Machine Learning Methods for Virus-Host Protein-Protein Interaction Prediction.

The attachment of a virion to a respective cellular receptor on the host organism occurring through the virus-host protein-protein interactions (PPIs) is a decisive step for viral pathogenicity and infectivity. Therefore, a vast number of wet-lab experimental techniques are used to study virus-host PPIs. Taking the great number and enormous variety of virus-host PPIs and the cost as well as labor of laboratory work, however, computational approaches toward analyzing the available interaction data and predicting previously unidentified interactions have been on the rise. Among them, machine-learning-based models are getting increasingly more attention with a great body of resources and tools proposed recently.In this chapter, we first provide the methodology with major steps toward the development of a virus-host PPI prediction tool. Next, we discuss the challenges involved and evaluate several existing machine-learning-based virus-host PPI prediction tools. Finally, we describe our experience with several ensemble techniques as utilized on available prediction results retrieved from individual PPI prediction tools. Overall, based on our experience, we recognize there is still room for the development of new individual and/or ensemble virus-host PPI prediction tools that leverage existing tools.

A correlation coefficient-based feature selection approach for virus-host protein-protein interaction prediction.

Prediction of virus-host protein-protein interactions (PPI) is a broad research area where various machine-learning-based classifiers are developed. Transforming biological data into machine-usable features is a preliminary step in constructing these virus-host PPI prediction tools. In this study, we have adopted a virus-host PPI dataset and a reduced amino acids alphabet to create tripeptide features and introduced a correlation coefficient-based feature selection. We applied feature selection across several correlation coefficient metrics and statistically tested their relevance in a structural context. We compared the performance of feature-selection models against that of the baseline virus-host PPI prediction models created using different classification algorithms without the feature selection. We also tested the performance of these baseline models against the previously available tools to ensure their predictive power is acceptable. Here, the Pearson coefficient provides the best performance with respect to the baseline model as measured by AUPR; a drop of 0.003 in AUPR while achieving a 73.3% (from 686 to 183) reduction in the number of tripeptides features for random forest. The results suggest our correlation coefficient-based feature selection approach, while decreasing the computation time and space complexity, has a limited impact on the prediction performance of virus-host PPI prediction tools.

ML-AdVInfect: A Machine-Learning Based Adenoviral Infection Predictor.

Adenoviruses (AdVs) constitute a diverse family with many pathogenic types that infect a broad range of hosts. Understanding the pathogenesis of adenoviral infections is not only clinically relevant but also important to elucidate the potential use of AdVs as vectors in therapeutic applications. For an adenoviral infection to occur, attachment of the viral ligand to a cellular receptor on the host organism is a prerequisite and, in this sense, it is a criterion to decide whether an adenoviral infection can potentially happen. The interaction between any virus and its corresponding host organism is a specific kind of protein-protein interaction (PPI) and several experimental techniques, including high-throughput methods are being used in exploring such interactions. As a result, there has been accumulating data on virus-host interactions including a significant portion reported at publicly available bioinformatics resources. There is not, however, a computational model to integrate and interpret the existing data to draw out concise decisions, such as whether an infection happens or not. In this study, accepting the cellular entry of AdV as a decisive parameter for infectivity, we have developed a machine learning, more precisely support vector machine (SVM), based methodology to predict whether adenoviral infection can take place in a given host. For this purpose, we used the sequence data of the known receptors of AdVs, we identified sets of adenoviral ligands and their respective host species, and eventually, we have constructed a comprehensive adenovirus-host interaction dataset. Then, we committed interaction predictions through publicly available virus-host PPI tools and constructed an AdV infection predictor model using SVM with RBF kernel, with the overall sensitivity, specificity, and AUC of 0.88 ± 0.011, 0.83 ± 0.064, and 0.86 ± 0.030, respectively. ML-AdVInfect is the first of its kind as an effective predictor to screen the infection capacity along with anticipating any cross-species shifts. We anticipate our approach led to ML-AdVInfect can be adapted in making predictions for other viral infections.

Echocardiographic evaluation of diastolic functions in patients with polycystic ovary syndrome: A comperative study of diastolic functions in sub-phenotypes of polycystic ovary syndrome.

BACKGROUND: Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder among reproductive-aged women. It is known to be associated with cardiovascular diseases. The aim of this study was to determine and compare the echocardiographic data of patients according to the phenotypes of PCOS. METHODS: This study included 113 patients with PCOS and 52 controls. Patients were classified into four potential PCOS phenotypes. Laboratory analyses and echocardiographic measurements were performed. Left ventricular mass was calculated by using Devereux formula and was indexed to body surface area. RESULTS: Phenotype-1 PCOS patients had significantly higher homeostasis model assessment - insu-lin resistance (HOMA-IR) (p = 0.023), free testosterone (p < 0.001), LDL cholesterol levels (p < 0.001) and free androgen index (p < 0.001) compared with the control group. There were significant differences between groups regarding the septal thickness, posterior wall thickness, Left ventricular ejection frac-tion, E/A ratio and left ventricular mass index (for all, p < 0.05). PCOS patients with phenotype 1 and 2 had significantly higher left ventricular mass index than the control group (p < 0.001). In univariate and multivariate analyses, PCOS phenotype, modified Ferriman-Gallwey Score and estradiol were found as variables, which independently could affect the left ventricular mass index. CONCLUSIONS: This study showed that women in their twenties who specifically fulfilled criteria for PCOS phenotype-1 according to the Rotterdam criteria, had higher left ventricular mass index and decreased E/A ratio, which might be suggestive of early stage diastolic dysfunction. (Cariol J 2017; 24, 4: 364-373).