Collaborative Research: CISE-MSI: RPEP: III: celtSTEM Research Collaborative: Catapulting MSI Faculty and Students into Computational Research.

University Of Saint Thomas, Houston TX

Investigators

Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). As datasets have grown in size and complexity, the data they contain are essential for advancing scientific discoveries. Computational methods such as machine learning and data mining play a key role in facilitating the analysis of large datasets. However, the tools used to store and manage datasets and the tools used for machine learning are largely separate and treat data differently. Machine learning algorithms for data that has a network structure, in which a key feature of the data is relationships between items in the network, are also less well-developed compared to machine learning algorithms that treat items independently. This project will advance network-based machine learning algorithms, developing new approaches that are well-matched to common database technologies and apply them to solve foundational problems in biology including genome and proteome sequences. The project will also develop a long-term computational research initiative at the lead institution, which is a Minority Serving Institution (MSI), through working with a research-intensive organization along with industry and government partners, to implement strategies for preparing MSI students and faculty to excel in computational research. The machine learning methods portion of the project will scale neural computations to huge graphs using graph neural networks. Current approaches are limited because of problems scaling to very large graphs and effectively distributing computations across multiple machines. The approach focuses on leveraging relational database technologies, which are ideally situated to overcome these limitations due to the close link between graphs and relations and the rich set of tools they already possess for optimizing computation across large datasets. The tools developed will be used to help solve foundational problems in biology, focusing on two projects. The first involves machine learning-aided search of large protein databases for unique sequence patterns, developing new high-dimensional data encodings and graph-based algorithms to facilitate evolutionary, structural, functional, and ontological searches. The second involves analysis of metagenomic data to identify viruses (and variants) carried by mosquitos, developing novel graph encodings of mosquitos’ DNA and RNA along with machine learning-based analyses of them to analyze nucleic acid sequence data and expand the understanding of the viral collections carried by insects that interact closely with human populations. The research will be carried out in close collaboration between the lead university partners, who will develop coursework, team structures and practices, and mentoring approaches that provide a framework in which MSI students and faculty can gain both research skills and opportunities. This capacity-building work will be guided by a situated learning pedagogy and a socio-technical lens that emphasizes context and relationships between people and technology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.