Using advanced machine learning to analyze single-cell genomic data
Official title: Deep Learning Methods to Integrate Biological Information for Analysis of Single-cell RNAseq Data
Funded at New Jersey Institute of Technology · NIH-10974530 · Data updated 2026-11-12
This study is working on new computer techniques to better understand genetic information from individual cells, which could help doctors learn more about diseases and create more personalized treatments for patients.
Quick facts
| Grant type | R15 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | New Jersey Institute of Technology NIH-funded |
| Lab location | 1 site (Newark, United States) |
| Project start | September 22, 2021 |
| Project end | August 31, 2027 |
| Funding year | 2024 |
| Award (this year) | $450,034 |
| NIH project number | 2R15HG012087-02 |
| Project ID | NIH-10974530 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients with conditions that involve complex genomic alterations, such as various cancers or genetic disorders.
✕Less likely to help
Patients with conditions that do not involve genomic analysis or those who are not undergoing treatments related to genomic data may not benefit from this research.
What this research studies
This research focuses on developing innovative machine learning methods to analyze complex genomic data, specifically single-cell RNA sequencing (scRNA-seq) and spatial genomic data. By integrating biological information with advanced statistical techniques, the project aims to enhance the accuracy and interpretability of genomic analyses. The methods being developed will address significant computational challenges and are designed to improve the understanding of biological processes at the single-cell level. Patients may benefit from these advancements as they could lead to better insights into diseases and more personalized treatment approaches.
Why it matters
Potential benefit: If successful, this research could lead to improved understanding of diseases at the cellular level, potentially resulting in more effective and personalized treatments for patients.
How similar studies have gone: Other research has shown success in using machine learning approaches for genomic data analysis, indicating that this project builds on established methodologies.
Where this research is happening
-
New Jersey Institute of Technology
Newark, United States Active
Researchers
- Principal investigator: Wei, Zhi — New Jersey Institute of Technology
- Study coordinator: Wei, Zhi
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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.