Machine Learning Assisted Array-based Biomolecular Sensing Using Surface Functionalized Carbon Dots.

Fluorescent array-based sensing is an emerging differential sensing platform for sensitive detection of analytes in a complex environment without involving a conventional 'lock and key' type specific interaction. These sensing techniques mainly rely on different optical pattern-generation from a sensor array and their pattern-recognition to differentiate analytes. Currently, emerging compelling pattern-recognition method, Machine Learning (ML), enables a machine to 'learn' a pattern by training without having the recognition method explicitly programmed into it. Thus, ML has an enormous potential to analyze these sensing data better than widely used statistical pattern-recognition methods. Here, an array-based sensor using easy-to-synthesize carbon dots (CDs) with varied surface functionality is reported, which can differentiate between eight different proteins at 100nM concentration. The utility of using machine learning algorithms in pattern-recognition of fluorescence signals from the array has also been demonstrated. In analyzing the array-based sensing data, Machine Learning algorithms like 'Gradient Boosted Trees' have achieved a 100% prediction efficiency compared to inferior-performing classical statistical method 'Linear Discriminant Analysis'.