Application of an interpretable classification model on Early Folding Residues during protein folding.

ABSTRACT:

Background

Machine learning strategies are prominent tools for data analysis. Especially in life sciences, they have become increasingly important to handle the growing datasets collected by the scientific community. Meanwhile, algorithms improve in performance, but also gain complexity, and tend to neglect interpretability and comprehensiveness of the resulting models.

Results

Generalized Matrix Learning Vector Quantization (GMLVQ) is a supervised, prototype-based machine learning method and provides comprehensive visualization capabilities not present in other classifiers which allow for a fine-grained interpretation of the data. In contrast to commonly used machine learning strategies, GMLVQ is well-suited for imbalanced classification problems which are frequent in life

SUBMITTER: Bittrich S 

PROVIDER: S-EPMC6321665 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature