ReviewBriefings in bioinformatics2012
Network biology methods integrating biological data for translational science.
Review in Briefings in bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed, 63 citations in OpenAlex.
- In Silico Analysis of Squamous Cell Carcinoma.Advances in experimental medicine and biology · 2026Review
- Metabolic reprogramming in sepsis-associated encephalopathy: emerging mechanisms, candidate biomarkers, and future therapeutic directions.Frontiers in medicine · 2026Review
- The mirror like expression of genes involved in the FOXO signaling pathway could be effective in the pathogenesis of human lymphotropic virus type 1 (HTLV-1) through disruption of the downstream pathways.BMC research notes · 2023Article
- From classical mendelian randomization to causal networks for systematic integration of multi-omics.Frontiers in genetics · 2022Review
- Alveolar Regeneration in COVID-19 Patients: A Network Perspective.International journal of molecular sciences · 2021Review
- Identifying disease trajectories with predicate information from a knowledge graph.Journal of biomedical semantics · 2020Article
- On fusion methods for knowledge discovery from multi-omics datasets.Computational and structural biotechnology journal · 2020Review
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.An international journal on information fusion · 2019Article
- Integrative Deep Learning for Identifying Differentially Expressed (DE) Biomarkers.Computational and mathematical methods in medicine · 2019Article
- Bipartite graphs in systems biology and medicine: a survey of methods and applications.GigaScience · 2018Review
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- Community metabolic modeling approaches to understanding the gut microbiome: Bridging biochemistry and ecology.Free radical biology & medicine · 2017Review
- Computational challenges in modeling gene regulatory events.Transcription · 2016Article
- Omics databases on kidney disease: where they can be found and how to benefit from them.Clinical kidney journal · 2016Review
- Methods for biological data integration: perspectives and challenges.Journal of the Royal Society, Interface · 2015Review
- From Molecules to Patients: The Clinical Applications of Translational Bioinformatics.Yearbook of medical informatics · 2015Article
- Identification of possible pathogenic pathways in Behçet's disease using genome-wide association study data from two different populations.European journal of human genetics : EJHG · 2015Article
- Regularized machine learning in the genetic prediction of complex traits.PLoS genetics · 2014Article
- A network perspective on unraveling the role of TRP channels in biology and disease.Pflugers Archiv : European journal of physiology · 2014Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 2 countries.
Funding
Abstract
The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.
Indexed as
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.