Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method

By Leping Li, Clarice R. Weinberg, T. Darden, Lee G. Pedersen. Bioinform., 2001

704 citations recorded by the source corpus.

Subfields: Information Systems, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/608ba959f3281276fb7d611002663d8ffebb60af

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Citations in the corpus

Markov blanket-embedded genetic algorithm for gene selection (2007, methodology): “In this study, we determine the robustness using Z-score analysis [7,11].”
Text Classification Algorithms: A Survey (2019, methodology): “Multiple KNN documents may belong to the same category; in this case, the summation of these scores would be the similarity score of class k with respect to the test document x.”
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (2010, methodology): “Whole research programs are, in many domains, built on experiments motivated by causal hypotheses that were generated by non-causal feature selection results (Zhou et al., 2002; Li et al., 2001; Holmes et al., 2000; Eisen et al., 1998) and this seems an unfortunate and inadvisable practice, in light”
Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments (2009, neutral): “Early clustering analysis of microarray data were mostly on static microarray experiments, such as hierarchial clustering [8], the k-nearest neighbors method [9,10], and other correlation-based methods [11,12].”
Cluster analysis for gene expression data: a survey (2004, background): “After about [24, 42] informative genes which manifest the phenotype partition within the training samples are selected, the whole set of samples are clustered using only the informative genes as features.”
A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue (2007, methodology): “Unsupervised learning methods, including the hierarchical cluster [4,7], self-organizing maps [8], fuzzy adaptive resonance theory (ART) [9,10] and K-means clustering [11], are widely used in functional assignment of novel genes, marker genes identification and classes discovery.”
Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework (2007, methodology): “In this paper, we propose a novel wrapper-filter feature selection algorithm (WFFSA) using a memetic framework [11-16], i.e., a combination of genetic algorithm (GA) [ 17-20 ] and local search (LS).”
Feature selection methods for big data bioinformatics: A survey from the search perspective. (2016, neutral): “sort of averaging or majority vote by these different FS methods [153,154].”
Building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis (2011, methodology): “Two basic approaches for feature selection used in machine learning and information theory literature are the filter methods and the wrapper methods [9,20,20,21].”