We are funded by the National Institutes of Health (NIH) to develop new and novel computational methods for the analysis of big biomedical data. Below is a sample of our active research grants. An overview of our work supported by these grants and others can be found on our research page.
Artificial Intelligence Strategies for Alzheimer’s Disease Research (R01 AG066833, PI – Moore)
The goal of this research program is to develop automated machine learning (AutoML) methods for the multimodal data analysis of Alzheimer’s disease. Data types include genetics, genomics, and imaging. A central goal is to develop an integrated knowledgebase to inform the AutoML methods. The ultimate goal is to identify new drug targets for treating Alzheimer’s disease. We are working closely with the NIA-funded Alzheimer’s Disease Research Centers (ADRCs) to share ideas, discuss solutions, and to collaborate. This project is a collaboration with Drs. Marylyn Ritchie and Li Shen from the University of Pennsylvania who serve as multiple PIs on the grant.
Here is an AutoML paper we published in Bioinformatics in 2020 which provides some background for the project.
Bioinformatics Strategies for Genome-Wide Association Studies (R01 LM010098, PI – Moore)
The goal of this research program is to develop methods for incorporating expert knowledge about functional genomics annotations from sources such as ENCODE into the genetic analysis of complex diseases using computational methods such as AutoML. Do annotations help identify genetic risk factors? What is the best way to incorporate knowledge? How can knowledge inform interpretation of computational models? The project is a collaboration with Dr. Folkert Asselbergs from the University of Utrecht and Dr. Scott Williams from Case-Western Reserve University who both serve as multiple PIs on the grant.
Here is a recent paper published in IEEE/ACM Transactions on Bioinformatics and Computational Biology illustrating our computational approach.
Informatics Algorithms for the Genomic Analysis of Brain Imaging Data (Ro1 LM013463, MPI – Moore)
The goal of this project is develop computational methods, including machine learning, to improve the genetic analysis of brain imaging phenotypes for understanding Alzheimer’s disease. This project addresses the unprecedented scale and complexity of the imaging genetic data sets and the lack of intermediate-level omics data (e.g. transcriptomics) to capture the molecular effects linking genetics to brain phenotypes. This projects is a collaboration with Dr. Li Shen from the University of Pennsylvania who serves as the contact PI for the grant.
Here is a paper we published in Bioinformatics in 2020 which illustrates the type of work this grant funds.