Hemolytic-Pred: A machine learning-based predictor for hemolytic proteins using position and composition-based features.

ABSTRACT:

Objective

The objective of this study is to propose a novel in-silico method called Hemolytic-Pred for identifying hemolytic proteins based on their sequences, using statistical moment-based features, along with position-relative and frequency-relative information.

Methods

Primary sequences were transformed into feature vectors using statistical and position-relative moment-based features. Varying machine learning algorithms were employed for classification. Computational models were rigorously evaluated using four different validation. The Hemolytic-Pred webserver is available for further analysis at http://ec2-54-160-229-10.compute-1.amazonaws.com/.

Results

XGBoost outperformed the other six classifiers with an accuracy value of 0.99, 0.98, 0.97, and 0.98 for self-c

SUBMITTER: Perveen G 

PROVIDER: S-EPMC10331097 | biostudies-literature | 2023 Jan-Dec

REPOSITORIES: biostudies-literature