ATHENA: the analysis tool for heritable and environmental network associations

Holzinger, Emily R.; Dudek, Scott M.; Frase, Alex T.; Pendergrass, Sarah A.; Ritchie, Marylyn D.

doi:10.1093/bioinformatics/btt572

Cited by 48 publications

(35 citation statements)

References 28 publications

(26 reference statements)

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“…Thus, ATHENA provides three key functions: (1) performing feature selection from categorical or continuous independent variables; (2) modeling single variable and/or interaction effects to predict categorical or continuous clinical outcomes; (3) annotating the candidate models for the interpretation in translational bioinformatics [19, 24, 51]. ATHENA contains several subcomponents: preprocessing, modeling, and an evolutionary-algorithm based machine learning technique at its core (Fig.…”

Section: Methodsmentioning

confidence: 99%

Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer

Kim

Dudek

et al. 2015

Journal of Biomedical Informatics

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Evaluation of survival models to predict cancer patient prognosis is one of the most important areas of emphasis in cancer research. A binary classification approach has difficulty directly predicting survival due to the characteristics of censored observations and the fact that the predictive power depends on the threshold used to set two classes. In contrast, the traditional Cox regression approach has some drawbacks in the sense that it does not allow for the identification of interactions between genomic features, which could have key roles associated with cancer prognosis. In addition, data integration is regarded as one of the important issues in improving the predictive power of survival models since cancer could be caused by multiple alterations through meta-dimensional genomic data including genome, epigenome, transcriptome, and proteome. Here we have proposed a new integrative framework designed to perform these three functions simultaneously: (1) predicting censored survival data; (2) integrating meta-dimensional omics data; (3) identifying interactions within/between meta-dimensional genomic features associated with survival. In order to predict censored survival time, martingale residuals were calculated as a new continuous outcome and a new fitness function used by the grammatical evolution neural network (GENN) based on mean absolute difference of martingale residuals was implemented. To test the utility of the proposed framework, a simulation study was conducted, followed by an analysis of meta-dimensional omics data including copy number, gene expression, DNA methylation, and protein expression data in breast cancer retrieved from The Cancer Genome Atlas (TCGA). On the basis of the results from breast cancer dataset, we were able to identify interactions not only within a single dimension of genomic data but also between meta-dimensional omics data that are associated with survival. Notably, the predictive power of our best meta-dimensional model was 73% which outperformed all of the other models conducted based on a single dimension of genomic data. Breast cancer is an extremely heterogeneous disease and the high levels of genomic diversity within/between breast tumors could affect the risk of therapeutic responses and disease progression. Thus, identifying interactions within/between meta-dimensional omics data associated with survival in breast cancer is expected to deliver direction for improved meta-dimensional prognostic biomarkers and therapeutic targets.

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Section: Methodsmentioning

confidence: 99%

Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer

Kim

Dudek

et al. 2015

Journal of Biomedical Informatics

Self Cite

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“…Briefly, one option for drawing inferences from these data involves pairwise analyses of data sets, mounting evidence to support a signal. However, analysing three or more data sets simultaneously requires more sophisticated multi-dimensional methods, such as Bayesian models 74 , neural networks 75 or dimensionality reduction 76 . This is further complicated by the fact that various omics data types are fundamentally different: for instance, genetic variation data are discrete and static, whereas RNA-seq measurements are continuous and can provide longitudinal information.…”

Section: Challengesmentioning

confidence: 99%

Integrative omics for health and disease

2018

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Advances in omics technologies — such as genomics, transcriptomics, proteomics and metabolomics — have begun to enable personalized medicine at an extraordinarily detailed molecular level. Individually, these technologies have contributed medical advances that have begun to enter clinical practice. However, each technology individually cannot capture the entire biological complexity of most human diseases. Integration of multiple technologies has emerged as an approach to provide a more comprehensive view of biology and disease. In this Review, we discuss the potential for combining diverse types of data and the utility of this approach in human health and disease. We provide examples of data integration to understand, diagnose and inform treatment of diseases, including rare and common diseases as well as cancer and transplant biology. Finally, we discuss technical and other challenges to clinical implementation of integrative omics.

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“…The most commonly used example of multi-stage integration is expression-quantitative trait loci (eQTL) analysis, wherein single nucleotide polymorphisms (SNPs) are associated with changes in gene expression, which in turn are associated with disease [8, 9]. Meta-dimensional techniques consist of integrated models, in which all data are used as part of a joint model or analysis, which might involve joint regression, or integration at the level of individual models [10, 11]. …”

Section: Introductionmentioning

confidence: 99%

A Methylation-to-Expression Feature Model for Generating Accurate Prognostic Risk Scores and Identifying Disease Targets in Clear Cell Kidney Cancer

Thompson

Marsit

2016

Biocomputing 2017

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Many researchers now have available multiple high-dimensional molecular and clinical datasets when studying a disease. As we enter this multi-omic era of data analysis, new approaches that combine different levels of data (e.g. at the genomic and epigenomic levels) are required to fully capitalize on this opportunity. In this work, we outline a new approach to multi-omic data integration, which combines molecular and clinical predictors as part of a single analysis to create a prognostic risk score for clear cell renal cell carcinoma. The approach integrates data in multiple ways and yet creates models that are relatively straightforward to interpret and with a high level of performance. Furthermore, the proposed process of data integration captures relationships in the data that represent highly disease-relevant functions.

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ATHENA: the analysis tool for heritable and environmental network associations

Abstract: ATHENA is freely available for download. The software, user manual and tutorial can be downloaded from http://ritchielab.psu.edu/ritchielab/software.

Cited by 48 publications

References 28 publications

Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer

Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer

Integrative omics for health and disease

A Methylation-to-Expression Feature Model for Generating Accurate Prognostic Risk Scores and Identifying Disease Targets in Clear Cell Kidney Cancer

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