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82
Minimum redundancy feature selection from microarray gene expression data. Journal of bioinformatics and computational biology
, 2005
"... Selecting a small subset of genes out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the topranked genes. We observe that feature sets ..."
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Cited by 118 (7 self)
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Selecting a small subset of genes out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the topranked genes. We observe that feature sets so obtained have certain redundancy and study methods to minimize it. Feature sets obtained through the minimum redundancy – maximum relevance framework represent broader spectrum of characteristics of phenotypes than those obtained through standard ranking methods; they are more robust, generalize well to unseen data, and lead to significantly improved classifications in extensive experiments on 5 gene expressions data sets. 1.
Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces
 Journal of Machine Learning Research
, 2004
"... We propose a novel method of dimensionality reduction for supervised learning problems. Given a regression or classification problem in which we wish to predict a response variable Y from an explanatory variable X, we treat the problem of dimensionality reduction as that of finding a lowdimensional ..."
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Cited by 117 (26 self)
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We propose a novel method of dimensionality reduction for supervised learning problems. Given a regression or classification problem in which we wish to predict a response variable Y from an explanatory variable X, we treat the problem of dimensionality reduction as that of finding a lowdimensional “effective subspace ” for X which retains the statistical relationship between X and Y. We show that this problem can be formulated in terms of conditional independence. To turn this formulation into an optimization problem we establish a general nonparametric characterization of conditional independence using covariance operators on reproducing kernel Hilbert spaces. This characterization allows us to derive a contrast function for estimation of the effective subspace. Unlike many conventional methods for dimensionality reduction in supervised learning, the proposed method requires neither assumptions on the marginal distribution of X, nor a parametric model of the conditional distribution of Y. We present experiments that compare the performance of the method with conventional methods.
Overview and recent advances in partial least squares
 in ‘Subspace, Latent Structure and Feature Selection Techniques’, Lecture Notes in Computer Science
, 2006
"... Partial Least Squares (PLS) is a wide class of methods for modeling relations between sets of observed variables by means of latent variables. It comprises of regression and classification tasks as well as dimension reduction techniques and modeling tools. The underlying assumption of all PLS method ..."
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Cited by 54 (4 self)
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Partial Least Squares (PLS) is a wide class of methods for modeling relations between sets of observed variables by means of latent variables. It comprises of regression and classification tasks as well as dimension reduction techniques and modeling tools. The underlying assumption of all PLS methods is that the
Prediction by supervised principal components
 Journal of the American Statistical Association
, 2006
"... In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory results. We describe a technique called supervised principal components that can be applied to this type of problem. Supervised principal co ..."
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Cited by 54 (6 self)
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In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory results. We describe a technique called supervised principal components that can be applied to this type of problem. Supervised principal components is similar to conventional principal components analysis except that it uses a subset of the predictors selected based on their association with the outcome. Supervised principal components can be applied to regression and generalized regression problems, such as survival analysis. It compares favorably to other techniques for this type of problem, and can also account for the effects of other covariates and help identify which predictor variables are most important. We also provide asymptotic consistency results to help support our empirical findings. These methods could become important tools for DNA microarray data, where they may be used to more accurately diagnose and treat cancer. KEY WORDS: Gene expression; Microarray; Regression; Survival analysis. 1.
Effective dimension reduction methods for tumor classification using gene expression data
 Bioinformatics
, 2003
"... Motivation: One particular application of microarray data, is to uncover the molecular variation among cancers. One feature of microarray studies is the fact that the number n of samples collected is relatively small compared to the number p of genes per sample which are usually in the thousands. In ..."
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Cited by 35 (2 self)
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Motivation: One particular application of microarray data, is to uncover the molecular variation among cancers. One feature of microarray studies is the fact that the number n of samples collected is relatively small compared to the number p of genes per sample which are usually in the thousands. In statistical terms this very large number of predictors compared to a small number of samples or observations makes the classification problem difficult. An efficient way to solve this problem is by using dimension reduction statistical techniques in conjunction with nonparametric discriminant procedures. Results: We view the classification problem as a regression problem with few observations and many predictor variables. We use an adaptive dimension reduction method for generalized semiparametric regression models that allows us to solve the ‘curse of dimensionality problem ’ arising in the context of expression data. The predictive performance of the resulting classification rule is illustrated on two well know data sets in the microarray literature: the leukemia data that is known to contain classes that are easy ‘separable ’ and the colon data set. Availability: Software that implements the procedures on which this paper focus are freely available at
Linear regression and twoclass classification with gene expression data
 Bioinformatics
, 2003
"... Motivation: Using gene expression data to classify (or predict) tumor types has received much research attention recently. Due to some special features of gene expression data, several new methods have been proposed, including the weighted voting scheme of Golub et al., the compound covariate method ..."
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Cited by 31 (2 self)
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Motivation: Using gene expression data to classify (or predict) tumor types has received much research attention recently. Due to some special features of gene expression data, several new methods have been proposed, including the weighted voting scheme of Golub et al., the compound covariate method of Hedenfalk et al. (originally proposed by Tukey), and the shrunken centroids method of Tibshirani et al. These methods look different and are more or less ad hoc. Results: We point out a close connection of the three methods with a linear regression model. Casting the classification problem in the general framework of linear regression naturally leads to new alternatives, such as partial least squares (PLS) methods and penalized PLS (PPLS) methods. Using two real data sets, we show the competitive performance of our new methods when compared with the other three methods. Contact:
High dimensional classification using features annealed independence rules
 Ann. Statist
, 2008
"... ABSTRACT. Classification using highdimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or other highthroughput data. The impact of dimensionality on classifications is largely poorly understood. In a seminal paper, Bickel an ..."
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Cited by 27 (8 self)
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ABSTRACT. Classification using highdimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or other highthroughput data. The impact of dimensionality on classifications is largely poorly understood. In a seminal paper, Bickel and Levina (2004) show that the Fisher discriminant performs poorly due to diverging spectra and they propose to use the independence rule to overcome the problem. We first demonstrate that even for the independence classification rule, classification using all the features can be as bad as the random guessing due to noise accumulation in estimating population centroids in highdimensional feature space. In fact, we demonstrate further that almost all linear discriminants can perform as bad as the random guessing. Thus, it is paramountly important to select a subset of important features for highdimensional classification, resulting in Features Annealed Independence Rules (FAIR). The conditions under which all the important features can be selected by the twosample tstatistic are established. The choice of the optimal number of features, or equivalently, the threshold value of the test statistics are proposed based on an upper bound of the classification error. Simulation studies and real data analysis support our theoretical results and demonstrate convincingly the advantage of our new classification procedure.
Partial least squares: A versatile tool for the analysis of highdimensional genomic data
 Briefings in Bioinformatics
, 2007
"... Partial Least Squares (PLS) is a highly efficient statistical regression technique that is well suited for the analysis of highdimensional genomic data. In this paper we review the theory and applications of PLS both under methodological and biological points of view. Focusing on microarray express ..."
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Cited by 26 (7 self)
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Partial Least Squares (PLS) is a highly efficient statistical regression technique that is well suited for the analysis of highdimensional genomic data. In this paper we review the theory and applications of PLS both under methodological and biological points of view. Focusing on microarray expression data we provide a systematic comparison of the PLS approaches currently employed, and discuss problems as different as tumor classification, identification of relevant genes, survival analysis and modeling of gene networks. 2 1
Bayesian model averaging: development of an improved multiclass, gene selection and classification tool for microarray data
, 2005
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PLS dimension reduction for classification with microarray data
 STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY 3, ISSUE 1, ARTICLE 33
, 2004
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