This federal Project Grant award of $142,065 from the National Human Genome Research Institute (CFDA 93.172 - Human Genome Research) supports the development of computational frameworks to model the impact of external signals on a cell's internal transcription factor activity. The key products and services to be delivered under this award include: Computational methods to more accurately deconvolve cell type fractions and gene expression from bulk RNA-seq and spatial transcriptomics data. A...
This Project Grant award from the National Human Genome Research Institute (CFDA 93.172 - Human Genome Research) provides $361,069 to The Johns Hopkins University to develop new analytical methods and software tools for unsupervised analysis of multi-sample single cell genomic data. The project aims to address three key challenges: inferring temporal progression of sample phenotype from cross-sectional data, integrating data across samples and modalities, and enabling comparative analysis of...
The National Human Genome Research Institute (NHGRI) awarded a $504,949 Project Grant (CFDA 93.172 - Human Genome Research) to the University of Washington to develop advanced machine learning models that integrate Hi-C chromatin contact data with other genomics data types. The project aims to: 1) Extend the SAGITTARIUS model to analyze Hi-C time-series data and study the dynamic interplay between gene expression and chromatin structure during biological processes; 2) Develop a graph-based...
The National Human Genome Research Institute (NHGRI) awarded a $259,446 Project Grant under the Human Genome Research program (CFDA 93.172) to the University of Washington for a 2-year research project. The project aims to develop semi-supervised machine learning integration tools that can predict undercharacterized single-cell genomic and proteomic profiles, such as chromatin accessibility, spatial locations, and proteomics, from known measurements like gene expression. Key objectives include...
This federal Project Grant award of $536,250.00, provided by the National Institute of General Medical Sciences (NIGMS) under the Biomedical Research and Research Training program (CFDA 93.859), aims to develop computational methods that can better identify and analyze rare cell populations from single-cell transcriptomic data. The project, titled "A Knowledge-Guided Analysis Approach to Recovering Rare Signals from Single-Cell Transcriptomic Data," will be conducted by the Sanford...
This Project Grant award of $129,708 from the National Institute of General Medical Sciences (NIGMS) Biomedical Research and Research Training program (CFDA 93.859) supports the development of advanced computational tools for analyzing single-cell data. The goal is to provide new methods for modeling and interpreting complex cellular processes like differentiation and disease progression, with a focus on applications in hematopoiesis, tissue regeneration, and large-scale perturbation studies....
This Project Grant award, valued at $491,366, was issued by the National Human Genome Research Institute (NHGRI) under the Human Genome Research federal grant program (CFDA 93.172). The award is focused on developing new algorithms to robustly map disease-associated genetic variants to critical target genes, particularly in underrepresented populations. Key objectives include: Improving the ability to identify disease-critical genes in non-European populations where data is limited. Developing...
This Project Grant award from the National Institute of General Medical Sciences (NIGMS), under the Biomedical Research and Research Training program (CFDA 93.859), provides $376,250.00 to the New Jersey Institute of Technology (NJIT) to develop novel computational and statistical methods for analyzing single-cell omics data. The key objectives of this 5-year project include: (1) developing and optimizing large-scale foundation models for single-cell omics data, (2) creating computational...
The National Human Genome Research Institute (NHGRI), under the Human Genome Research Federal Grant Program (CFDA 93.172), awarded a $1,341,011 Project Grant to the University of Pittsburgh to develop a novel computational framework that integrates deep learning and causal discovery algorithms. The goal is to unveil individualized cell-cell communication networks underlying tissue environment heterogeneity across normal and disease conditions. The framework will leverage single-cell...
The National Human Genome Research Institute (NHGRI), under the Human Genome Research Federal Grant Program (CFDA 93.172), awarded a $993,446 Project Grant to the Sloan-Kettering Institute for Cancer Research. The 5-year grant, effective May 1, 2025, will support the development of statistical and machine learning approaches to decode the regulatory activity underlying disease variants, link variants to genes in relevant cell types, and identify disease-critical gene programs and genomic...