ArticleBioinformatics (Oxford, England)2010
Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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.
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Who cites it
12 citing papers in PubMed.
- MoNETA: MultiOmics Network Embedding for SubType Analysis.NAR genomics and bioinformatics · 2024Article
- Development and verification of a combined diagnostic model for primary Sjögren's syndrome by integrated bioinformatics analysis and machine learning.Scientific reports · 2023Article
- Machine learning: its challenges and opportunities in plant system biology.Applied microbiology and biotechnology · 2022Review
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.An international journal on information fusion · 2019Article
- Synstable Fusion: A Network-Based Algorithm for Estimating Driver Genes in Fusion Structures.Molecules (Basel, Switzerland) · 2018Article
- FusionPathway: Prediction of pathways and therapeutic targets associated with gene fusions in cancer.PLoS computational biology · 2018Article
- A comparison of graph- and kernel-based -omics data integration algorithms for classifying complex traits.BMC bioinformatics · 2017Article
- Identification of functional modules by integration of multiple data sources using a Bayesian network classifier.Circulation. Cardiovascular genetics · 2014Article
- Identification of cancer fusion drivers using network fusion centrality.Bioinformatics (Oxford, England) · 2013Article
- Systems genetics in "-omics" era: current and future development.Theory in biosciences = Theorie in den Biowissenschaften · 2013Review
- Article
- Bayesian inference for genomic data integration reduces misclassification rate in predicting protein-protein interactions.PLoS computational biology · 2011Article
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.
Funding
Abstract
motivationThree major problems confront the construction of a human genetic network from heterogeneous genomics data using kernel-based approaches: definition of a robust gold-standard negative set, large-scale learning and massive missing data values.
resultsThe proposed graph-based approach generates a robust GSN for the training process of genetic network construction. The RVM-based ensemble model that combines AdaBoost and reduced-feature yields improved performance on large-scale learning problems with massive missing values in comparison to Naïve Bayes. CONTACT: dargenio@bmsr.usc.edu SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.
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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.