Tao Huang

TL;DR: This study proposed and compared two novel computational approaches based on multiple machine learning algorithms for the qualitative and quantitative analyses of methylation-associated genes and their dys-methylated patterns and contributes to the identification of novel effective genes and the establishment of optimal quantitative rules for aberrant methylation distinguishing tumor cells with different origin tissues.

TL;DR: The results showed that by using gene expression profiles as features, the optimal classification model could achieve very high even perfect performance for Golgi, granule, interneuron, and unipolar brush cells, respectively, suggesting a remarkable difference between the genomic profiles of human and mouse.

TL;DR: The SVM classifier may serve as an important clinical tool to address the challenging task of differentiating between CHP and IPF and many of the biomarker genes on the differential co-expression network showed great promise in revealing the underlying mechanisms of CHP.

TL;DR: A computational method was developed to predict protein amidation sites, by incorporating the maximum relevance minimum redundancy method and the incremental feature selection method based on the nearest neighbor algorithm, which could be used as an efficient tool to theoretically predict amidated peptides.

TL;DR: This study constructed similarity network for phenotype ontology, and then applied network analysis methods to discover phenotype/disease clusters, and used machine learning models to predict protein-phenotype associations.