Prediction of Drug-Target Interactions with High-Quality Negative Samples and A Network-Based Deep Learning Framework.

Identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared to traditional experimental methods, computer-based methods for predicting DTIs can significantly reduce the time and financial burdens of drug development. In recent years, numerous machine learning-based methods have been proposed for predicting potential DTIs.

GCNA-Cluster: A Gene Co-Expression Network Alignment to Cluster Cancer Patients Algorithm for Identifying Subtypes of Pancreatic Ductal Adenocarcinoma.

Cancer heterogeneity makes it necessary to use different treatment strategies for patients with the same pathological features. Accurate identification of cancer subtypes is a crucial step in this approach. The current studies of pancreatic ductal adenocarcinoma (PDAC) subtypes mainly focus on single genes and ignore the synergistic effects of genes.

Identification of subtype specific biomarkers of clear cell renal cell carcinoma using random forest and greedy algorithm.

Suitable biomarkers can be good indicator for cancer subtype. To find biomarkers that can accurately distinguish clear cell renal cell carcinoma (ccRCC) subtypes, we first determined ccRCC subtypes based on the expression of mRNA, miRNA and lncRNA, named clear cell type 1 (ccluster1) and 2 (ccluster2), using three unsupervised clustering algorithms. Besides being associated with the expression pattern derived from the single type of RNA, the differences between subtypes are relevant to the interactions between RNAs.

Temporal variation and spatial scale dependency of the trade-offs and synergies among multiple ecosystem services in the Taihu Lake Basin of China.

Grasping the temporal-spatial characteristics of interactions and spatial scales of multiple ecosystem services is the foundation for sustainable ecosystem management. Eight ecosystem services, including crop production, freshwater supply, aquatic production, net primary production, soil conservation, water retention, flood regulation, and forest recreation were measured at the 1-km pixel scale in the Taihu Lake Basin (TLB) of China from 1990 to 2010. Furthermore, we quantified the trade-offs and synergies of services at different periods of urbanization and across the 1-km pixel scale and the county scale.