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Abstract: MicroRNA (miRNA) is the negative regulator of gene expression, also known as guide strand of transient miRNA:miRNA* duplex. It is critical in maintaining the normal physiological processes such as development, differentiation, and apoptosis in many organisms. With increasing miRNA data, it is desirable to design methods to identify guide strand based on machine learning algorithms. In this study, the random forest models based on local sequence–structure features were proposed to identify miRNA in four species. The accuracies achieved were 86.51% for Homo sapiens, 81.66% for Ornithorhynchus anatinus, 82.33% for Mus musculus and 85.71% for Schmidtea mediterranea, respectively. Furthermore, the important analysis of feature elements was carried out by using the conditional feature importance strategy. The analysis results revealed that most of the significant elements were related to guanine–cytosine (GC) base pair. We believed that our method could be beneficial to annotate the function of miRNA and help the further understanding of the RNA interference mechanism. [Copyright &y& Elsevier]

Biomarker discovery plays an important role in cancer diagnosis and prognosis assessments. The biomarkers that could be applied among different cancer types are highly useful. Although many traditional feature selection algorithms have shown their power on picking discriminative genes, they are incapable of identifying biologically meaningful biomarkers. Here, on the hypothesis that gene essentiality would be disrupted in cancers, we estimated the gene sets with significant essentiality alteration in six cancer types. We found that different cancer types would share some common gene essentiality alterations. Then, the variable combination population analysis (VCPA) algorithm was applied to identify the potential biomarkers from these common genes, which were used to construct prediction models and exhibited satisfactory classification ability (averaged accuracy: 0.9752) among six cancer types. Interestingly, these biomarkers would tend to cluster as a subnetwork and be characterized by high centrality values in the protein–protein interaction network. They were significantly enriched in the cell cycle and DNA replication pathway which are hallmark signatures of cancers. Several biomarkers have been even verified by the literature searching, reported having roles in chromosome instability and aberrantly expressed between cancer/normal samples. An additional comparison analysis between the VCPA and other six feature selection methods in WEKA suggested biomarkers by VCPA perform superior over those by other methods. These results suggested that our method is promising in identifying the potential multi-cancer biomarkers. [ABSTRACT FROM AUTHOR]