Comparing the prediction performance of item response theory and machine learning methods on item responses for educational assessments.

To obtain more accurate and robust feedback information from the students' assessment outcomes and to communicate it to students and optimize teaching and learning strategies, educational researchers and practitioners must critically reflect on whether the existing methods of data analytics are capable of retrieving the information provided in the database. This study compared and contrasted the prediction performance of an item response theory method, particularly the use of an explanatory item response model (EIRM), and six supervised machine learning (ML) methods for predicting students' item responses in educational assessments, considering student- and item-related background information. Each of seven prediction methods was evaluated through cross-validation approaches under three prediction scenarios: (a) unrealized responses of new students to existing items, (b) unrealized responses of existing students to new items, and (c) missing responses of existing students to existing items.

Drug-target interaction prediction with tree-ensemble learning and output space reconstruction.

Background: Computational prediction of drug-target interactions (DTI) is vital for drug discovery. The experimental identification of interactions between drugs and target proteins is very onerous. Modern technologies have mitigated the problem, leveraging the development of new drugs.

Predicting Drug-Target Interactions With Multi-Label Classification and Label Partitioning.

Identifying drug-target interactions is crucial for drug discovery. Despite modern technologies used in drug screening, experimental identification of drug-target interactions is an extremely demanding task. Predicting drug-target interactions in silico can thereby facilitate drug discovery as well as drug repositioning.

Network inference with ensembles of bi-clustering trees.

Background: Network inference is crucial for biomedicine and systems biology. Biological entities and their associations are often modeled as interaction networks. Examples include drug protein interaction or gene regulatory networks.

Mining features for biomedical data using clustering tree ensembles.

The volume of biomedical data available to the machine learning community grows very rapidly. A rational question is how informative these data really are or how discriminant the features describing the data instances are. Several biomedical datasets suffer from lack of variance in the instance representation, or even worse, contain instances with identical features and different class labels.

Making sense of big data in health research: Towards an EU action plan.

Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions.

Decreased CD3+CD16+ natural killer-like T-cell percentage and zeta-chain expression accompany chronic inflammation in haemodialysis patients.

Aim: Clinical and experimental data indicate a deficient immune response in haemodialysis (HD) patients. Natural killer-like (NKL) T cells express on their surface both the T-cell antigen receptor (TCR) and a diverse set of NK-cell receptors (NKR) and share properties of both T cells and NK cells. zeta-Chain phosphorylation is an early event that follows TCR activation or some NKR activation.