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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]

miRmine

Submitted by ChenLiang on Thu, 04/06/2017 - 19:25

MicroRNAs (miRNAs) are small non-coding RNAs that are involved in post-transcriptional regulation of gene expression. In this high-throughput sequencing era, a tremendous amount of RNA-seq data is accumulating, and full utilization of publicly available miRNA data is an important challenge. These data are useful to determine expression values for each miRNA, but quantification pipelines are in a primitive stage and still evolving; there are many factors that affect expression values significantly.

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iBFE

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

High-throughput biotechnologies have been widely used to characterize clinical samples from various perspectives e.g., epigenomics, genomics and transcriptomics. However, because of the heterogeneity of these technologies and their outputs, individual analysis of the various types of data is hard to create a comprehensive view of disease subtypes. Integrative methods are of pressing need.

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miR_Path

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

MicroRNAs (miRNAs) are short non-coding RNAs that play important roles in post-transcriptional regulations as well as other important biological processes. Recently, accumulating evidences indicate that miRNAs are extensively involved in cancer. However, it is a big challenge to identify which miRNAs are related to which cancer considering the complex processes involved in tumors, where one miRNA may target hundreds or even thousands of genes and one gene may regulate multiple miRNAs.

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Radiogenomics

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

Magnetic Resonance Imaging (MRI) has been routinely used for the diagnosis and treatment of breast cancer. However, the relationship between the MRI tumor phenotypes and the underlying genetic mechanisms remains under-explored. We integrated multi-omics molecular data from The Cancer Genome Atlas (TCGA) with MRI data from The Cancer Imaging Archive (TCIA) for 91 breast invasive carcinomas.

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