Effective moment feature vectors for protein domain structures

Predicting combinative drug pairs via multiple classifier system with positive samples only

BACKGROUND AND OBJECTIVE

Due to the synergistic effects of drugs, drug combination is one of the effective approaches for treating complex diseases. However, the identification of drug combinations by dose-response methods is still costly. It is promising to develop supervised learning-based approaches to predict potential drug combinations on a large scale. Nevertheless, these approaches have the inadequate utilization of heterogeneous features, which causes the loss of information useful to classification. Moreover, they have an intrinsic bias, because they assume unknown drug pairs as non-combinations, of which some could be real drug combinations in practice.

METHODS

To address above issues, this work first designs a two-layer multiple classifier system (TLMCS) to effectively integrate heterogeneous features involving anatomical therapeutic chemical codes of drugs, drug-drug interactions, drug-target interactions, gene ontology of drug targets, and side effects. To avoid the bias caused by labelling unknown samples as negative, it then utilizes the one-class support vector machines, (which requires no negative instance and only labels approved drug combinations as positive instances), as the member classifiers in TLMCS. Last, both a 10-fold cross validation (10-CV) and a novel prediction are performed to validate the performance of TLMCS.

RESULTS

The comparison with three state-of-the-art approaches under 10-CV exhibits the superiority of TLMCS, which achieves the area under the receiver operating characteristic curve = 0.824 and the area under the precision-recall curve = 0.372. Moreover, the experiment under the novel prediction demonstrates its ability, where 9 out of the top-20 predicted combinative drug pairs are validated by checking the published literature. Furthermore, for each of the newly-validated drug combinations, this work analyses the combining mode of the member drugs and investigates their relationship in terms of drug targeting pathways.

CONCLUSIONS

The proposed TLMCS provides an effective framework to integrate those heterogeneous features and is trained by only positive samples such that the bias of taking unknown drug pairs as negative samples can be avoided. Furthermore, its results in the novel prediction reveal five types of drug combinations and three types of drug relationships in terms of pathways.

Homocysteine Induces Apoptosis of Human Umbilical Vein Endothelial Cells via Mitochondrial Dysfunction and Endoplasmic Reticulum Stress

Homocysteine- (Hcy-) induced endothelial cell apoptosis has been suggested as a cause of Hcy-dependent vascular injury, while the proposed molecular pathways underlying this process are unclear. In this study, we investigated the adverse effects of Hcy on human umbilical vein endothelial cells (HUVEC) and the underlying mechanisms. Our results demonstrated that moderate-dose Hcy treatment induced HUVEC apoptosis in a time-dependent manner. Furthermore, prolonged Hcy treatment increased the expression of NOX4 and the production of intracellular ROS but decreased the ratio of Bcl-2/Bax and mitochondrial membrane potential (MMP), resulting in the leakage of cytochrome c and activation of caspase-3. Prolonged Hcy treatment also upregulated glucose-regulated protein 78 (GRP78), activated protein kinase RNA-like ER kinase (PERK), and induced the expression of C/EBP homologous protein (CHOP) and the phosphorylation of NF-κb. The inhibition of NOX4 decreased the production of ROS and alleviated the Hcy-induced HUVEC apoptosis and ER stress. Blocking the PERK pathway partly alleviated Hcy-induced HUVEC apoptosis and the activation of NF-κb. Taken together, our results suggest that Hcy-induced mitochondrial dysfunction crucially modulated apoptosis and contributed to the activation of ER stress in HUVEC. The excessive activation of the PERK pathway partly contributed to Hcy-induced HUVEC apoptosis and the phosphorylation of NF-κb.