Multi-class tumor classification by discriminant partial least squares using microarray gene expression data and assessment of classification models

Tan, Yan‐Xi; Shi, Leming; Tong, Weida; Hwang, Gyeongjo; Wang, Charles

doi:10.1016/j.compbiolchem.2004.05.002

Cited by 81 publications

(56 citation statements)

References 29 publications

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“…In the case of genetic expression data sets (such as the one analysed in Fig. 4(d)), different steps of preprocessing commonly used are filtering, thresholding, log normalisation and gene selection Tan et al [23]. The latter is done in order to reduce the dimensionality of the feature space, by discarding redundant information.…”

Section: Discussionmentioning

confidence: 99%

“…For the Iris Setosa data, the DifFUZZY and FCM ROC curves correspond to perfect classifications, with both curves going through the (0,1) corner; both methods classify all those points correctly, and do not assign other points to that cluster (zero false positives), but for the Iris Versicolor and Iris Virginica data, DifFUZZY performs better than FCM, since its curves pass closer to the upper left corner. Genetic expression data set: We tested DifFUZZY on the publicly available Leukaemia data set (Golub et al [13]), which contains genetic expression data from patients diagnosed with either of two different types of leukaemia: acute myeloid leukaemia (AML) or acute lymphoblastic leukaemia (ALL) (Tan et al [23]). This data set, composed of 7129 genes, was obtained from an Affimetrix high-density oligonucleotide microarray.…”

Section: Biological Data Setsmentioning

confidence: 99%

“…Since our method is unsupervised we merged both sets obtaining a set with data from 72 patients: 25 with AML, 47 with ALL. Before testing our clustering method on the Leukaemia data set we pre-processed the data as done by Tan et al [23]. The gene selection procedure consisted of selecting the Q genes with the highest squared correlation coefficient sums (Tan et al [23]), where Q corresponds to the number of genes to be selected, which for the case of this data set was set to be 70.…”

Section: Biological Data Setsmentioning

confidence: 99%

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DifFUZZY: a fuzzy clustering algorithm for complex datasets

Cominetti

Matzavinos

Samarasinghe

et al. 2010

IJCIBSB

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Soft (fuzzy) clustering techniques are often used in the study of high-dimensional data sets, such as microarray and other high-throughput bioinformatics data. The most widely used method is the Fuzzy C-means algorithm (FCM), but it can present difficulties when dealing with some data sets. A fuzzy clustering algorithm, DifFUZZY, which utilises concepts from diffusion processes in graphs and is applicable to a larger class of clustering problems than other fuzzy clustering algorithms is developed. Examples of data sets (synthetic and real) for which this method outperforms other frequently used algorithms are presented, including two benchmark biological data sets, a genetic expression data set and a data set that contains taxonomic measurements. This method is better than traditional fuzzy clustering algorithms at handling data sets that are "curved", elongated or those which contain clusters of different dispersion. The algorithm has been implemented in Matlab and C++ and is available at http://www.maths.ox.ac.uk/cmb/difFUZZY.

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Section: Discussionmentioning

confidence: 99%

Section: Biological Data Setsmentioning

confidence: 99%

Section: Biological Data Setsmentioning

confidence: 99%

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DifFUZZY: a fuzzy clustering algorithm for complex datasets

Cominetti

Matzavinos

Samarasinghe

et al. 2010

IJCIBSB

View full text Add to dashboard Cite

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“…In addition, some direct approaches are available to develop multiclass extension of traditional binary classifiers, such as multicategory SVMs, which was developed by Lee and Lee (2003) and often lead to a complex optimization problem. Tan et al (2004) applied discriminant partial least squares to predict the categories for multiclass samples and Berrar et al (2006) constructed an instance-based multiclass microarray data classification approach. Both Li et al (2004) and Statnikov et al (2005) performed systematic and comprehensive evaluation of several major multiclass classification methods for microarray data and concluded that OVR-SVM outperforms other approaches because it has averagely higher classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Methodology Multiclass microarray data classification based on confidence evaluation

Yu¹,

Gao²,

Qin³

et al. 2012

Genet. Mol. Res.

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ABSTRACT. Microarray technology is becoming a powerful tool for clinical diagnosis, as it has potential to discover gene expression patterns that are characteristic for a particular disease. To date, this possibility has received much attention in the context of cancer research, especially in tumor classification. However, most published articles have concentrated on the development of binary classification methods while neglected ubiquitous multiclass problems. Unfortunately, only a few multiclass classification approaches have had poor predictive accuracy. In an effort to improve classification accuracy, we developed a novel multiclass microarray data classification method. First, we applied a "one versus rest-support vector machine" to classify the samples. Then the classification confidence of each testing sample was evaluated according to its distribution in feature space and some with poor confidence were extracted. Next, a novel strategy, which we named as "class priority estimation method based on centroid distance", was used to make decisions about categories for those poor confidence samples. This approach was tested on seven benchmark multiclass microarray datasets, with encouraging results, demonstrating effectiveness and feasibility.

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“…Other work related to the analysis of Microarray data using dimensionality reduction techniques include [15], [11] and [21]. In [15] a semi-parametric approach is used to produce generalised linear models reducing the dimension, [11] uses graph theoretical methods to aid the search for models of reduced dimension and [21] uses discriminant partial least squares to provide models with more explanation of the response variables than might arise from the standard PCA method.…”

Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction and Microarray Data

Elizondo

Passow

Birkenhead

et al. 2008

Lecture Notes in Computational Science and Enginee

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Summary. Microarrays are being currently used for the expression levels of thousands of genes simultaneously. They present new analytical challenges because they have a very high input dimension and a very low sample size. It is highly complex to analyse multi-dimensional data with complex geometry and to identify lowdimensional "principal objects" that relate to the optimal projection while losing the least amount of information. Several methods have been proposed for dimensionality reduction of microarray data. Some of these methods include principal component analysis and principal manifolds. This article presents a comparison study of the performance of the linear principal component analysis and the non linear local tangent space alignment principal manifold methods on such a problem. Two microarray data sets will be used in this study. A classification model will be created using fully dimensional and dimensionality reduced data sets. To measure the amount of information lost with the two dimensionality reduction methods, the level of performance of each of the methods will be measured in terms of level of generalisation obtained by the classification models on previously unseen data sets. These results will be compared with the ones obtained using the fully dimensional data sets.

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Multi-class tumor classification by discriminant partial least squares using microarray gene expression data and assessment of classification models

Cited by 81 publications

References 29 publications

DifFUZZY: a fuzzy clustering algorithm for complex datasets

DifFUZZY: a fuzzy clustering algorithm for complex datasets

Methodology Multiclass microarray data classification based on confidence evaluation

Dimensionality Reduction and Microarray Data

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