ArticleProceedings of the National Academy of Sciences of the United States of America2005
A data integration methodology for systems biology.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 153 papers, 5 of them syntheses that pooled it.
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Who cites it
153 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- MAOA, childhood maltreatment, and antisocial behavior: meta-analysis of a gene-environment interaction.Biological psychiatry · 2014Pooled it
- Pig immune response to general stimulus and to porcine reproductive and respiratory syndrome virus infection: a meta-analysis approach.BMC genomics · 2013Pooled it
- Strengthening insights into host responses to mastitis infection in ruminants by combining heterogeneous microarray data sources.BMC genomics · 2011Pooled it
- The serotonin transporter promoter variant (5-HTTLPR), stress, and depression meta-analysis revisited: evidence of genetic moderation.Archives of general psychiatry · 2011Pooled it
- Bacterial postgenomics: the promise and peril of systems biology.Journal of bacteriology · 2006Pooled it
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- BOGO: A Proteome-Wide Gene Overexpression Platform for Discovering Rational Cancer Combination Therapies.bioRxiv : the preprint server for biology · 2025Article
- Proteogenomic characterization of molecular and cellular targets for treatment-resistant subtypes in locally advanced cervical cancers.Molecular cancer · 2025Article
- Low tristetraprolin expression activates phenotypic plasticity and primes transition to lethal prostate cancer in mice.The Journal of clinical investigation · 2024Article
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- ONECUT2 Activates Diverse Resistance Drivers of Androgen Receptor-Independent Heterogeneity in Prostate Cancer.bioRxiv : the preprint server for biology · 2023Article
- DDM1-mediated gene body DNA methylation is associated with inducible activation of defense-related genes in Arabidopsis.Genome biology · 2023Article
- Engineered Mesenchymal Stem Cells Over-Expressing BDNF Protect the Brain from Traumatic Brain Injury-Induced Neuronal Death, Neurological Deficits, and Cognitive Impairments.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Proteogenomic landscape of human pancreatic ductal adenocarcinoma in an Asian population reveals tumor cell-enriched and immune-rich subtypes.Nature cancer · 2023Article
- Editorial: Insights in human and medical genomics: 2022.Frontiers in genetics · 2023Article
- A systematic exploration of ginsenoside Rg5 reveals anti-inflammatory functions in airway mucosa cells.Journal of ginseng research · 2023Article
- Article
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93 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Different experimental technologies measure different aspects of a system and to differing depth and breadth. High-throughput assays have inherently high false-positive and false-negative rates. Moreover, each technology includes systematic biases of a different nature. These differences make network reconstruction from multiple data sets difficult and error-prone. Additionally, because of the rapid rate of progress in biotechnology, there is usually no curated exemplar data set from which one might estimate data integration parameters. To address these concerns, we have developed data integration methods that can handle multiple data sets differing in statistical power, type, size, and network coverage without requiring a curated training data set. Our methodology is general in purpose and may be applied to integrate data from any existing and future technologies. Here we outline our methods and then demonstrate their performance by applying them to simulated data sets. The results show that these methods select true-positive data elements much more accurately than classical approaches. In an accompanying companion paper, we demonstrate the applicability of our approach to biological data. We have integrated our methodology into a free open source software package named POINTILLIST.
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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.