Feature construction from synergic pairs to improve microarray-based classification
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Summary
A new dimension reduction and feature construction method, called FeatKNN, which assesses interactions between expression profiles to improve microarray-based classification accuracy and shows experimentally that the interactional information has a degree of significance comparable to that of the gene expression profiles considered separately.
- Type
- article
- Published
- 2007-11-01
- Cited by
- 25
- References
- 26
- Access
- Open access
- OpenAlex
- https://openalex.org/W2150126969
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:7786279
Keywords
Feature (linguistics), Computer science, Microarray analysis techniques, Data mining, Microarray databases
References
- Scoring Genes for Relevance
- New feature subset selection procedures for classification of expression profiles
- Classification of microarray data using gene networks
- Improving classification of microarray data using prototype-based feature selection
- Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
- Physical nature of higher-order mutual information: intrinsic correlations and frustration
- Gene selection from microarray data for cancer classification - a machine learning approach
- Boosting the margin: A new explanation for the effectiveness of voting methods
- The mathematical theory of communication
- Cellular survival pathways and resistance to cancer therapy.
- Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements.
- All in the CCN family: essential matricellular signaling modulators emerge from the bunker
- DNMT1 and DNMT3b cooperate to silence genes in human cancer cells
- The mutual information: Detecting and evaluating dependencies between variables
- Selection bias in gene extraction on the basis of microarray gene-expression data
- Support vector machine classification and validation of cancer tissue samples using microarray expression data
- Is cross-validation valid for small-sample microarray classification?
- Minimum redundancy feature selection from microarray gene expression data
- An extensive comparison of recent classification tools applied to microarray data
- Overfitting in Making Comparisons Between Variable Selection Methods
Cited by
- A whole-blood RNA transcript-based prognostic model in men with castration-resistant prostate cancer: a prospective study.
- Maximum weight and minimum redundancy: A novel framework for feature subset selection
- SlimPLS: A Method for Feature Selection in Gene Expression-Based Disease Classification
- A new histogram-based estimation technique of entropy and mutual information using mean squared error minimization
- Protein-protein Interaction Reveals Synergistic Discrimination of Cancer Phenotype
- A Study of Network-based Approach for Cancer Classification
- Voting features based classifier with feature construction and its application to predicting financial distress
- Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets
- Automating Microarray Classification Using General Regression Neural Networks
- The Role of Proxy Genes in Predictive Models: An Application to Early Detection of Prostate Cancer
- Abstraction in Artificial Intelligence and Complex Systems
- Construction of synergy networks from gene expression data related to disease.
- Discovering Pair-wise Synergies in Microarray Data
- Exploiting document feature interactions for efficient information fusion in high dimensional spaces
- Features Identification for Phenotypic Classification Based on Genes and Gene Pairs
- GENVISAGE: Rapid Identification of Discriminative and Explainable Feature Pairs for Genomic Analysis
- Uncovering Effective Explanations for Interactive Genomic Data Analysis
- Optimizing use of multi-antibody assays for Lyme disease diagnosis: A bioinformatic approach
- Integration of single‐cell and bulk RNA‐sequencing data reveals the prognostic potential of epithelial gene markers for prostate cancer
- Class-Add, une procédure de sélection de variables basée sur une troncature k-additive de l'information mutuelle et sur une classification ascendante hiérarchique en pré-traitement
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