Biological pathways are important for understanding biological mechanisms. Thus, finding important pathways that underlie biological problems helps researchers to focus on the most relevant sets of genes. Pathways resemble networks with complicated structures, but most of the existing pathway enrichment tools ignore topological information embedded within pathways, which limits their applicability.
Identifying key microRNAs (miRNAs) contributing to the genesis and development of a particular disease is a focus of many recent studies. We introduce here a rank-based algorithm to detect miRNA regulatory activity in cancer-derived tissue samples which combines measurements of gene and miRNA expression levels and sequence-based target predictions. The method is designed to detect modest but coordinated changes in the expression of sequence-based predicted target genes.
MicroRNA (miRNA), which is short non-coding RNA, plays a pivotal role in the regulation of many biological processes and affects the stability and/or translation of mRNA. Recently, machine learning algorithms were developed to predict potential miRNA targets. Most of these methods are robust but are not sensitive to redundant or irrelevant features. Despite their good performance, the relative importance of each feature is still unclear.
Ovarian cancer has the lowest survival rate of all gynaecologic cancers and is characterised by a lack of early symptoms and frequent late stage diagnosis. There is a paucity of robust molecular markers that are independent of and complementary to clinical parameters such as disease stage and tumour grade.
Large-scale molecular interaction networks are dynamic in nature and are of special interest in the analysis of complex diseases, which are characterized by network-level perturbations rather than changes in individual genes/proteins. The methods developed for the identification of differentially expressed genes or gene sets are not suitable for network-level analyses.
ToppCluster is a web server application that leverages a powerful enrichment analysis and underlying data environment for comparative analyses of multiple gene lists. It generates heatmaps or connectivity networks that reveal functional features shared or specific to multiple gene lists. ToppCluster uses hypergeometric tests to obtain list-specific feature enrichment P-values for currently 17 categories of annotations of human-ortholog genes, and provides user-selectable cutoffs and multiple testing correction methods to control false discovery.
Compared with complementary DNA (cDNA) or messenger RNA (mRNA) microarray data, microRNA (miRNA) microarray data are harder to normalize due to the facts that the total number of miRNAs is small, and that the majority of miRNAs usually have low expression levels. In bead-based microarrays, the hybridization is completed in several pools. As a result, the number of miRNAs tested in each pool is even smaller, which poses extra difficulty to intrasample normalization and ultimately affects the quality of the final profiles assembled from various pools.
MicroRNAs (miRNAs) play a crucial role in tumorigenesis and development through their effects on target genes. The characterization of miRNA-gene interactions will lead to a better understanding of cancer mechanisms. Many computational methods have been developed to infer miRNA targets with/without expression data. Because expression datasets are in general limited in size, most existing methods concatenate datasets from multiple studies to form one aggregated dataset to increase sample size and power.
MicroRNAs (miRNAs) can regulate nearly all biological processes and their dysregulation is implicated in various complex diseases and pathological conditions.
MicroRNAs are short RNAs that serve as regulators of gene expression and are essential components of normal development as well as modulators of disease. MicroRNAs generally act cell-autonomously, and thus their localization to specific cell types is needed to guide our understanding of microRNA activity. Current tissue-level data have caused considerable confusion, and comprehensive cell-level data do not yet exist. Here, we establish the landscape of human cell-specific microRNA expression.
Our database aim to make it easy for researchers to find the right tools or data source for their own specific study in miRNA field. Now we have collect >1000 tools. This database will update when every new 100 tools add in. Last update time 22nd April, 2018 (v1.2).
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