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New machine learning algorithm accurately decodes molecular optical 'fingerprints'

23
2025-05-09 10:57:00
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Recently, a research team from Rice University in the United States developed a new machine learning algorithm - Peak Sensitive Elastic Network Logistic Regression (PSE-LR). This algorithm is adept at interpreting the unique optical characteristics of molecules, materials, and disease biomarkers, which can help achieve faster and more accurate medical diagnosis and sample analysis. The relevant paper was published in the latest issue of the journal Nano.

The research team stated that the core breakthrough of this technology lies in teaching computers to recognize unique "fingerprints" generated by the interaction between molecules or materials and light. With the help of this technology, in the future, doctors may be able to capture early signals of Alzheimer's disease by simply shining light on a drop of liquid or tissue sample.

PSE-LR not only has the ability to distinguish autumn hair, but also has the interpretability of being open and honest. Unlike other "black box" machine learning models, it can generate clear "feature importance maps" that accurately highlight key spectral segments, making diagnostic results reliable, interpretable, traceable, and easy to verify.

Compared with other machine learning models, PSE-LR shows superior performance, especially in identifying subtle or overlapping spectral features. In addition, in the subsequent series of validation experiments, the performance of the algorithm was also commendable, including the successful detection of the trace presence of COVID-19 spike protein in the liquid, the accurate identification of neuroprotective components in mouse brain tissue, the effective differentiation of microscopic spectral differences in Alzheimer's disease samples, and the identification of the unique optical characteristics of two-dimensional semiconductor materials.

Source: Opticsky

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