Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine.

At present, the study of gene expression data provides a reference for tumor diagnosis at the molecular level. It is a challenging task to select the feature genes related to the classification from the high-dimensional and small-sample gene expression data and successfully separate the different subtypes of tumor or between the normal and patient. In this paper, we present a new method for tumor classification-relaxed Lasso (least absolute shrinkage and selection operator) and generalized multi-class support vector machine (rL-GenSVM).

Predicting protein-protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach.

Research on protein-protein interactions (PPIs) not only helps to reveal the nature of life activities but also plays a driving role in understanding the mechanisms of disease activity and the development of effective drugs. The rapid development of machine learning provides new opportunities and challenges for understanding the mechanism of PPIs. It plays an important role in the field of proteomics research.

Prediction of protein structural class for low-similarity sequences using Chou's pseudo amino acid composition and wavelet denoising.

Prediction of protein structural class plays an important role in protein structure and function analysis, drug design and many other biological applications. Prediction of protein structural class for low-similarity sequences is still a challenging task. Based on the theory of wavelet denoising, this paper presents a novel method of prediction of protein structural class for the first time.