Pei Zhao

TL;DR: iFeature is a versatile Python‐based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences, capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors.

TL;DR: iLearn is a comprehensive and versatile Python-based toolkit, integrating the functionality of feature extraction, clustering, normalization, selection, dimensionality reduction, predictor construction, best descriptor/model selection, ensemble learning and results visualization for DNA, RNA and protein sequences.

TL;DR: A comprehensive survey on a large collection of 27 state-of-the-art approaches for predicting N1-methyladenosine and N6-methyl adenosine sites and a proposed computational approach called DeepPromise based on deep learning techniques for simultaneous prediction.

TL;DR: iLearnPlus as discussed by the authors is a machine learning platform with graphical-and web-based interfaces for the construction of machine-learning pipelines for analysis and predictions using nucleic acid and protein sequences.

TL;DR: Benefits of iFeatureOmega are highlighted based on three research applications, demonstrating how it can be used to accelerate and streamline research in bioinformatics, computational biology, and cheminformatics areas.