Analysis of racial disparities in HCC by systems metabolomics. National Cancer Institute (NCI) U01CA185188 (PI: Ressom)
The goal of this project is to find and validate race-specific biomarkers for HCC and identify perturbed intercellular signaling pathways in HCC. The specific aims are to: (1) perform targeted analysis of metabolites in liver tissues and sera for identification of race-specific biomarkers for HCC; (2) identify aberrant pathways and network activities using systems metabolomics approaches; and (3) conduct validation and functional characterization of candidate biomarkers and aberrant pathways through in vitro and in vivo experiments.
Analysis of LC-MS data to identify peptide and glycan biomarkers for Hepatocellular Carcinoma. National Cancer Institute (NCI) R01CA143420 (PI: Ressom)
The goal of this project is to find and validate peptide and glycan biomarkers for early detection of hepatocellular carcinoma (HCC) in a high risk population of cirrhotic patients. The specific aims are to: (1) develop a Bayesian hierarchical model for alignment and normalization of liquid chromatography mass spectrometry (LC-MS) data; the model will be used for label-free quantification and comparison of peptides and glycans by LC-MS systems. (2) identify and validate an integrated set of candidate peptide and glycan biomarkers for early detection of hepatocellular carcinoma; a novel machine learning method will integrate peptide and glycan biomarkers to enhance their diagnostic capability.
Quantitative analysis of LC-MS data for peptide and glycan biomarker discovery.
National Institute of General Medicine (NIGMS) R01GM086746 (PI: Ressom)
The goal of this project is to develop computational methods for quantitative comparison of peptides and glycans in serum and plasma samples by label-free LC-ESI-MS and LC-MALDI-TOF methods. The specific aims are to: (1) develop a probabilistic-based mixture regression model for alignment of LC-ESI-MS data, (2) develop a clustering-based method for alignment of LC-MALDI-TOF data, (3) identify differentially abundant peptides and glycans between distinct biological groups, and (4) identify key pathways enriched by multiple candidate biomarkers.
Metabolomic and integromic approaches to identify fingerprints for early detection and treatment of liver cancer. NCI R21 CA153176 (PI: Ressom)
III-CXT-Small: Analysis of mass spectrometry data for biomarker discovery. National Science Foundation (NSF) IIS-0812246 (PI: Ressom)
Peptide biomarker discovery by mass spectrometry for early detection of liver cancer. NCI R21 CA130837 (PI: Ressom)
Novel analytical tools for biomarker discovery from mass spectrometry data.
Associate Membership to NCI's Early Detection Research Network (PI: Ressom)
Identification of serum biomarkers for early detection of liver cancer. Prevent Cancer Foundation (PI: Ressom)
Novel machine learning methods for analysis of MALDI-TOF mass spectrometry data.
NCI R03 CA119313 (PI: Ressom).
A pattern recognition system for identifying organic compounds from XMF EEMs. National Aeronautics and Space Administration (NASA) (PI: Ressom)
Integrated fluorocarbon microsensor system using catalytic modification.
NSF CBET-0428341 (PI: Wheeler, Co-PIs: Ressom and Pereira da Cunha)
Acquisition of an x-band satellite data groundstation for regional multidisciplinary research. NSF EIA-0131889 (PI: Thomas, Co-PIs: Beard-Tisdale, Sader, and Ressom)
Biodiversity and ecosystems informatics. NSF OCE-0420393 (PI: Musavi, Co-PIs: Markowsky, Hinkle, Stefanidis, and Ressom)
Accurate DNA base calling.
NSF DBI-0090738 (PI: Musavi, Co-PIs: Van Beneden, Ressom, and Domnisoru)
Analysis of gene expression data from Rhizoctonia solani using a novel clustering algorithm. University of Maine Faculty Research Fund (PI: Ressom, Co-PI: Tavantzis)
Seagrass health estimation using neural networks.
Maine Space Grant Consortium (PI: Ressom)
Neural network-based estimation of chlorophyll-a concentration in coastal waters. NASA and the Maine Space Grant Consortium (PI: Ressom)