Proteomics and machine learning: Leveraging domain knowledge for feature selection in a skeletal muscle tissue meta-analysis
Alireza Shahin-Shamsabadi, John Cappuccitti · 2024
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Description
Alireza Shahin-Shamsabadi and John Cappuccitti present a 2024 study on making machine learning work better for muscle proteomics. Proteomics measures thousands of proteins at once, but that flood of data is hard to use. Most studies also rely on one small data set, which blocks fair comparison across labs.
The authors merged skeletal muscle data from five studies, covering lab-grown muscle, real tissue, and tendon. Software tools cleaned the data, and databases added facts about each protein's structure and role. They then split the data into five groups by cell makeup and trained a random forest model on each. Using biological context in this way improved the models. It also made the results easier to tailor and explain.
Catalog record
| CREATOR | Alireza Shahin-Shamsabadi, John Cappuccitti |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 73 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |