ArticleGenome biology2008
Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function.
Article in Genome biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 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
46 citing papers in PubMed.
- A machine learning classifier to identify and prioritise genes associated with murine cardiac development.PLoS genetics · 2026Article
- Fibrinaloid Microclots and Atrial Fibrillation.Biomedicines · 2024Review
- Transcriptomic analysis reveals mitochondrial pathways associated with distinct adolescent behavioral phenotypes and stress response.Translational psychiatry · 2023Article
- Identification of hypoxia-related genes and exploration of their relationship with immune cells in ischemic stroke.Scientific reports · 2023Article
- Ferroptosis-related genes as diagnostic markers for major depressive disorder and their correlations with immune infiltration.Frontiers in medicine · 2023Article
- DapBCH: a disease association prediction model Based on Cross-species and Heterogeneous graph embedding.Frontiers in genetics · 2023Article
- SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks.BMC bioinformatics · 2022Article
- Identification and functional annotation of long intergenic non-coding RNAs in Brassicaceae.The Plant cell · 2022Article
- IID 2021: towards context-specific protein interaction analyses by increased coverage, enhanced annotation and enrichment analysis.Nucleic acids research · 2022Article
- The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets.Nucleic acids research · 2021Article
- Identifying functionally relevant candidate genes for inflexible ethanol intake in mice and humans using a guilt-by-association approach.Brain and behavior · 2020Article
- Natural display of nuclear-encoded RNA on the cell surface and its impact on cell interaction.Genome biology · 2020Article
- Hierarchy and levels: analysing networks to study mechanisms in molecular biology.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020Article
- ISOGO: Functional annotation of protein-coding splice variants.Scientific reports · 2020Article
- The Genetic Perspective of Familial Glucocorticoid Deficiency:International journal of endocrinology · 2020Article
- Unified feature association networks through integration of transcriptomic and proteomic data.PLoS computational biology · 2019Article
- IID 2018 update: context-specific physical protein-protein interactions in human, model organisms and domesticated species.Nucleic acids research · 2019Article
- The evolutionary signal in metagenome phyletic profiles predicts many gene functions.Microbiome · 2018Article
- The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible.Nucleic acids research · 2017Article
- Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.Nature methods · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundLearning the function of genes is a major goal of computational genomics. Methods for inferring gene function have typically fallen into two categories: 'guilt-by-profiling', which exploits correlation between function and other gene characteristics; and 'guilt-by-association', which transfers function from one gene to another via biological relationships.
resultsWe have developed a strategy ('Funckenstein') that performs guilt-by-profiling and guilt-by-association and combines the results. Using a benchmark set of functional categories and input data for protein-coding genes in Saccharomyces cerevisiae, Funckenstein was compared with a previous combined strategy. Subsequently, we applied Funckenstein to 2,455 Gene Ontology terms. In the process, we developed 2,455 guilt-by-profiling classifiers based on 8,848 gene characteristics and 12 functional linkage graphs based on 23 biological relationships.
conclusionFunckenstein outperforms a previous combined strategy using a common benchmark dataset. The combination of 'guilt-by-profiling' and 'guilt-by-association' gave significant improvement over the component classifiers, showing the greatest synergy for the most specific functions. Performance was evaluated by cross-validation and by literature examination of the top-scoring novel predictions. These quantitative predictions should help prioritize experimental study of yeast gene functions.
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.