Statistical Linear Models in Virus Genomic Alignmentfree Classification: Application to Hepatitis C Viruses Viral sequence classification is an important task in pathogen detection, epidemiological surveys and evolutionary studies. Statistical learning methods are widely used to classify and identify viral sequences in samples from environments. These methods face several challenges associated with the nature and properties of viral genomes such as recombination, mutation rate and diversity. Also, new generations of sequencing technologies rise other difficulties by generating massive amounts of fragmented sequences. While linear classifiers are often used to classify viruses, there is a lack of exploration of the accuracy space of existing models in the context of alignment free approaches. In this study, we present an exhaustive assessment procedure exploring the power of linear classifiers in genotyping and subtyping partial and complete genomes. It is applied to the Hepatitis C viruses (HCV). Several variables are considered in this investigation such as classifier types (generative and discriminative) and their hyperparameters (smoothing value and regularization penalty function), the classification task (genotyping and subtyping), the length of the tested sequences (partial and complete) and the length of  k mer words. Overall, several classifiers perform well given a set of precise combination of the experimental variables mentioned above. Finally, we provide the procedure and benchmark data to allow for more robust assessment of classification from virus genomes.

A Multiclassifier Model to Identify Mitochondrial Respiratory Gene Signatures in Human Cancer Whether alteration of mitochondrial gene expression can serve as an effective molecular signature for cancer classification currently remains controversy. To tackle this challenge, here we present a multiclassifier model to identify mitochondrial aberrant gene signatures and then assess the effectiveness on cancer classification. Specifically, we first applied a supervised learning model, Empirical Bayes statistics, to detect differentially expressed genes from a liver cancer mitochondrial gene expression dataset (GEO accession: GSE64505). Next, we applied two wellknown classifiers, Prediction Analysis of Microarrays (PAM) and Random Forest (RF), with several folds of crossvalidation, to find molecular signatures that can classify liver cancer samples from normal samples. We obtained a mitochondrial molecular signature comprising 25 genes. Classification accuracy was 87.5% by PAM classifier (5 fold crossvalidation with 30 repeats), while it was 87.5% and 75.0% by Random Forest with 5 and 2fold crossvalidations (30 repeats), respectively. We further performed literature mining and Gene Set Enrichment Analysis (GSEA) to evaluate the biological significance and novelty of genes in this gene signature.