Support vector machine classification and validation of cancer
  tissue samples using microarray expression data

Furey, Terrence S.; Cristianini, Nello; Duffy, Nigel; Bednarski, David W.; Schummer, Michèl; Haussler, David

doi:10.1093/bioinformatics/16.10.906

Cited by 2,090 publications

(1,100 citation statements)

References 27 publications

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“…After learning the features of the class, the SVM recognizes unknown samples as a member of a specific class. SVMs have been shown to perform especially well in multiple areas of biological analyses, especially functional class prediction from microarray gene expression data and chemometrics (24)(25)(26)(27)(28). We constructed an SVM classifier with a nonlinear algorithm with Matlab (version 6.5) (Mathworks, Natick, MA) using the training set of sensor response data from subjects with lung cancer, subjects with noncancer disease, and healthy control subjects.…”

Section: Svm Analysismentioning

confidence: 99%

Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath

Machado

Laskowski²,

Deffenderfer³

et al. 2005

Am J Respir Crit Care Med

532

429

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Rationale: Electronic noses are successfully used in commercial applications, including detection and analysis of volatile organic compounds in the food industry. Objectives: We hypothesized that the electronic nose could identify and discriminate between lung diseases, especially bronchogenic carcinoma. Methods: In a discovery and training phase, exhaled breath of 14 individuals with bronchogenic carcinoma and 45 healthy control subjects or control subjects without cancer was analyzed. Principal components and canonic discriminant analysis of the sensor data was used to determine whether exhaled gases could discriminate between cancer and noncancer. Discrimination between classes was performed using Mahalanobis distance. Support vector machine analysis was used to create and apply a cancer prediction model prospectively in a separate group of 76 individuals, 14 with and 62 without cancer. Main Results: Principal components and canonic discriminant analysis demonstrated discrimination between samples from patients with lung cancer and those from other groups. In the validation study, the electronic nose had 71.4% sensitivity and 91.9% specificity for detecting lung cancer; positive and negative predictive values were 66.6 and 93.4%, respectively. In this population with a lung cancer prevalence of 18%, positive and negative predictive values were 66.6 and 94.5%, respectively. Conclusion: The exhaled breath of patients with lung cancer has distinct characteristics that can be identified with an electronic nose. The results provide feasibility to the concept of using the electronic nose for managing and detecting lung cancer.

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Section: Svm Analysismentioning

confidence: 99%

Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath

Machado

Laskowski²,

Deffenderfer³

et al. 2005

Am J Respir Crit Care Med

532

429

View full text Add to dashboard Cite

show abstract

“…The discovery of diseases subtypes defined by gene expression data may lead to more refined predictions than classical clinical correlates in terms of correct diagnosis, survival, disease-free survival and disease recurrence [11][12][13]. Moreover the definition of subtypes of diseases on molecular basis may help to develop therapies targeted to the bio-molecular characteristics of patients [14], and to design automatic classification methods for supporting diagnostic procedures [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses

Bertoni

Valentini

2006

Artificial Intelligence in Medicine

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Objective: Clustering algorithms may be applied to the analysis of DNA microarray data to identify novel subgroups that may lead to new taxonomies of diseases defined at bio-molecular level. A major problem related to the identification of biologically meaningful clusters is the assessment of their reliability, since clustering algorithms may find clusters even if no structure is present. Methodology:Recently, methods based on random "perturbations" of the data, such as bootstrapping, noise injections techniques and random subspace methods have been applied to the problem of cluster validity estimation. In this framework, we propose stability measures that exploits the high dimensionality of DNA microarray data and the redundancy of information stored in microarray chips. To this end we randomly project the original gene expression data into lower dimensional subspaces, approximately preserving the distance between the examples according to the Johnson-Lindenstrauss (JL) theory. The stability of the clusters discovered in the original high dimensional space is estimated by comparing them with the clusters discovered in randomly projected lower dimensional subspaces. The proposed cluster-stability measures may be applied to validate and to quantitatively assess the reliability of the clusters obtained by a large class of clustering algorithms. Results and conclusion:We tested the effectiveness of our approach with high dimensional synthetic data, whose distribution is a priori known, showing that the stability measures based on randomized maps correctly predict the number of clusters and the reliability of each individual cluster. Then we showed how to apply the proposed measures to the analysis of DNA microarray data, whose underlying distribution is unknown. We evaluated the validity of clusters discovered by hierarchical clustering algorithms in diffuse large B-cell lymphoma (DLBCL) and malignant melanoma patients, showing that the proposed reliability measures can support bio-medical researchers in the identification of stable clusters of patients and in the discovery of new subtypes of diseases characterized at bio-molecular level.

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“…6 As a new and powerful modeling tool, support vector machine (SVM) has gained much interest in pattern recognition and function approximation applications recently. In bioinformatics, SVMs have been successfully used to solve classification and correlation problems, such as cancer diagnosis, [7][8][9][10] identification of HIV protease cleavage sites, 11 protein class prediction, 12 etc. SVMs have also been applied in chemistry, for example, the prediction of retention index of protein, and other QSAR studies.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression

Yao

Panaye

Doucet

et al. 2004

J. Chem. Inf. Comput. Sci.

222

115

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Support vector machines (SVMs) were used to develop QSAR models that correlate molecular structures to their toxicity and bioactivities. The performance and predictive ability of SVM are investigated and compared with other methods such as multiple linear regression and radial basis function neural network methods. In the present study, two different data sets were evaluated. The first one involves an application of SVM to the development of a QSAR model for the prediction of toxicities of 153 phenols, and the second investigation deals with the QSAR model between the structures and the activities of a set of 85 cyclooxygenase 2 (COX-2) inhibitors. For each application, the molecular structures were described using either the physicochemical parameters or molecular descriptors. In both studied cases, the predictive ability of the SVM model is comparable or superior to those obtained by MLR and RBFNN. The results indicate that SVM can be used as an alternative powerful modeling tool for QSAR studies.

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Support vector machine classification and validation of cancer tissue samples using microarray expression data

Abstract: The SVM software is available at http://www.cs. columbia.edu/ approximately bgrundy/svm.

Cited by 2,090 publications

References 27 publications

Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath

Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath

Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses

Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression

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