ArticleScientific reports2020
HDMAC: A Web-Based Interactive Program for High-Dimensional Analysis of Molecular Alterations in Cancer.
Article in Scientific reports, 2020. 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, 5 citations in OpenAlex.
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in high-throughput genomic technologies have nurtured a growing demand for statistical tools to facilitate identification of molecular changes as potential prognostic biomarkers or drugable targets for personalized precision medicine. In this study, we developed a web-based interactive and user-friendly platform for high-dimensional analysis of molecular alterations in cancer (HDMAC) (https://ripsung26.shinyapps.io/rshiny/). On HDMAC, several penalized regression models that are suitable for high-dimensional data analysis, Ridge, Lasso and adaptive Lasso, are offered, with Cox regression for survival and logistic regression for binary outcomes. Choice of a first-step screening is provided to address the multiple-comparison issue that often arises with large-volume genomic data. Hazard ratio or estimated coefficient is provided with each selected gene so that a multivariate regression model may be built based on the genes selected. Cross validation is provided as the method to estimate the prediction power of each regression model. In addition, R codes are also provided to facilitate download of whole sets of molecular variables from TCGA. In this study, illustration of the use of HDMAC was made through a set of data on gene mutations and a set on mRNA expression from ovarian cancer patients and a set on mRNA expression from bladder cancer patient. From the analysis of each set of data, a list of candidate genes was obtained that might be associated with mutations or abnormal expression of genes in ovarian and bladder cancers. HDMAC offers a solution for rigorous and validation analysis of high-dimensional genomic data.
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