Dataset Information

HDMAC: A Web-Based Interactive Program for High-Dimensional Analysis of Molecular Alterations in Cancer.

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.

SUBMITTER: Chang C

PROVIDER: S-EPMC7054321 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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HDMAC: A Web-Based Interactive Program for High-Dimensional Analysis of Molecular Alterations in Cancer.

Chang Chung C Sung Chan-Yu CY Hsiao Han H Chen Jiabin J Chen I-Hsuan IH Kuo Wei-Ting WT Cheng Lung-Feng LF Korla Praveen Kumar PK Chung Ming-Jhe MJ Wu Pei-Jhen PJ Yu Chia-Cheng CC Sheu Jim Jinn-Chyuan JJ

Scientific reports 20200303 1

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 ...[more]

PMID: 32127576

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Project description:IntroductionIncreased physical exercise is linked to enhanced brain health and reduced dementia risk. Exercise intervention studies usually are conducted at facilities in groups under trainer supervision. To improve scalability, accessibility, and engagement, programs may need to be structured such that individuals can execute and adjust routines in their own homes.MethodsOne hundred eighty-three healthy older adults from two sites (the United States and Sweden) were screened. One hundred fifty-six subjects (mean age 73.2), randomly assigned to one of four interventions (PACE-Yourself physical exercise program, mindfulness meditation, or Cogmed® adaptive or nonadaptive computerized working memory training) began the study. All interventions were structurally similar: occurring in subjects' homes using interactive, web-based software, over five weeks, ∼175 minutes/week. In the PACE-Yourself program, video segments presented aerobic exercises at different pace and intensity (P&I). The program paused frequently, allowing subjects to indicate whether P&I was "too easy," "too hard," or "somewhat hard." P&I of the subsequent exercise set was adjusted, allowing subjects to exercise at a perceived exertion level of "somewhat hard." Program completion was defined as finishing ≥60% of sessions.ResultsA high percentage of participants in all groups completed the program, although the number (86%) was slightly lower in the PACE-Yourself group than the other three. Excluding dropouts, the PACE-Yourself group had a lower adherence rate of 93%, compared with the other three (∼98%). Over the five weeks, PACE-Yourself participants increased exercising at the highest intensity level, consistent with augmented aerobic activity over time. The number of exercise sessions completed predicted the postintervention versus preintervention increase in self-reported level of physical activity.DiscussionThis study supports the feasibility of a home-based, subject-controlled, exercise program in which P&I is regulated via real-time participant feedback, which may promote self-efficacy. Further study is needed to determine if similar results are found over longer periods and in more diverse populations.

Dataset Information

HDMAC: A Web-Based Interactive Program for High-Dimensional Analysis of Molecular Alterations in Cancer.

Publications

HDMAC: A Web-Based Interactive Program for High-Dimensional Analysis of Molecular Alterations in Cancer.

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