Tissue Classification with Gene Expression Profiles

By A. Ben-Dor, L. Bruhn, Nir Friedman, Iftach Nachman, Michèl Schummer, Zohar Yakhini. Annual International Conference on Research in Computational Molecular Biology, 2000

836 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6ed30fb64cdbc3bc5f6662f746e203cc2786c239

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Citations in the corpus

A review of feature selection techniques in bioinformatics (2007, methodology): “(2000) who use the threshold number of misclassification (TNoM, see further in the section on microarray analysis) to score genes for relevance to tissue classification.”
A Review of Ensemble Methods in Bioinformatics (2010, methodology): “In contrast to the results obtained by Dudoit et al. and Ben-Dor et al. , the follow up studies revealed that much better results can be achieved through minor tuning and modification.”
CLICK and EXPANDER: a system for clustering and visualizing gene expression data (2003, methodology): “The algorithm builds on the HCS algorithm of Hartuv and Shamir (2000).”
Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production (2009, methodology): “Other methods have been based on classifying gene expression by similarity to expression patterns corresponding to known metabolic or cellular states [50–52].”
From Boolean to probabilistic Boolean networks as models of genetic regulatory networks (2002, background): “For example, cancer classification using a variety of methods has been used to exploit the class-separating power of expression data: leukemias [6], various cancers [7], small, round, blue-cell cancers [8], and hereditary breast cancer [9].”
Gene expression data analysis (2000, neutral): “[24] applied a new clustering algorithm for classification of colon and ovarian cancer data sets.”
Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method (2001, methodology): “Other computational methods that select a subset of genes for sample classification have also been developed (Golub et al., 1999; Ben-Dor et al., 2000).”
Ensemble machine learning on gene expression data for cancer classification. (2003, methodology): “Thus, machine learning and other artificial intelligence techniques have been widely used to classify or characterise gene expression data (Golub et al 1999; Ben-dor et al 2000; Brazma and Vilo 2000; Brown et al 2000; Li and Wong 2002; Shipp et al 2002).”
Filter versus wrapper gene selection approaches in DNA microarray domains (2004, neutral): “) [10,11, 37,38], support vector machines [8,18,39], neural networks [40,41] and K-NN [3,11,16,42] are the most broadly used supervised class prediction procedures in microarray domains.”
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection (2016, methodology): “) [49] Pairwise constraint score (Must Link and Cannot Link) with Laplacian score & Colon [64]; KNN Kendall's coefficient; Rank sum; accuracy rate & Leukemia [67]; (Benabdeslem and Hindawi, 2011) [147] Constrained Laplacian score (CLS) & Colon [64] ; (Benabdeslem and Hindawi, 2013) [146] Constrained”
Model-Based Clustering, Discriminant Analysis, and Density Estimation (2002, neutral): “There is also considerable interest in discriminant analysis, or supervised classi cation, of gene expression data (e.g., Chu et al. 1998; Wasserman and Fickett 1998; Golub et al. 1999; Brown et al. 2000; Ben-Dor et al. 2000; Hastie et al. 2000a, 2000b; West et al. 2000).”
Analysis of Gene Expression Data Using BRB-Array Tools (2007, methodology): “Most comparative studies have shown that simple classifiers generally perform at least as well as more complex methods for p>>n settings (Ben-Dor et al., 2000, Dudoit et al., 2002).”
Support vector machine classification and validation of cancer tissue samples using microarray expression data (2000, methodology): “…(Brown et al., 2000; Mukherjee et al., 1999) clustering methods (Eisen et al., 1998; Spellman et al., 1998; Alon et al., 1999; Perou et al., 1999; Ben-Dor et al., 2000; Hastie et al., 2000), self-organizing maps (Tamayo et al., 1999; Golub et al., 1999), and a weighted correlation method (Golub…”
A mixture model-based approach to the clustering of microarray expression data (2002, background): “Alon et al. (1999) and Ben-Dor et al. (2000) noted that the normal colon biopsy also included smooth muscle tissue from the colon walls.”
A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression (2004, background): “However, while binary classification and clustering are heavily studied, (see, e.g. Alizadeh et al., 2000; Ben-Dor et al., 2000; Brown et al., 2000; Der et al., 1998; Eisen et al., 1998; Friedman et al., 2000; Raychaudhuri et al., 2002; Tamayo et al., 1999; Welsh et al., 2001), only a small amount o”
Improving molecular cancer class discovery through sparse non-negative matrix factorization (2005, neutral): “…have been proposed for molecular cancer classification using gene expression data (Alon et al., 1999; Golub et al., 1999; Alizadeh et al., 2000; Ben-Dor et al., 2000; Bittner et al., 2000; Ross et al., 2000; Slonim et al., 2000; Pomeroy et al., 2002; Nguyen and Rocke, 2002; Brunet et al.,…”
Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data (2005, neutral): “2001), the Threshold Number of Misclassification (TNOM) score (Ben-Dor et al. 2000), the Wilcoxon test statistic (Dettling 2004) and many others.”