ArticleBioinformation2013
Search for signatures in miRNAs associated with cancer.
Article in Bioinformation, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Handling class imbalance problem in miRNA dataset associated with cancer.Bioinformation · 2015Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since the first discovery in the early 1990's, the predicted and validated population of microRNAs (miRNAs or miRs) has grown significantly. These small (~22 nucleotides long) regulators of gene expression have been implicated and associated with several genes in the cancer pathway as well. Globally, the identification and verification of microRNAs as biomarkers for cancer cell types has been the area of thrust for most miRNA biologists. However, there has been a noticeable vacuum when it comes to identifying a common signature or trademark that could be used to demarcate a miR to be associated with the development or suppression of cancer. To answer these queries, we report an in silico study involving the identification of global signatures in experimentally validated microRNAs which have been associated with cancer. This study has thrown light on the presence of significant common signatures, viz., - sequential and hybridization, which may distinguish a miR to be associated with cancer. Based on our analysis, we suggest the utility of such signatures in the design and development of algorithms for prediction of miRs involved in the cancer pathway.
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