Southwest University

About: Southwest University is a education organization based out in Chongqing, China. It is known for research contribution in the topics: Gene & Population. The organization has 29772 authors who have published 27755 publications receiving 409441 citations. The organization is also known as: Southwest University in Chongqing & SWU.

Predicting protein functions using incomplete hierarchical labels

Abstract: Protein function prediction is to assign biological or biochemical functions to proteins, and it is a challenging computational problem characterized by several factors: (1) the number of function labels (annotations) is large; (2) a protein may be associated with multiple labels; (3) the function labels are structured in a hierarchy; and (4) the labels are incomplete. Current predictive models often assume that the labels of the labeled proteins are complete, i.e. no label is missing. But in real scenarios, we may be aware of only some hierarchical labels of a protein, and we may not know whether additional ones are actually present. The scenario of incomplete hierarchical labels, a challenging and practical problem, is seldom studied in protein function prediction. In this paper, we propose an algorithm to Predict protein functions using Incomplete hierarchical LabeLs (PILL in short). PILL takes into account the hierarchical and the flat taxonomy similarity between function labels, and defines a Combined Similarity (ComSim) to measure the correlation between labels. PILL estimates the missing labels for a protein based on ComSim and the known labels of the protein, and uses a regularization to exploit the interactions between proteins for function prediction. PILL is shown to outperform other related techniques in replenishing the missing labels and in predicting the functions of completely unlabeled proteins on publicly available PPI datasets annotated with MIPS Functional Catalogue and Gene Ontology labels. The empirical study shows that it is important to consider the incomplete annotation for protein function prediction. The proposed method (PILL) can serve as a valuable tool for protein function prediction using incomplete labels. The Matlab code of PILL is available upon request.

Circular RNA: a novel biomarker and therapeutic target for human cancers.

Abstract: Circular (circ)RNAs, a newly recognized class of noncoding RNA, have been implicated in the occurrence and development of several diseases, including neurological and cardiovascular diseases. Studies of human tumors, including those of liver cancer, gastric cancer, lung cancer and colorectal cancer, have shown differential expression profiles of circRNAs, suggesting regulatory roles in cancer pathogenesis and metastasis. In this review, we discuss the most recent research into tumor-related circRNAs, providing a comprehensive summary of the expression or/and function of these circRNAs and proposing rational perspectives on the potential clinical application of circRNAs as helpful biomarkers or therapeutic targets in human tumors.