BACKGROUND/AIM: Using machine learning approaches as non-invasive methods have been used recently as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy. This study aims to evaluate different machine learning techniques in prediction of advanced fibrosis by combining the serum bio-markers and clinical information to develop the classification models. METHODS: A prospective cohort of 39,567 patients with chronic hepatitis C was divided into two sets-one categorized as mild to moderate fibrosis (F0-F2), and the other categorized as advanced fibrosis (F3-F4) according to METAVIR score. Decision tree, genetic algorithm, particle swarm optimization, and multi-linear regression models for advanced fibrosis risk prediction were developed. Receiver operating characteristic curve analysis was performed to evaluate the performance of the proposed models. RESULTS: Age, platelet count, AST, and albumin were found to be statistically significant to advanced fibrosis. The machine learning algorithms under study were able to predict advanced fibrosis in patients with HCC with AUROC ranging between 0.73 and 0.76 and accuracy between 66.3 and 84.4 percent. CONCLUSIONS: Machine-learning approaches could be used as alternative methods in prediction of the risk of advanced liver fibrosis due to chronic hepatitis C.

MOTIVATION: Can we predict protein-protein interactions (PPIs) of a novel virus with its host? Three major problems arise: the lack of known PPIs for that virus to learn from, the cost of learning about its proteins and the sequence dissimilarity among viral families that makes most methods inapplicable or inefficient. We develop DeNovo, a sequence-based negative sampling and machine learning framework that learns from PPIs of different viruses to predict for a novel one, exploiting the shared host proteins. We tested DeNovo on PPIs from different domains to assess generalization. RESULTS: By solving the challenge of generating less noisy negative interactions, DeNovo achieved accuracy up to 81 and 86% when predicting PPIs of viral proteins that have no and distant sequence similarity to the ones used for training, receptively. This result is comparable to the best achieved in single virus-host and intra-species PPI prediction cases. Thus, we can now predict PPIs for virtually any virus infecting human. DeNovo generalizes well; it achieved near optimal accuracy when tested on bacteria-human interactions. AVAILABILITY AND IMPLEMENTATION: Code, data and additional supplementary materials needed to reproduce this study are available at: https://bioinformatics.cs.vt.edu/~alzahraa/denovo CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The RNA polymerase NS5B of Hepatitis C virus (HCV) is a well-characterised drug target with an active site and four allosteric binding sites. This work presents a workflow for virtual screening and its application to Drug Bank screening targeting the Hepatitis C Virus (HCV) RNA polymerase non-nucleoside binding sites. Potential polypharmacological drugs are sought with predicted active inhibition on viral replication, and with proven positive pharmaco-clinical profiles. The approach adopted was receptor-based. Docking screens, guided with contact pharmacophores and neural-network activity prediction models on all allosteric binding sites and MD simulations, constituted our analysis workflow for identification of potential hits. Steps included: 1) using a two-phase docking screen with Surflex and Glide Xp. 2) Ranking based on scores, and important H interactions. 3) a machine-learning target-trained artificial neural network PIC prediction model used for ranking. This provided a better correlation of IC50 values of the training sets for each site with different docking scores and sub-scores. 4) interaction pharmacophores-through retrospective analysis of protein-inhibitor complex X-ray structures for the interaction pharmacophore (common interaction modes) of inhibitors for the five non-nucleoside binding sites were constructed. These were used for filtering the hits according to the critical binding feature of formerly reported inhibitors. This filtration process resulted in identification of potential new inhibitors as well as formerly reported ones for the thumb II and Palm I sites (HCV-81) NS5B binding sites. Eventually molecular dynamics simulations were carried out, confirming the binding hypothesis and resulting in 4 hits.

MOTIVATION: There is an urgent need for new medications to combat influenza pandemics. METHODS: Using the genome analysis of the influenza A virus performed previously, we designed and performed a combinatorial exhaustive systematic methodology for optimal design of universal therapeutic small interfering RNA molecules (siRNAs) targeting all diverse influenza A viral strains. The rationale was to integrate the factors for highly efficient design in a pipeline of analysis performed on possible influenza-targeting siRNAs. This analysis selects specific siRNAs that has the ability to target highly conserved, accessible and biologically significant regions. This would require minimal dosage and side effects. RESULTS AND DISCUSSION: First, >6000 possible siRNAs were designed. Successive filtration followed where a novel method for siRNA scoring filtration layers was implemented. This method excluded siRNAs below the 90% experimental inhibition mapped scores using the intersection of 12 different scoring algorithms. Further filtration of siRNAs is done by eliminating those with off-targets in the human genome and those with undesirable properties and selecting siRNA targeting highly probable single-stranded regions. Finally, the optimal properties of the siRNA were ensured through selection of those targeting 100% conserved, biologically functional short motifs. Validation of a predicted active (sh114) and a predicted inactive (sh113) (that was filtered out in Stage 8) silencer of the NS1 gene showed significant inhibition of the NS1 gene for sh114, with negligible decrease for sh113 which failed target accessibility. This demonstrated the fertility of this methodology. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.