ArticleF1000Research2012
Progress and challenges in the computational prediction of gene function using networks.
Article in F1000Research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed.
- Cayman enables large-scale analysis of gut microbiome carbohydrate-active enzyme repertoires.Nature microbiology · 2026Article
- Identification of growth regulators using cross-species network analysis in plants.Plant physiology · 2022Article
- "Guilt by association" is not competitive with genetic association for identifying autism risk genes.Scientific reports · 2021Article
- CommWalker: correctly evaluating modules in molecular networks in light of annotation bias.Bioinformatics (Oxford, England) · 2018Article
- Exploration of gene functions for esophageal squamous cell carcinoma using network-based guilt by association principle.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2018Article
- Text mining in cancer gene and pathway prioritization.Cancer informatics · 2014Review
- Parametric Bayesian priors and better choice of negative examples improve protein function prediction.Bioinformatics (Oxford, England) · 2013Article
- Progress and challenges in the computational prediction of gene function using networks: 2012-2013 update.F1000Research · 2013Article
- Characterizing the state of the art in the computational assignment of gene function: lessons from the first critical assessment of functional annotation (CAFA).BMC bioinformatics · 2013Article
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
In this opinion piece, we attempt to unify recent arguments we have made that serious confounds affect the use of network data to predict and characterize gene function. The development of computational approaches to determine gene function is a major strand of computational genomics research. However, progress beyond using BLAST to transfer annotations has been surprisingly slow. We have previously argued that a large part of the reported success in using "guilt by association" in network data is due to the tendency of methods to simply assign new functions to already well-annotated genes. While such predictions will tend to be correct, they are generic; it is true, but not very helpful, that a gene with many functions is more likely to have any function. We have also presented evidence that much of the remaining performance in cross-validation cannot be usefully generalized to new predictions, making progressive improvement in analysis difficult to engineer. Here we summarize our findings about how these problems will affect network analysis, discuss some ongoing responses within the field to these issues, and consolidate some recommendations and speculation, which we hope will modestly increase the reliability and specificity of gene function prediction.
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.