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Expression Profiles

gene expression profiling is the measurement of the activity (the expression) of thousands of genes at once, to create a global picture of cellular function. These profiles can, for example, distinguish between cells that are actively dividing, or show how the cells react to a particular treatment. Many experiments of this sort measure an entire genome simultaneously, that is, every gene present in a particular cell. [Source: Wikipedia]

miRNA-ensemble

Submitted by ChenLiang on Mon, 01/09/2017 - 10:36

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.

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activeMiRNA

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

Identifying microRNA signatures for the different types and subtypes of cancer can result in improved detection, characterization and understanding of cancer and move us towards more personalized treatment strategies. However, using microRNA's differential expression (tumour versus normal) to determine these signatures may lead to inaccurate predictions and low interpretability because of the noisy nature of miRNA expression data. We present a method for the selection of biologically active microRNAs using gene expression data and microRNA-to-gene interaction network.

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SlideBase

Submitted by ChenLiang on Mon, 01/09/2017 - 11:33

Genomics consortia have produced large datasets profiling the expression of genes, micro-RNAs, enhancers and more across human tissues or cells. There is a need for intuitive tools to select subsets of such data that is the most relevant for specific studies. To this end, we present SlideBase, a web tool which offers a new way of selecting genes, promoters, enhancers and microRNAs that are preferentially expressed/used in a specified set of cells/tissues, based on the use of interactive sliders.

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FlyAtlas

Submitted by ChenLiang on Tue, 01/09/2018 - 18:24

FlyAtlas 2 ( www.flyatlas2.org ) is part successor, part complement to the FlyAtlas database and web application for studying the expression of the genes of Drosophila melanogaster in different tissues of adults and larvae.

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SCLC

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

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.

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GEISHA

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

With sequencing of the chicken genome largely completed, significant effort is focusing on gene annotation, including acquiring information about the patterns of gene expression. The chicken embryo is ideally suited to provide detailed temporal and spatial expression information through in situ hybridization gene expression analysis in vivo.

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MethHC

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

We present MethHC (http://MethHC.mbc.nctu.edu.tw), a database comprising a systematic integration of a large collection of DNA methylation data and mRNA/microRNA expression profiles in human cancer. DNA methylation is an important epigenetic regulator of gene transcription, and genes with high levels of DNA methylation in their promoter regions are transcriptionally silent. Increasing numbers of DNA methylation and mRNA/microRNA expression profiles are being published in different public repositories.

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BioVLAB-MMIA

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

MicroRNAs, by regulating the expression of hundreds of target genes, play critical roles in developmental biology and the etiology of numerous diseases, including cancer. As a vast amount of microRNA expression profile data are now publicly available, the integration of microRNA expression data sets with gene expression profiles is a key research problem in life science research.

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MIRAGAA

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

Cancer evolves through microevolution where random lesions that provide the biggest advantage to cancer stand out in their frequent occurrence in multiple samples. At the same time, a gene function can be changed by aberration of the corresponding gene or modification of microRNA (miRNA) expression, which attenuates the gene. In a large number of cancer samples, these two mechanisms might be distributed in a coordinated and almost mutually exclusive manner.

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UFFizi

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

Feature selection is an important pre-processing task in the analysis of complex data. Selecting an appropriate subset of features can improve classification or clustering and lead to better understanding of the data. An important example is that of finding an informative group of genes out of thousands that appear in gene-expression analysis. Numerous supervised methods have been suggested but only a few unsupervised ones exist.

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