These results would encourage early discharge of individuals with a NLR of less than 11.12, short-term in-hospital care for those with a NLR between 11.12 and 13.4, middle-term hospitalization for those with a NLR between 13.4 and 28.3, and admission to a respiratory intensive care unit for those with a NLR greater than 28.3.

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.itSupplementary information: Supplementary data are available at Bioinformatics online.

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MotivationPrediction of phenotypes from high-dimensional data is a crucial task in precision biology and medicine. Many technologies employ genomic biomarkers to characterize phenotypes. However, such elements are not sufficient to explain the underlying biology. To improve this, pathway analysis techniques have been proposed. Nevertheless, such methods have shown lack of accuracy in phenotypes classification.ResultsHere we propose a novel methodology called MITHrIL (Mirna enrIched paTHway Impact anaLysis) for the analysis of signaling pathways, which extends the work of Tarca et al., 2009. MITHrIL augments pathways with missing regulatory elements, such as microRNAs, and their interactions with genes. The method takes as input the expression values of genes and/or microRNAs and returns a list of pathways sorted according to their degree of deregulation, together with the corresponding statistical significance (p-values). Our analysis shows that MITHrIL outperforms its competitors even in the worst case. In addition, our method is able to correctly classify sets of tumor samples drawn from TCGA.AvailabilityMITHrIL is freely available at the following URL: http://alpha.dmi.unict.it/mithril/

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Autism spectrum disorder (ASD) is currently diagnosed according to behavioral criteria. Biomarkers that identify children with ASD could lead to more accurate and early diagnosis. ASD is a complex disorder with multifactorial and heterogeneous etiology supporting recognition of biomarkers that identify patient subsets. We investigated an easily testable blood metabolic profile associated with ASD diagnosis using high throughput analyses of samples extracted from dried blood spots (DBS). A targeted panel of 45 ASD analytes including acyl-carnitines and amino acids extracted from DBS was examined in 83 children with ASD (60 males; age 6.06 ± 3.58, range: 2–10 years) and 79 matched, neurotypical (NT) control children (57 males; age 6.8 ± 4.11 years, range 2.5–11 years). Based on their chronological ages, participants were divided in two groups: younger or older than 5 years. Two-sided T-tests were used to identify significant differences in measured metabolite levels between groups. Näive Bayes algorithm trained on the identified metabolites was used to profile children with ASD vs. NT controls. Of the 45 analyzed metabolites, nine (20%) were significantly increased in ASD patients including the amino acid citrulline and acyl-carnitines C2, C4DC/C5OH, C10, C12, C14:2, C16, C16:1, C18:1 (P: < 0.001). Näive Bayes algorithm using acyl-carnitine metabolites which were identified as significantly abnormal showed the highest performances for classifying ASD in children younger than 5 years (n: 42; mean age 3.26 ± 0.89) with 72.3% sensitivity (95% CI: 71.3;73.9), 72.1% specificity (95% CI: 71.2;72.9) and a diagnostic odds ratio 11.25 (95% CI: 9.47;17.7). Re-test analyses as a measure of validity showed an accuracy of 73% in children with ASD aged ≤ 5 years. This easily testable, non-invasive profile in DBS may support recognition of metabolic ASD individuals aged ≤ 5 years and represents a potential complementary tool to improve diagnosis at earlier stages of ASD development.

Next-generation sequencing is increasing our understanding and knowledge of non-coding RNAs (ncRNAs), elucidating their roles in molecular mechanisms and processes such as cell growth and development. Within such a class, tRNA-derived ncRNAs have been recently associated with gene expression regulation in cancer progression. In this paper, we characterize, for the first time, tRNA-derived ncRNAs in NCI-60. Furthermore, we assess their expression profile in The Cancer Genome Atlas (TCGA). Our comprehensive analysis allowed us to report 322 distinct tRNA-derived ncRNAs in NCI-60, categorized in tRNA-derived fragments (11 tRF-5s, 55 tRF-3s), tRNA-derived small RNAs (107 tsRNAs) and tRNA 5′ leader RNAs (149 sequences identified). In TCGA, we were able to identify 232 distinct tRNA-derived ncRNAs categorized in 53 tRF-5s, 58 tRF-3s, 63 tsRNAs and 58 5′ leader RNAs. This latter group represents an additional evidence of tRNA-derived ncRNAs originating from the 5′ leader region of precursor tRNA. We developed a public database, tRFexplorer, which provides users with the expression profile of each tRNA-derived ncRNAs in every cell line in NCI-60 as well as for each TCGA tumor type. Moreover, the system allows us to perform differential expression analyses of such fragments in TCGA, as well as correlation analyses of tRNA-derived ncRNAs expression in TCGA and NCI-60 with gene and miRNA expression in TCGA samples, in association with all omics and compound activities data available on CellMiner. Hence, the tool provides an important opportunity to investigate their potential biological roles in absence of any direct experimental evidence. Database URL: https://trfexplorer.cloud/

Posttranscriptional cross talk and communication between genes mediated by microRNA response element (MREs) yield large regulatory competing endogenous RNA (ceRNA) networks. Their inference may improve the understanding of pathologies and shed new light on biological mechanisms. A variety of RNA: messenger RNA, transcribed pseudogenes, noncoding RNA, circular RNA and proteins related to RNA-induced silencing complex complex interacting with RNA transfer and ribosomal RNA have been experimentally proved to be ceRNAs. We retrace the ceRNA hypothesis of posttranscriptional regulation from its original formulation [Salmena L, Poliseno L, Tay Y, et al. Cell 2011;146:353-8] to the most recent experimental and computational validations. We experimentally analyze the methods in literature [Li J-H, Liu S, Zhou H, et al. Nucleic Acids Res 2013;42:D92-7; Sumazin P, Yang X, Chiu H-S, et al. Cell 2011;147:370-81; Sarver AL, Subramanian S. Bioinformation 2012;8:731-3] comparing them with a general machine learning approach, called ceRNA predIction Algorithm, evaluating the performance in predicting novel MRE-based ceRNAs.