Using deep learning to improve the analysis of metabolites in biological samples
Official title: Deep Learning Models for Metabolomics Analysis
Funded at Tufts University Medford · NIH-11057615 · Data updated 2026-11-12
This study is working on using smart computer techniques to better identify chemicals in biological samples, which can help find new markers for diseases and improve our understanding of how our bodies work.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Tufts University Medford NIH-funded |
| Lab location | 1 site (Boston, UNITED STATES) |
| Project start | April 1, 2023 |
| Project end | March 31, 2028 |
| Funding year | 2025 |
| Award (this year) | $370,308 |
| NIH project number | 5R35GM148219-03 |
| Project ID | NIH-11057615 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals undergoing metabolic profiling for conditions such as metabolic disorders, cancer, or other diseases where biomarker discovery is critical.
✕Less likely to help
Patients who are not undergoing any form of metabolic analysis or do not have conditions related to metabolic disorders may not benefit from this research.
What this research studies
This research focuses on enhancing the annotation of metabolites identified through untargeted metabolomics using advanced deep learning techniques. By leveraging tandem mass spectrometry data, the project aims to improve the identification of chemical compounds in biological samples, which is crucial for biomarker discovery. The researchers will develop models that not only annotate spectra but also translate data from different instrument settings and provide explanations for their predictions. This approach could significantly advance our understanding of cellular metabolism and improve the accuracy of metabolomics analyses.
Why it matters
Potential benefit: If successful, this research could lead to more accurate identification of biomarkers, which may improve disease diagnosis and treatment.
How similar studies have gone: Other research has shown promise in using deep learning for metabolomics, indicating that this approach could lead to significant advancements in the field.
Where this research is happening
-
Tufts University Medford
Boston, United States Active
Researchers
- Principal investigator: Hassoun, Soha — Tufts University Medford
- Study coordinator: Hassoun, Soha
Related research
Other NIH-funded projects on the same topics.
-
Integrative histo-genomic analysis to guide cancer risk and targeted therapies with interpretable AI
Researchers are developing an advanced AI-powered approach to map genetic and cellular patterns across breast and cervical cancers to help tailor treatments.
-
Predicting how antimalarial drug resistance spreads with AI
A powerful AI model will be built to forecast how resistance to antimalarial drugs might spread in Africa and beyond, helping clinicians and public health teams stay ahead of outbreaks.
-
Newborn screening: using gene and metabolite data to refine true vs false positives
A study using genetic and metabolomic data from newborn blood spots to better distinguish real metabolic disorders from false alarms.
-
Self-supervised deep learning for biological sequences
A deep learning project uses self-supervised methods to better understand biological sequences and related data, with the goal of advancing biology and medicine.
-
Mobile phone imaging and AI to screen oral cancers in low-resource settings
A low-cost mobile phone device with artificial intelligence is being developed to help frontline health workers screen for oral cancer early in low-resource areas.
-
Linking Genetic Variants to Real-Life Health Effects with Smart AI
A new AI-powered approach will predict how genetic differences affect health across multiple levels to help doctors understand diseases and tailor care.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.