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Cancer classification has been a crucial topic of research in cancer treatment. In the last decade, messenger RNA (mRNA) expression profiles have been widely used to classify different types of cancers. With the discovery of a new class of small non-coding RNAs; known as microRNAs (miRNAs), various studies have shown that the expression patterns of miRNA can also accurately classify human cancers. Therefore, there is a great demand for the development of machine learning approaches to accurately classify various types of cancers using miRNA expression data.

MicroRNA (miRNA) sponges are RNA transcripts containing multiple high-affinity binding sites that associate with and sequester specific miRNAs to prevent them from interacting with their target messenger (m)RNAs. Due to the high specificity of miRNA sponges and strong inhibition of target miRNAs, these molecules have become increasingly applied in miRNA loss-of-function studies. However, improperly designed sponge constructs may sequester off-target miRNAs; thus, it has become increasingly important to develop a tool for miRNA sponge construct design and testing.

BACKGROUND: In many cancers, microRNAs (miRs) contribute to metastatic progression by modulating phenotypic reprogramming processes such as epithelial-mesenchymal plasticity. This can be driven by miRs targeting multiple mRNA transcripts, inducing regulated changes across large sets of genes. The miR-target databases TargetScan and DIANA-microT predict putative relationships by examining sequence complementarity between miRs and mRNAs.

BACKGROUND: Long noncoding RNAs (lncRNAs) have been shown to play essential roles in almost every important biological process through multiple mechanisms. Although the repertoire of human lncRNAs has rapidly expanded, their biological function and regulation remain largely elusive, calling for a systematic and integrative annotation tool. RESULTS: Here we present AnnoLnc ( http://annolnc.cbi.pku.edu.cn ), a one-stop portal for systematically annotating novel human lncRNAs.

MicroRNAs (miRNAs) are known as an important indicator of cancers. Presence of cancer can be detected by identifying the responsible miRNAs. A fuzzy-rough entropy measure (FREM) is developed which can rank the miRNAs and thereby identifying the relevant ones. FREM is used to determine the relevance of a miRNA in terms of separability between normal and cancer classes. While computing the FREM for a miRNA, fuzziness takes care of the overlapping between normal and cancer expressions, whereas rough lower approximation determines their class sizes.

The miRBase database is the central and official repository for miRNAs and the current release is miRBase version 21.0. Name changes in different miRBase releases cause inconsistencies in miRNA names from version to version. When working with only a small number of miRNAs the translation can be done manually. However, with large sets of miRNAs, the necessary correction of such inconsistencies becomes burdensome and error-prone.