Motivation: The identification of drug–target interaction (DTI) represents a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Recently, recommendation methods relying on network-based inference (NBI) have been proposed. However, such approaches implement naive topology-based inference and do not take into account important features within the drug–target domain. Results: In this article, we present a new NBI method, called domain tuned-hybrid (DT-Hybrid), which extends a well-established recommendation technique by domain-based knowledge including drug and target similarity. DT-Hybrid has been extensively tested using the last version of an experimentally validated DTI database obtained from DrugBank. Comparison with other recently proposed NBI methods clearly shows that DT-Hybrid is capable of predicting more reliable DTIs. Availability: DT-Hybrid has been developed in R and it is available, along with all the results on the predictions, through an R package at the following URL: http://sites.google.com/site/ehybridalgo/. Contact:apulvirenti@dmi.unict.it Supplementary information: Supplementary data are available at Bioinformatics online.

The controlling nutritional status (CONUT) score represents poor nutritional status and has been identified as an indicator of adverse outcomes. Our aim was to evaluate the prognostic role of the CONUT score on in-hospital outcomes in an Internal Medicine Department. This is a retrospective study analyzing data from 369 patients, divided into four groups based on the CONUT score: normal (0–1), mild–high (2–4), moderate–high (5–8), and marked high (9–12). In-hospital all-cause mortality increased from normal to marked high CONUT score group (2.2% vs. 3.6% vs. 13.4% vs. 15.3%, p < 0.009). Furthermore, a higher CONUT score was linked to a longer length of hospital stay (LOS) (9.48 ± 6.22 vs. 11.09 ± 7.11 vs. 12.45 ± 7.88 vs. 13.10 ± 8.12, p < 0.013) and an increased prevalence of sepsis. The excess risk of a high CONUT score relative to a low CONUT score remained significant after adjusting for confounders (all-cause mortality: OR: 3.3, 95% CI: 1.1–9.7, p < 0.02; sepsis: OR: 2.7, 95% CI: 1.5–4.9, p < 0.01; LOS: OR: 2.1, 95% CI: 1.2–3.9, p < 0.007). The present study demonstrated that an increased CONUT score is related to a higher risk of short-term in-hospital death and complications.

Over the past few years, experimental evidence has highlighted the role of microRNAs to human diseases. miRNAs are critical for the regulation of cellular processes, and, therefore, their aberration can be among the triggering causes of pathological phenomena. They are just one member of the large class of non coding RNAs which include transcribed ultra-conserved regions (T-UCRs), small nucleolar RNAs (snoRNAs), PIWI-interacting RNAs (piRNAs), large intergenic non-coding RNAs (lincRNAs) and, the heterogeneous group of long non-coding RNAs (lncRNAs). Their associations with diseases are few in number, and their reliability is questionable. In literature there is only one recent method proposed by Yang et al. to predict lncRNA--disease associations. This technique, however, lacks in prediction quality. All these elements entail the need to investigate new bioinformatics tools for the prediction of high quality ncRNA--disease associations. Here we propose a method called ncPred for the inference of novel ncRNA-disease association based on recommendation technique. We represent our knowledge through a tripartite network, whose nodes are ncRNAs, targets, or diseases. Interactions in such a network associate each ncRNA with a disease through its targets. Our algorithm, starting from such a network, computes weights between each target-disease pair using a multi-level resource transfer technique that at each step takes into account the resource transferred in the previous one. The results of our experimental analysis show that our approach is able to predict more biologically significant associations with respect to those obtained by Yang et al. yielding an improvement in terms of the average area under the ROC curve (AUC). These results prove the ability of our approach to predict biologically significant associations, which could lead to a better understanding of the molecular processes involved in complex diseases.

Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification