ArticleNucleic acids research2004
Gene mining: a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling.
Article in Nucleic acids research, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 96 citations in OpenAlex.
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- Identifying SM-miRNA associations based on layer attention graph convolutional network and matrix decomposition.Frontiers in molecular biosciences · 2022Article
- Feature Selection and Feature Stability Measurement Method for High-Dimensional Small Sample Data Based on Big Data Technology.Computational intelligence and neuroscience · 2021Article
- Metagenomic Analysis of Silage.Journal of visualized experiments : JoVE · 2017Article
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- Network topology measures for identifying disease-gene association in breast cancer.BMC bioinformatics · 2016Article
- Identifying ultrasound and clinical features of breast cancer molecular subtypes by ensemble decision.Scientific reports · 2015Article
- Feature Import Vector Machine: A General Classifier with Flexible Feature Selection.Statistical analysis and data mining · 2015Article
- Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm.BMC bioinformatics · 2014Article
- A robust hybrid approach based on estimation of distribution algorithm and support vector machine for hunting candidate disease genes.TheScientificWorldJournal · 2013Article
- To control false positives in gene-gene interaction analysis: two novel conditional entropy-based approaches.PloS one · 2013Article
- Genome-scale screening of drug-target associations relevant to Ki using a chemogenomics approach.PloS one · 2013Article
- Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification.BMC bioinformatics · 2012Article
- Mining functional gene modules linked with rheumatoid arthritis using a SNP-SNP network.Genomics, proteomics & bioinformatics · 2012Article
- Characterizing genetic interactions in human disease association studies using statistical epistasis networks.BMC bioinformatics · 2011Article
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- Construction and application of an electronic spatiotemporal expression profile and gene ontology analysis platform based on the EST database of the silkworm, Bombyx mori.Journal of insect science (Online) · 2010Article
- A modified ant colony optimization algorithm for tumor marker gene selection.Genomics, proteomics & bioinformatics · 2009Article
- SubpathwayMiner: a software package for flexible identification of pathways.Nucleic acids research · 2009Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current applications of microarrays focus on precise classification or discovery of biological types, for example tumor versus normal phenotypes in cancer research. Several challenging scientific tasks in the post-genomic epoch, like hunting for the genes underlying complex diseases from genome-wide gene expression profiles and thereby building the corresponding gene networks, are largely overlooked because of the lack of an efficient analysis approach. We have thus developed an innovative ensemble decision approach, which can efficiently perform multiple gene mining tasks. An application of this approach to analyze two publicly available data sets (colon data and leukemia data) identified 20 highly significant colon cancer genes and 23 highly significant molecular signatures for refining the acute leukemia phenotype, most of which have been verified either by biological experiments or by alternative analysis approaches. Furthermore, the globally optimal gene subsets identified by the novel approach have so far achieved the highest accuracy for classification of colon cancer tissue types. Establishment of this analysis strategy has offered the promise of advancing microarray technology as a means of deciphering the involved genetic complexities of complex diseases.
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