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Survival Analysis

Survival analysis is a branch of statistics for analyzing the expected duration of time until one or more events happen, such as death in biological organisms and failure in mechanical systems. This topic is called reliability theory or reliability analysis in engineering, duration analysis or duration modelling in economics, and event history analysis in sociology. [Source: Wikipedia]

BreastMark

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

Breast cancer is a complex heterogeneous disease for which a substantial resource of transcriptomic data is available. Gene expression data have facilitated the division of breast cancer into, at least, five molecular subtypes, namely luminal A, luminal B, HER2, normal-like and basal. Once identified, breast cancer subtypes can inform clinical decisions surrounding patient treatment and prognosis. Indeed, it is important to identify patients at risk of developing aggressive disease so as to tailor the level of clinical intervention.

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Mirsynergy

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

Identification of microRNA regulatory modules (MiRMs) will aid deciphering aberrant transcriptional regulatory network in cancer but is computationally challenging. Existing methods are stochastic or require a fixed number of regulatory modules.

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MIRUMIR

Submitted by ChenLiang on Tue, 01/09/2018 - 19:19

Abstract is not available.[1]

 

 

 

 

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OvMark

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

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.

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icTAIR

Submitted by ChenLiang on Thu, 04/06/2017 - 17:57

Gene expression regulators, such as transcription factors (TFs) and microRNAs (miRNAs), have varying regulatory targets based on the tissue and physiological state (context) within which they are expressed. While the emergence of regulator-characterizing experiments has inferred the target genes of many regulators across many contexts, methods for transferring regulator target genes across contexts are lacking. Further, regulator target gene lists frequently are not curated or have permissive inclusion criteria, impairing their use.

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TissGDB

Submitted by ChenLiang on Tue, 01/09/2018 - 19:09

Tissue-specific gene expression is critical in understanding biological processes, physiological conditions, and disease. The identification and appropriate use of tissue-specific genes (TissGenes) will provide important insights into disease mechanisms and organ-specific therapeutic targets.

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dbPHCC

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

Hepatocellular carcinoma (HCC) is one of the most common malignant cancers with a poor prognosis. For decades, more and more biomarkers were found to effect on HCC prognosis, but these studies were scattered and there were no unified identifiers. Therefore, we built the database of prognostic biomarkers and models for hepatocellular carcinoma (dbPHCC).

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BiCliques Merging

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

MicroRNAs (miRNAs) are post-transcriptional regulators that repress the expression of their targets. They are known to work cooperatively with genes and play important roles in numerous cellular processes. Identification of miRNA regulatory modules (MRMs) would aid deciphering the combinatorial effects derived from the many-to-many regulatory relationships in complex cellular systems. Here, we develop an effective method called BiCliques Merging (BCM) to predict MRMs based on bicliques merging.

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4
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dChip

Submitted by ChenLiang on Thu, 04/06/2017 - 17:42

Genome-wide expression signatures are emerging as potential marker for overall survival and disease recurrence risk as evidenced by recent commercialization of gene expression based biomarkers in breast cancer. Similar predictions have recently been carried out using genome-wide copy number alterations and microRNAs. Existing software packages for microarray data analysis provide functions to define expression-based survival gene signatures.

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OncomiR

Submitted by ChenLiang on Tue, 01/09/2018 - 17:26

Dysregulation of microRNAs (miRNAs) is extensively associated with cancer development and progression. miRNAs have been shown to be biomarkers for predicting tumor formation and outcome. However, identification of the relationships between miRNA expression and tumor characteristics can be difficult and time-consuming without appropriate bioinformatics expertise. To address this issue, we present OncomiR, an online resource for exploring miRNA dysregulation in cancer.

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