Monday, June 30, 2025 6/30/2025

Deep Learning Models for Metabolomics Analysis

Award Number: R35GM148219
ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 04/01/2023
PERIOD OF PERFORMANCE END DATE: 03/31/2028

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Deep Learning Models for Metabolomics Analysis - PROJECT SUMMARY Untargeted metabolomics using tandem mass spectrometry (MS) have attained substantial success in the discovery of biomarkers and advancing our understanding of cellular metabolism. Despite this success, only a small fraction of measured spectra can currently be annotated (assigned a chemical identity). This bottleneck can be attributed to the limitations of current annotation tools that have not yet exploited advances in deep learning and available data modalities (spectra, peaks, molecules, and fragments). The goal of this application is to advance the interpretation of spectra collected through untargeted metabolomics. We focus on annotating data collected through liquid or gas chromatology followed by MS, or MS/MS, as these three tandem technologies have become dominant technologies. Over the next five years, the plan is to harness deep learning to address three problems: 1) annotation, 2) translation between spectra measured under different instrument settings, and 3) explainable models for annotation, where explainability arises from connecting peaks to their respective molecular fragments. The Hassoun lab has extensive, relevant deep learning experience to effectively tackle these problems. The Lab also has experience in dealing with the nuances of metabolomics datasets. The Lab recently developed a novel deep learning annotation model that achieves 41% and 30% performance improvement over multi-layer neural networks and graph neural networks, respectively. Additionally, our lab has developed an ontology- traversal algorithm that yields correct-by-construction molecular substructures that can be assigned to peaks, thus giving rise to datasets that can be used to train explainable annotation models. The Significance of this research is that it addresses fundamental barriers that hinder developing deep learning annotation models. Our models and datasets will be released on GitHub to benefit biological and biomedical applications and metabolomics research. Because of their expected high accuracy and explainability, the models will expedite the interpretation of experiments, improve our understanding of cellular metabolism, and facilitate data sharing among labs. The innovation lies in maximally learn from data modalities and in creating models that exploit the learned representations. Further, the annotation and translation problems are formulated as a bidirectional mapping between domains, in contrast to current annotation models that assume unimodal mappings. These innovations are necessary to advance metabolomics research and they will open new research horizons in the field of metabolomics.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $370,308 )
2025	2025	TRUSTEES OF TUFTS COLLEGE	80 GEORGE ST	MEDFORD	MA	02155	MIDDLESEX	USA	Biomedical Research and Research Training	002	3	6/2/2025	NON-COMPETING CONTINUATION	$370,308
2025	2024	TRUSTEES OF TUFTS COLLEGE	169 HOLLAND ST	SOMERVILLE	MA	02144	MIDDLESEX	USA	Biomedical Research and Research Training	000	2	11/11/2024	NON-COMPETING CONTINUATION	$0
2025	2024	TRUSTEES OF TUFTS COLLEGE	80 GEORGE ST	MEDFORD	MA	02155	MIDDLESEX	USA	Biomedical Research and Research Training	001	2	5/30/2025	SUPPLEMENT FOR EXPANSION	$0
														Subtotal = $370,308

Issue Date FY: 2024 ( Subtotal = $459,397 )
2024	2024	TRUSTEES OF TUFTS COLLEGE	169 HOLLAND ST	SOMERVILLE	MA	02144	MIDDLESEX	USA	Biomedical Research and Research Training	002	2	9/19/2024	SUPPLEMENT FOR EXPANSION	$62,824
2024	2024	TRUSTEES OF TUFTS COLLEGE	169 HOLLAND ST	SOMERVILLE	MA	02144	MIDDLESEX	USA	Biomedical Research and Research Training	001	2	6/18/2024	NON-COMPETING CONTINUATION	$39,658
2024	2024	TRUSTEES OF TUFTS COLLEGE	169 HOLLAND ST	SOMERVILLE	MA	02144	MIDDLESEX	USA	Biomedical Research and Research Training	000	2	3/18/2024	NON-COMPETING CONTINUATION	$356,915
														Subtotal = $459,397

Issue Date FY: 2023 ( Subtotal = $216,672 )
2023	2023	TRUSTEES OF TUFTS COLLEGE INC	169 HOLLAND ST	SOMERVILLE	MA	02144	MIDDLESEX	USA	Biomedical Research and Research Training	000	1	3/7/2023	NEW	$216,672
														Subtotal = $216,672

Grand Total All Awards = $1,046,377

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Deep Learning Models for Metabolomics Analysis

Award Number: R35GM148219

ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 04/01/2023

PERIOD OF PERFORMANCE END DATE: 03/31/2028

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