ArticlePharmacogenomics2012
Integrating heterogeneous high-throughput data for meta-dimensional pharmacogenomics and disease-related studies.
Article in Pharmacogenomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- A parametric survival model with bayesian structural equation based on multi-omics integration.BMC bioinformatics · 2025Article
- A practical introduction to holo-omics.Cell reports methods · 2024Review
- Classifying breast cancer using multi-view graph neural network based on multi-omics data.Frontiers in genetics · 2024Article
- Drug-disease association prediction with literature based multi-feature fusion.Frontiers in pharmacology · 2023Article
- Computational approaches for network-based integrative multi-omics analysis.Frontiers in molecular biosciences · 2022Review
- Pan-transcriptome assembly combined with multiple association analysis provides new insights into the regulatory network of specialized metabolites in the tea plantHorticulture research · 2022Article
- Multi-Staged Data-Integrated Multi-Omics Analysis for Symptom Science Research.Biological research for nursing · 2021Article
- Studying the Exposome to Understand the Environmental Determinants of Complex Liver Diseases.Hepatology (Baltimore, Md.) · 2020Review
- A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification.GigaScience · 2019Article
- Tightly integrated genomic and epigenomic data mining using tensor decomposition.Bioinformatics (Oxford, England) · 2019Article
- integRATE: a desirability-based data integration framework for the prioritization of candidate genes across heterogeneous omics and its application to preterm birth.BMC medical genomics · 2018Article
- Mini-Review: The Contribution of Intermediate Phenotypes to GxE Effects on Disorders of Body Composition in the New OMICS Era.International journal of environmental research and public health · 2017Review
- Using omics approaches to understand pulmonary diseases.Respiratory research · 2017Review
- Methods of integrating data to uncover genotype-phenotype interactions.Nature reviews. Genetics · 2015Review
- Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis.Computational and structural biotechnology journal · 2015Review
- Molecular network analysis enhances understanding of the biology of mental disorders.BioEssays : news and reviews in molecular, cellular and developmental biology · 2014Review
- ATHENA: the analysis tool for heritable and environmental network associations.Bioinformatics (Oxford, England) · 2014Article
- ATHENA: a tool for meta-dimensional analysis applied to genotypes and gene expression data to predict HDL cholesterol levels.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2013Article
- The success of pharmacogenomics in moving genetic association studies from bench to bedside: study design and implementation of precision medicine in the post-GWAS era.Human genetics · 2012Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The current paradigm of human genetics research is to analyze variation of a single data type (i.e., DNA sequence or RNA levels) to detect genes and pathways that underlie complex traits such as disease state or drug response. While these studies have detected thousands of variations that associate with hundreds of complex phenotypes, much of the estimated heritability, or trait variability due to genetic factors, remain unexplained. We may be able to account for a portion of the missing heritability if we incorporate a systems biology approach into these analyses. Rapid technological advances will make it possible for scientists to explore this hypothesis via the generation of high-throughput omics data - transcriptomic, proteomic and methylomic to name a few. Analyzing this 'meta-dimensional' data will require clever statistical techniques that allow for the integration of qualitative and quantitative predictor variables. For this article, we examine two major categories of approaches for integrated data analysis, give examples of their use in experimental and in silico datasets, and assess the limitations of each method.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.