Optimizing intervertebral disc cell metabolic phenotyping with machine learning and artificial neural networks
Md Entaz Bahar, Rizi Firman Maulidi, Quang Nhat Ngo, Deok Ryong Kim · 2025
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Description
Md Entaz Bahar and three co-authors pair laboratory work with machine learning and artificial neural networks to sharpen metabolic phenotyping of intervertebral disc cells. A bench analyzer tracks oxygen consumption and acid output in living cells, showing how cells produce energy. Accurate readings need a uniform cell layer and carefully titrated doses of key reagents. Their 2025 study asks which mathematical tools best predict the readings and cut wasted experimental runs.
Tree-based models won the prediction tasks for both continuous scores and class labels. One classification tool using straight and curved cuts also did well. The team then built a strict workflow joining blinded grouping with a trained neural network, and its output sat close to the real laboratory readings. They offer the workflow as a fair, data-driven way to read cell energy use at fine scale. It may serve both basic research and drug screening.
Catalog record
| CREATOR | Md Entaz Bahar, Rizi Firman Maulidi, Quang Nhat Ngo, Deok Ryong Kim |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 65 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |