Improve hot region prediction by analyzing different machine learning algorithms.

ABSTRACT:

Background

In the process of designing drugs and proteins, it is crucial to recognize hot regions in protein-protein interactions. Each hot region of protein-protein interaction is composed of at least three hot spots, which play an important role in binding. However, it takes time and labor force to identify hot spots through biological experiments. If predictive models based on machine learning methods can be trained, the drug design process can be effectively accelerated.

Results

The results show that different machine learning algorithms perform similarly, as evaluating using the F-measure. The main differences between these methods are recall and precision. Since the key attribute of hot regions is that they are packed tightly, we used the cluster algorithm to predict h

SUBMITTER: Hu J 

PROVIDER: S-EPMC8543831 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature