Project Grant W911NF2510149

This National Science Foundation (NSF) Project Grant award of $384,214 to Northeastern University will develop statistical inference methods that leverage advanced techniques like reinforcement learning, Bayesian statistics, and machine learning. The project aims to 1) incorporate expert knowledge into the modeling process without requiring expert oversight, and 2) systematize data collection for accurate inference of complex systems and processes. The proposed approaches will be applied in...

This $138,428 project grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) will support the development of computationally tractable structured hierarchical models to identify complex genetic associations hidden from current methods. The grantee, J. David Gladstone Institutes, will analyze genomic and clinical trait data using machine learning to build predictive models for individualized disease risk assessment and personalized...

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) is providing $290,325 to the University of Illinois to develop advanced deep learning methods for estimating cell-type resolution and spatially-resolved gene regulatory networks from spatial transcriptomics data. The objectives are to: 1) Create machine learning models that can integrate regulatory network components and distinguish cell types...

The National Human Genome Research Institute (NHGRI) awarded the University of Wisconsin System a $407,645 Project Grant under the "Human Genome Research" (CFDA 93.172) program to develop statistical methods for constructing gene co-expression networks from population-scale single-cell RNA sequencing (scRNA-seq) data. The two-year project aims to: 1) develop a de-biasing approach to estimate gene-gene correlations from scRNA-seq data, accounting for technical limitations like low...

This five-year, $997,720 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop new mathematical models and algorithms for genetic data analysis using deep learning techniques. Funded through the NSF's statutory mission to support basic research, the award will support research at Cornell University from July 2022 through June 2027. Specifically, the principal investigator will create novel deep generative models to replace...

This Project Grant from the National Science Foundation's National Center for Science and Engineering Statistics will fund the development of Bayesian statistical and machine learning methodologies tailored for complex survey and census data. Awarded $743,050 under the Social, Behavioral, and Economic Sciences program, the grant will support research at the University of Missouri from September 2022 through August 2025. The research aims to advance computational efficiency and expand...

This $685,925 National Science Foundation Project Grant supports research at Georgia Tech Research Corporation to develop novel graph neural networks and machine learning strategies to understand, predict, and design allosteric transcription factors. The NSF Division of Chemistry and Divisions of Chemical, Bioengineering, Environmental and Transport Systems and Information and Intelligent Systems are jointly supporting this work under the Mathematical and Physical Sciences program (CFDA 47.049)....

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 $324,118 National Science Foundation project grant supports research to decipher RNA-based regulatory logic through interpretable machine learning models. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the three-year award to New York University will develop and apply interpretable neural networks to massive parallel reporter assay datasets. Specifically, the models aim to elucidate the splicing code that determines exon skipping and the 5'UTR code regulating...

This three-year, $674,542 National Science Foundation project grant supports research at the University of California Santa Cruz to develop Bayesian statistical and machine learning methods for analyzing complex survey data from the federal statistical system. The grant falls under the NSF's Social, Behavioral, and Economic Sciences program (CFDA 47.075), which promotes basic research and education in these fields. Specifically, the investigators will extend existing models using data...