Guoxian Yu

TL;DR: The proposed method (PILL) can serve as a valuable tool for protein function prediction using incomplete labels and 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.

TL;DR: A case study on identifying lncRNAs associated with breast, lung and stomach cancers show that 38 out of 45 associations predicted by MFLDA are supported by recent biomedical literature and further proves the capability of M FLDA in identifying novel lncRNA-disease associations.

TL;DR: A neural network based approach called EnsDNN (Ensemble Deep Neural Networks) is proposed to predict PPIs based on different representations of amino acid sequences, which achieves superior performance on predicting PPIs of Saccharomyces cerevisiae.

TL;DR: This paper focuses on graph construction for semi-supervised learning and proposes a novel method called Semi-Supervised Classification based on Random Subspace Dimensionality Reduction, SSC-RSDR, which has superior recognition performance with respect to competitive methods, but also is robust against a wide range of values of input parameters.

TL;DR: iMVWL simultaneously learns a shared subspace from incomplete views with weak labels, the local label structure and the predictor in this subspace, which can not only capture cross-view relationships but also weak-label information of training samples.