Correlation of mental and psychological status with disease activity in patients with inflammatory bowel disease using SCL-90 R Questionnaire.

Introduction: Inflammatory bowel disease (IBD) can affect mental health. There is no evidence that stress is a direct cause of the disease. Most IBD patients describe an emotional impact, mainly feelings of depression and anxiety.

Emerging inflammatory bowel disease demographics, phenotype, and treatment in South Asia, South-East Asia, and Middle East: Preliminary findings from the Inflammatory Bowel Disease-Emerging Nations' Consortium.

Background And Aim: Inflammatory bowel disease (IBD) is emerging in the newly industrialized countries of South Asia, South-East Asia, and the Middle East, yet epidemiological data are scarce.

Methods: We performed a cross-sectional study of IBD demographics, disease phenotype, and treatment across 38 centers in 15 countries of South Asia, South-East Asia, and Middle East. Intergroup comparisons included gross national income (GNI) per capita.

Computational Prediction of Drug-Target Interactions via Ensemble Learning.

Therapeutic effects of drugs are mediated via interactions between them and their intended targets. As such, prediction of drug-target interactions is of great importance. Drug-target interaction prediction is especially relevant in the case of drug repositioning where attempts are made to repurpose old drugs for new indications.

Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey.

Computational prediction of drug-target interactions (DTIs) has become an essential task in the drug discovery process. It narrows down the search space for interactions by suggesting potential interaction candidates for validation via wet-lab experiments that are well known to be expensive and time-consuming. In this article, we aim to provide a comprehensive overview and empirical evaluation on the computational DTI prediction techniques, to act as a guide and reference for our fellow researchers.

Drug-target interaction prediction using ensemble learning and dimensionality reduction.

Experimental prediction of drug-target interactions is expensive, time-consuming and tedious. Fortunately, computational methods help narrow down the search space for interaction candidates to be further examined via wet-lab techniques. Nowadays, the number of attributes/features for drugs and targets, as well as the amount of their interactions, are increasing, making these computational methods inefficient or occasionally prohibitive.

Drug-target interaction prediction via class imbalance-aware ensemble learning.

Background: Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance.

Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization.

Experimental determination of drug-target interactions is expensive and time-consuming. Therefore, there is a continuous demand for more accurate predictions of interactions using computational techniques. Algorithms have been devised to infer novel interactions on a global scale where the input to these algorithms is a drug-target network (i.