatial...