Lei Wang

TL;DR: The data showed that MALAT-1 could increase the proportion of pancreatic CSCs, maintain self-renewing capacity, decrease the chemosensitivity to anticancer drugs, and accelerate tumor angiogenesis in vitro, and the potential effects of MALat-1 on the stem cell-like phenotypes in pancreatic cancer cells were found.

TL;DR: A new computational method for predicting DTIs from drug molecular structure and protein sequence by using the stacked autoencoder of deep learning, which has the advantage that it can automatically mine the hidden information from protein sequences and generate highly representative features through iterations of multiple layers.

TL;DR: A novel computational method was proposed to effectively predict the PPIs using the information of a protein sequence, which adopts Zernike moments to extract the protein sequence feature from a position specific scoring matrix (PSSM).

TL;DR: A new model of Logistic Model Tree for predicting miRNA-Disease Association (LMTRDA) is proposed by fusing multi-source information including miRNA sequences, miRNA functional similarity, disease semantic similarity, and known mi RNA-disease associations by introducing miRNA sequence information and extract its features using natural language processing technique for the first time in the miRNA and disease prediction model.

TL;DR: The experimental results suggest that the MLMDA model could serve as a useful tool guiding the future experimental validation for those promising miRNA biomarker candidates.