ArticleHuman genomics and proteomics : HGP2009
Data integration in genetics and genomics: methods and challenges.
Article in Human genomics and proteomics : HGP, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 6 of them syntheses that pooled it.
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
74 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Meta-analysis of endometrial transcriptome data reveals novel molecular targets for recurrent implantation failure.Journal of assisted reproduction and genetics · 2024Pooled it
- Functional Requirements for Medical Data Integration into Knowledge Management Environments: Requirements Elicitation Approach Based on Systematic Literature Analysis.Journal of medical Internet research · 2023Pooled it
- Identifying novel host-based diagnostic biomarker panels for COVID-19: a whole-blood/nasopharyngeal transcriptome meta-analysis.Molecular medicine (Cambridge, Mass.) · 2022Pooled it
- Meta-analysis of gene expression disease signatures in colonic biopsy tissue from patients with ulcerative colitis.Scientific reports · 2021Pooled it
- Horizontal meta-analysis identifies common deregulated genes across AML subgroups providing a robust prognostic signature.Blood advances · 2020Pooled it
- Pooled it
- Reproducible Tools and Enhanced Computational Workflows for Batch Effect Evaluation of High-Throughput Data Using BatchQC.bioRxiv : the preprint server for biology · 2026Article
- Conversational Artificial Intelligence for Integrating Social Determinants, Genomics, and Clinical Data in Precision Medicine: Development and Implementation Study of the AI-HOPE-PM System.JMIR bioinformatics and biotechnology · 2025Article
- CCNV: a user-friendly R package enabling large-scale cumulative copy number variation analyses of DNA methylation data.BMC bioinformatics · 2025Article
- Paradigm Lost.Cancers · 2025Article
- Integrating genetic and gene expression data in network-based stratification analysis of cancers.BMC bioinformatics · 2025Article
- A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data.PLoS computational biology · 2025Article
- The Use of AI for Phenotype-Genotype Mapping.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Reversing the decline of threatened koala (Ecology and evolution · 2024Article
- Deeply integrating latent consistent representations in high-noise multi-omics data for cancer subtyping.Briefings in bioinformatics · 2024Article
- Classifying breast cancer using multi-view graph neural network based on multi-omics data.Frontiers in genetics · 2024Article
- Prediction of anti-TNF therapy failure in ulcerative colitis patients by ensemble machine learning: A prospective study.Heliyon · 2023Article
- Bioinformatic Analysis of miR-200b/429 and Hub Gene Network in Cervical Cancer.Biochemical genetics · 2023Article
- Multi-view clustering by CPS-merge analysis with application to multimodal single-cell data.PLoS computational biology · 2023Article
- Crowdsourcing temporal transcriptomic coronavirus host infection data: Resources, guide, and novel insights.Biology methods & protocols · 2023Article
14 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to rapid technological advances, various types of genomic and proteomic data with different sizes, formats, and structures have become available. Among them are gene expression, single nucleotide polymorphism, copy number variation, and protein-protein/gene-gene interactions. Each of these distinct data types provides a different, partly independent and complementary, view of the whole genome. However, understanding functions of genes, proteins, and other aspects of the genome requires more information than provided by each of the datasets. Integrating data from different sources is, therefore, an important part of current research in genomics and proteomics. Data integration also plays important roles in combining clinical, environmental, and demographic data with high-throughput genomic data. Nevertheless, the concept of data integration is not well defined in the literature and it may mean different things to different researchers. In this paper, we first propose a conceptual framework for integrating genetic, genomic, and proteomic data. The framework captures fundamental aspects of data integration and is developed taking the key steps in genetic, genomic, and proteomic data fusion. Secondly, we provide a review of some of the most commonly used current methods and approaches for combining genomic data with focus on the statistical aspects.
Identifiers
What OpenQuestion holds
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.