Dual-view jointly learning improves personalized drug synergy prediction.

Motivation: Accurate and robust estimation of the synergistic drug combination is important for medicine precision. Although some computational methods have been developed, some predictions are still unreliable especially for the cross-dataset predictions, due to the complex mechanism of drug combinations and heterogeneity of cancer samples.

Results: We have proposed JointSyn that utilizes dual-view jointly learning to predict sample-specific effects of drug combination from drug and cell features.

A systematic assessment of deep learning methods for drug response prediction: from in vitro to clinical applications.

Drug response prediction is an important problem in personalized cancer therapy. Among various newly developed models, significant improvement in prediction performance has been reported using deep learning methods. However, systematic comparisons of deep learning methods, especially of the transferability from preclinical models to clinical cohorts, are currently lacking.

Large-scale pharmacogenomic studies and drug response prediction for personalized cancer medicine.

The response rate of most anti-cancer drugs is limited because of the high heterogeneity of cancer and the complex mechanism of drug action. Personalized treatment that stratifies patients into subgroups using molecular biomarkers is promising to improve clinical benefit. With the accumulation of preclinical models and advances in computational approaches of drug response prediction, pharmacogenomics has made great success over the last 20 years and is increasingly used in the clinical practice of personalized cancer medicine.

PDXliver: a database of liver cancer patient derived xenograft mouse models.

Background: Liver cancer is the second leading cause of cancer-related deaths and characterized by heterogeneity and drug resistance. Patient-derived xenograft (PDX) models have been widely used in cancer research because they reproduce the characteristics of original tumors. However, the current studies of liver cancer PDX mice are scattered and the number of available PDX models are too small to represent the heterogeneity of liver cancer patients.

Systematic identification of rabbit LncRNAs reveals functional roles in atherosclerosis.

Long noncoding RNAs (lncRNAs) have been gradually emerging as important regulators in various biological processes and diseases, while the contributions of lncRNAs to atherosclerosis remain largely unknown. Our previous work has discovered atherosclerosis associated protein-coding genes by transcriptome sequencing of rabbit models. Here we investigated the roles of lncRNAs in atherosclerosis.

Population-based study of effectiveness of neoadjuvant radiotherapy on survival in US rectal cancer patients according to age.

Recent cancer researches pay more attention to younger patients due to the variable treatment response among different age groups. Here we investigated the effectiveness of neoadjuvant radiation on the survival of younger and older patients in stage II/III rectal cancer. Data was obtained from Surveillance, Epidemiology, and End Results (SEER) database (n = 12801).

Global Prioritizing Disease Candidate lncRNAs via a Multi-level Composite Network.

LncRNAs play pivotal roles in many important biological processes, but research on the functions of lncRNAs in human disease is still in its infancy. Therefore, it is urgent to prioritize lncRNAs that are potentially associated with diseases. In this work, we developed a novel algorithm, LncPriCNet, that uses a multi-level composite network to prioritize candidate lncRNAs associated with diseases.