Correction to: dengue virus causes changes of MicroRNA-genes regulatory network revealing potential targets for antiviral drugs

Involvement of host microRNAs in flavivirus-induced neuropathology: An update

Flaviviruses are a spectrum of vector-borne RNA viruses that cause potentially severe diseases in humans including encephalitis, acute-flaccid paralysis, cognitive disorders and foetal abnormalities. Japanese encephalitis virus (JEV), Zika virus (ZIKV), West Nile virus (WNV) and Dengue virus (DENV) are globally emerging pathogens that lead to epidemics and outbreaks with continued transmission to newer geographical areas over time. In the past decade, studies have focussed on understanding the pathogenic mechanisms of these viruses in a bid to alleviate their disease burden. MicroRNAs (miRNAs) are short single-stranded RNAs that have emerged as master-regulators of cellular gene expression. The dynamics of miRNAs within a cell have the capacity to modulate hundreds of genes and, consequently, their physiological manifestation. Increasing evidence suggests their role in host response to disease and infection including cell survival, intracellular viral replication and immune activation. In this review, we aim to comprehensively update published evidence on the role of miRNAs in host cells infected with the common neurotropic flaviviruses, with an increased focus on neuropathogenic mechanisms. In addition, we briefly cover therapeutic advancements made in the context of miRNA-based antiviral strategies.

Comparison of five supervised feature selection algorithms leading to top features and gene signatures from multi-omics data in cancer

BACKGROUND

As many complex omics data have been generated during the last two decades, dimensionality reduction problem has been a challenging issue in better mining such data. The omics data typically consists of many features. Accordingly, many feature selection algorithms have been developed. The performance of those feature selection methods often varies by specific data, making the discovery and interpretation of results challenging.

METHODS AND RESULTS

In this study, we performed a comprehensive comparative study of five widely used supervised feature selection methods (mRMR, INMIFS, DFS, SVM-RFE-CBR and VWMRmR) for multi-omics datasets. Specifically, we used five representative datasets: gene expression (Exp), exon expression (ExpExon), DNA methylation (hMethyl27), copy number variation (Gistic2), and pathway activity dataset (Paradigm IPLs) from a multi-omics study of acute myeloid leukemia (LAML) from The Cancer Genome Atlas (TCGA). The different feature subsets selected by the aforesaid five different feature selection algorithms are assessed using three evaluation criteria: (1) classification accuracy (Acc), (2) representation entropy (RE) and (3) redundancy rate (RR). Four different classifiers, viz., C4.5, NaiveBayes, KNN, and AdaBoost, were used to measure the classification accuary (Acc) for each selected feature subset. The VWMRmR algorithm obtains the best Acc for three datasets (ExpExon, hMethyl27 and Paradigm IPLs). The VWMRmR algorithm offers the best RR (obtained using normalized mutual information) for three datasets (Exp, Gistic2 and Paradigm IPLs), while it gives the best RR (obtained using Pearson correlation coefficient) for two datasets (Gistic2 and Paradigm IPLs). It also obtains the best RE for three datasets (Exp, Gistic2 and Paradigm IPLs). Overall, the VWMRmR algorithm yields best performance for all three evaluation criteria for majority of the datasets. In addition, we identified signature genes using supervised learning collected from the overlapped top feature set among five feature selection methods. We obtained a 7-gene signature (ZMIZ1, ENG, FGFR1, PAWR, KRT17, MPO and LAT2) for EXP, a 9-gene signature for ExpExon, a 7-gene signature for hMethyl27, one single-gene signature (PIK3CG) for Gistic2 and a 3-gene signature for Paradigm IPLs.

CONCLUSION

We performed a comprehensive comparison of the performance evaluation of five well-known feature selection methods for mining features from various high-dimensional datasets. We identified signature genes using supervised learning for the specific omic data for the disease. The study will help incorporate higher order dependencies among features.