Tao Huang

TL;DR: The gene expressions among the colorectal cancer patients in the aforementioned four stages were compared and the early and late stage biomarkers, respectively, were obtained and both kinds of biomarkers were mapped onto the protein interaction network.

TL;DR: Investigating tissue expression difference between mRNAs and lncRNAs revealed the heterogeneous expression pattern of lncRNA and mRNA and gave rise to the development of a new tool to identify the potential biological functions of such RNA subgroups.

TL;DR: The results not only demonstrate a high classification capacity and subtype‐specific gene expression patterns but also quantitatively reflect the pattern of the gene expression levels across the NSC lineage, providing insight into deciphering the molecular basis of NSC differentiation.

TL;DR: This study proposes a novel computational method by incorporating several machine learning algorithms, including Monte Carlo feature selection, random forest, and rough set-based rule learning, to identify genes with significant expression differences between patient-derived tumor xenograft (PDX) and original human tumors.

TL;DR: It was observed that the identification accuracy was higher with the tissue samples defined by extracting the features from the second biomarker pool than that with the samples defined based on the first biomarker Pool, indicating that the novel approach holds a quite promising potential in helping find effective biomarkers for diagnosing the liver cirrhosis disease and the hepatocellular carcinoma disease.