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MKRMDA

Submitted by ChenLiang on Tue, 01/09/2018 - 18:50

Recently, as the research of microRNA (miRNA) continues, there are plenty of experimental evidences indicating that miRNA could be associated with various human complex diseases development and progression. Hence, it is necessary and urgent to pay more attentions to the relevant study of predicting diseases associated miRNAs, which may be helpful for effective prevention, diagnosis and treatment of human diseases.

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GeneHub-GEPIS

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

GeneHub-GEPIS is a web application that performs digital expression analysis in human and mouse tissues based on an integrated gene database. Using aggregated expressed sequence tag (EST) library information and EST counts, the application calculates the normalized gene expression levels across a large panel of normal and tumor tissues, thus providing rapid expression profiling for a given gene.

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MixMir

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

microRNAs (miRNAs) are a class of ~22nt non-coding RNAs that potentially regulate over 60% of human protein-coding genes. miRNA activity is highly specific, differing between cell types, developmental stages and environmental conditions, so the identification of active miRNAs in a given sample is of great interest. Here we present a novel computational approach for analyzing both mRNA sequence and gene expression data, called MixMir.

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CellBase

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

During the past years, the advances in high-throughput technologies have produced an unprecedented growth in the number and size of repositories and databases storing relevant biological data. Today, there is more biological information than ever but, unfortunately, the current status of many of these repositories is far from being optimal.

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rVarBase

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

We present here the rVarBase database (http://rv.psych.ac.cn), an updated version of the rSNPBase database, to provide reliable and detailed regulatory annotations for known and novel human variants. This update expands the database to include additional types of human variants, such as copy number variations (CNVs) and novel variants, and include additional types of regulatory features.

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Ortho2ExpressMatrix

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

The study of gene families is pivotal for the understanding of gene evolution across different organisms and such phylogenetic background is often used to infer biochemical functions of genes. Modern high-throughput experiments offer the possibility to analyze the entire transcriptome of an organism; however, it is often difficult to deduct functional information from that data.

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pseudoMap

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

RNA interference (RNAi) is a gene silencing process within living cells, which is controlled by the RNA-induced silencing complex with a sequence-specific manner. In flies and mice, the pseudogene transcripts can be processed into short interfering RNAs (siRNAs) that regulate protein-coding genes through the RNAi pathway. Following these findings, we construct an innovative and comprehensive database to elucidate siRNA-mediated mechanism in human transcribed pseudogenes (TPGs).

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BBBomics

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Abstract is not available.[1]

 

 

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RegNetwork

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Transcriptional and post-transcriptional regulation of gene expression is of fundamental importance to numerous biological processes. Nowadays, an increasing amount of gene regulatory relationships have been documented in various databases and literature. However, to more efficiently exploit such knowledge for biomedical research and applications, it is necessary to construct a genome-wide regulatory network database to integrate the information on gene regulatory relationships that are widely scattered in many different places.

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WSNF

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Identifying cancer subtypes is an important component of the personalised medicine framework. An increasing number of computational methods have been developed to identify cancer subtypes. However, existing methods rarely use information from gene regulatory networks to facilitate the subtype identification. It is widely accepted that gene regulatory networks play crucial roles in understanding the mechanisms of diseases. Different cancer subtypes are likely caused by different regulatory mechanisms.

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