miRToolsGallery is a database of miRNA tools. It provides the following services: (a) Search，(b) Filter and (c) Rank the tools. 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. And it’s also very convenient for writing a tools review paper. Now we have collect above 1000 tools. miRToolsGallery will update when every new 100 tools add in. The first public online was in 1st Oct, 2016, and latest update time is 22nd April, 2018(v1.2).
MicroRNAs are crucial regulators of gene expression at post-transcriptional level. Understanding origin and evolution of miRNAs and their functions. Transposable elements (TEs) provide a natural mechanism for the origin of new miRNAs derived from TEs (MDTEs) were collected to contruct a database named MDTE database (MDTE DB) for storing, searching and analyzing MDTEs. The database proveds a convenient source for studying the origin and evolution of miRNAs.[1]
MicroRNAs are short non-coding RNA which function to fine-tune protein levels in all cells. This is achieved mainly by sequence-specific binding to 3' untranslated regions of target mRNA. The result is post-transcriptional interference in gene expression which reduces protein levels either by promoting destabilisation of mRNA or translational repression. Research published since 2010 shows that microRNAs are important regulators of gene expression in epilepsy.
Small cell lung carcinoma (SCLC) is an aggressive, recalcitrant cancer, often metastatic at diagnosis and unresponsive to chemotherapy upon recurrence, thus it is challenging to treat.
The identification of microRNA (miRNA) target sites is fundamentally important for studying gene regulation. There are dozens of computational methods available for miRNA target site prediction. Despite their existence, we still cannot reliably identify miRNA target sites, partially due to our limited understanding of the characteristics of miRNA target sites.
MicroRNAs (miRNAs) regulate genes that are associated with various diseases. To better understand miRNAs, the miRNA regulatory mechanism needs to be investigated and the real targets identified. Here, we present miRTDL, a new miRNA target prediction algorithm based on convolutional neural network (CNN). The CNN automatically extracts essential information from the input data rather than completely relying on the input dataset generated artificially when the precise miRNA target mechanisms are poorly known.
As a special class of non-coding RNAs (ncRNAs), microRNAs (miRNAs) perform important roles in numerous biological and pathological processes. The realization of miRNA functions depends largely on how miRNAs regulate specific target genes. It is therefore critical to identify, analyze, and cross-reference miRNA-target interactions to better explore and delineate miRNA functions. Semantic technologies can help in this regard.
