Classification Of Tissue Samples Research Articles

Multiclass Response Feature Selection and Cancer Tumour Classification With Support Vector Machine

Background & Aim: In this study, efficient Support Vector Machine (SVM) algorithm for feature selection and classification of multi-category tumour classes of biological samples using gene expression profiles was proposed. Methods: Feature selection interface of the algorithm employed the F-statistic of the ANOVA–like testing scheme at some chosen family-wise-error-rate which ensured efficient detection of false-positive genes. The selected gene subsets using the above method were further screened for optimality using the Misclassification Error Rates yielded by each of them and their combinations in a sequential selection manner. In a 10-fold cross-validation, the optimal values of the SVM parameters with appropriate kernel were determined for tissue sample classification using one-versus-all approach. The entire data matrix was randomly partitioned into 95% training set to train the SVM classifier and 5% test set to evaluate the predictive performance of the classifier over 1,000 Monte-Carlo cross-validation runs. Published microarray breast cancer dataset with five clinical endpoints was employed to validate the results from the simulation studies. Results: Results from Monte-Carlo study showed excellent performance of the SVM classifier with higher prediction accuracy of the tissue samples based on the few gene biomarkers selected by the proposed feature selection method. Conclusion: SVM could be considered as a classification of multi-category tumour classes of biological

Real time FTIR spectroscopy of tissue smears from gynecologic cancer tissues: A preliminary study.

e17089 Background: Women with suspected Gynecologic cancer undergo surgical procedures during which tissue from suspected areas is excised. Fast histopathology analysis is performed intra-operatively using frozen section (FS) analysis, results of which are available within less than an hour. However, the accuracy of the FS test ranges between 75% and 100% when compared to final histopathology diagnoses. Fourier transform Infrared (FTIR) spectroscopy, utilized for classification of tissue samples into malignant and benign tumors, has shown comparable results to those of FS histopathology analysis. However, the sample preparation time and the effects of tissue preparation on the measured spectra have been a concern for the utilization of this technique in clinical practice. In this study we used attenuated total reflection (ATR) FTIR spectroscopy to examine fresh tissue impression smears as an alternative to the FS technique for rapid classification of tissue samples obtained during surgery. Methods: The study was approved by relevant ethics committees and was conducted in accordance with the Declaration of Helsinki. All patients provided written, informed consent. In total, 23 biopsies (ovarian and uterine) were extracted from suspected tumor sites during surgical procedures and sent to the histopathology laboratory for both pathological and FTIR analyses. Results of the histopathology analysis classified 15 samples as benign and 8 samples as malignant. Prior to the histopathologic analysis, tissue samples from these tumors were lightly pressed against the surface of an ATR crystal, leaving on its' surface impression smears. These smears were air dried for ~5 minutes. Mid-IR absorbance spectra were collected using an ATR-FTIR spectrometer. Machine learning techniques (PCA-LDA and SVM) were utilized to build discrimination models from the absorbance data of the measured smears. Sensitivity and specificity were calculated. Results: IR absorbance spectra of malignant smears were consistently higher from spectra of benign smears in the 850cm-1 to 1450 cm-1 range and they were consistently lower in the 3200cm-1 to 3600cm-1 range. The PCA-LDA discrimination model correctly classified the samples with a sensitivity and specificity of 100%, and the SVM showed a training accuracy of 100% and a cross validation accuracy of 91.3%. Conclusions: These preliminary results suggest that ATR-FTIR spectroscopy of tissue smears may have an important role in the development of next-generation techniques for intra-operative tumor classification.

Evolutionary Computational Algorithm by Blending of PPCA and EP-Enhanced Supervised Classifier for Microarray Gene Expression Data

In DNA microarray technology, gene classification is considered to be difficult because the attributes of the data, are characterized by high dimensionality and small sample size. Classification of tissue samples in such high dimensional problems is a complicated task. Furthermore, there is a high redundancy in microarray data and several genes comprise inappropriate information for accurate classification of diseases or phenotypes. Consequently, an efficient classification technique is necessary to retrieve the gene information from the microarray experimental data. In this paper, a classification technique is proposed that classifies the microarray gene expression data well. In the proposed technique, the dimensionality of the gene expression dataset is reduced by Probabilistic PCA. Then, an Artificial Neural Network (ANN) is selected as the supervised classifier and it is enhanced using Evolutionary programming (EP) technique. The enhancement of the classifier is accomplished by optimizing the dimension of the ANN. The enhanced classifier is trained using the Back Propagation (BP) algorithm and so the BP error gets minimized. The well-trained ANN has the capacity of classifying the gene expression data to the associated classes. The proposed technique is evaluated by classification performance over the cancer classes, Acute myeloid leukemia (AML) and Acute Lymphoblastic Leukemia (ALL). The classification performance of the enhanced ANN classifier is compared over the existing ANN classifier and SVM classifier.

Cancer tissue sample classification using point symmetry-based clustering algorithm

Cancer tissue sample classification using point symmetry-based clustering algorithm

Cancer tissue sample classification using point symmetry-based clustering algorithm

Clustering or unsupervised classification techniques can be used to solve different types of classification problems of different domains. Symmetry is an important property for any real life object. Therefore, symmetry-based distance measurements play some important roles in identifying some patterns or clusters of real life datasets. In this paper, inspired by the symmetric property, we have proposed a point symmetry-based clustering algorithm which has been used to identify clusters of tissue samples from some real life cancer datasets. Our proposed algorithm is also multi-objective-optimisation (MOO) based, i.e., optimises more than one objectives simultaneously. We have also shown the superiority of our proposed algorithm with respect to some state-of-the-art clustering algorithms.

Fuzzy based Feature Selection Scheme through Transductive SVM Technique for Cancer Pattern Classification and Prediction

Objectives: A reliable and precise classification of tumor types is of great importance and essential for successful Diagnosis and Drug Discovery. Methods: Gene expression profiling has shown a great prospective in the outcome prediction of different types of Cancers, with the innovation of Microarray Technology. Microarray cancer data, which have been organized as samples versus genes fashion, are being exploited for the tissue sample classification. They are also useful for identifying potential gene markers in each subtype of cancer, that helps to analyze a particular cancer type in a successful manner. Findings: In this paper a new method for classification based on Fuzzy Rough-Set Feature Selection Approach through Transductive SVM Technique, which is called as FFS+TSVM is proposed. Moreover recently, for Cancer Pattern Classification, a combined Consistency based Feature Selection Approach through Transductive Support Vector Machine (CBFS+TSVM) has proposed and its prediction accuracy has encouraged than that of existing other Classifiers. However, from the literature survey, it is revealed that the performance of the existing scheme can be improved if Fuzzy Rough Set Approach for Gene Feature Selection with Transductive Support Vector Machine is employed. The present work is implemented with the help of Bio Weka and studied thoroughly in terms of Computational Cost, Dimensionality Reduction, Threshold, Classification Error and Classification Accuracy. Applications/Improvements: The proposed work outperforms the existing Transductive Support Vector Machine (TSVM) in terms of Dimensionality Reduction, Threshold, Classification Error and Classification Accuracy for various Cancer Patterns.

Interval-value Based Particle Swarm Optimization algorithm for cancer-type specific gene selection and sample classification

Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM), K-nearest neighbor (KNN), Interval Valued Classification (IVC) and the improvised Interval Value based Particle Swarm Optimization (IVPSO) algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.

Multiobjective Simulated Annealing-Based Clustering of Tissue Samples for Cancer Diagnosis.

In the field of pattern recognition, the study of the gene expression profiles of different tissue samples over different experimental conditions has become feasible with the arrival of microarray-based technology. In cancer research, classification of tissue samples is necessary for cancer diagnosis, which can be done with the help of microarray technology. In this paper, we have presented a multiobjective optimization (MOO)-based clustering technique utilizing archived multiobjective simulated annealing(AMOSA) as the underlying optimization strategy for classification of tissue samples from cancer datasets. The presented clustering technique is evaluated for three open source benchmark cancer datasets [Brain tumor dataset, Adult Malignancy, and Small Round Blood Cell Tumors (SRBCT)]. In order to evaluate the quality or goodness of produced clusters, two cluster quality measures viz, adjusted rand index and classification accuracy ( % CoA) are calculated. Comparative results of the presented clustering algorithm with ten state-of-the-art existing clustering techniques are shown for three benchmark datasets. Also, we have conducted a statistical significance test called t-test to prove the superiority of our presented MOO-based clustering technique over other clustering techniques. Moreover, significant gene markers have been identified and demonstrated visually from the clustering solutions obtained. In the field of cancer subtype prediction, this study can have important impact.

Multiclass Gene Selection Using Pareto-Fronts

Filter methods are often used for selection of genes in multiclass sample classification by using microarray data. Such techniques usually tend to bias toward a few classes that are easily distinguishable from other classes due to imbalances of strong features and sample sizes of different classes. It could therefore lead to selection of redundant genes while missing the relevant genes, leading to poor classification of tissue samples. In this manuscript, we propose to decompose multiclass ranking statistics into class-specific statistics and then use Pareto-front analysis for selection of genes. This alleviates the bias induced by class intrinsic characteristics of dominating classes. The use of Pareto-front analysis is demonstrated on two filter criteria commonly used for gene selection: F-score and KW-score. A significant improvement in classification performance and reduction in redundancy among top-ranked genes were achieved in experiments with both synthetic and real-benchmark data sets.

Gene-Expression-Based Cancer Subtypes Prediction Through Feature Selection and Transductive SVM

With the advancement of microarray technology, gene expression profiling has shown great potential in outcome prediction for different types of cancers. Microarray cancer data, organized as samples versus genes fashion, are being exploited for the classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer type. Nevertheless, small sample size remains a bottleneck to design suitable classifiers. Traditional supervised classifiers can only work with labeled data. On the other hand, a large number of microarray data that do not have adequate follow-up information are disregarded. A novel approach to combine feature (gene) selection and transductive support vector machine (TSVM) is proposed. We demonstrated that 1) potential gene markers could be identified and 2) TSVMs improved prediction accuracy as compared to the standard inductive SVMs (ISVMs). A forward greedy search algorithm based on consistency and a statistic called signal-to-noise ratio were employed to obtain the potential gene markers. The selected genes of the microarray data were then exploited to design the TSVM. Experimental results confirm the effectiveness of the proposed technique compared to the ISVM and low-density separation method in the area of semisupervised cancer classification as well as gene-marker identification.

Interval-valued analysis for discriminative gene selection and tissue sample classification using microarray data

An important application of gene expression data is to classify samples in a variety of diagnostic fields. However, high dimensionality and a small number of noisy samples pose significant challenges to existing classification methods. Focused on the problems of overfitting and sensitivity to noise of the dataset in the classification of microarray data, we propose an interval-valued analysis method based on a rough set technique to select discriminative genes and to use these genes to classify tissue samples of microarray data. We first select a small subset of genes based on interval-valued rough set by considering the preference-ordered domains of the gene expression data, and then classify test samples into certain classes with a term of similar degree. Experiments show that the proposed method is able to reach high prediction accuracies with a small number of selected genes and its performance is robust to noise.

Two-Step Cross-Entropy Feature Selection for Microarrays—Power Through Complementarity

Current feature selection methods for supervised classification of tissue samples from microarray data generally fail to exploit complementary discriminatory power that can be found in sets of features. Using a feature selection method with the computational architecture of the cross-entropy method, including an additional preliminary step ensuring a lower bound on the number of times any feature is considered, we show when testing on a human lymph node data set that there are a significant number of genes that perform well when their complementary power is assessed, but “pass under the radar” of popular feature selection methods that only assess genes individually on a given classification tool. We also show that this phenomenon becomes more apparent as diagnostic specificity of the tissue samples analysed increases.

Multi-class clustering of cancer subtypes through SVM based ensemble of pareto-optimal solutions for gene marker identification.

With the advancement of microarray technology, it is now possible to study the expression profiles of thousands of genes across different experimental conditions or tissue samples simultaneously. Microarray cancer datasets, organized as samples versus genes fashion, are being used for classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer types. In this article, we have presented an unsupervised cancer classification technique based on multiobjective genetic clustering of the tissue samples. In this regard, a real-coded encoding of the cluster centers is used and cluster compactness and separation are simultaneously optimized. The resultant set of near-Pareto-optimal solutions contains a number of non-dominated solutions. A novel approach to combine the clustering information possessed by the non-dominated solutions through Support Vector Machine (SVM) classifier has been proposed. Final clustering is obtained by consensus among the clusterings yielded by different kernel functions. The performance of the proposed multiobjective clustering method has been compared with that of several other microarray clustering algorithms for three publicly available benchmark cancer datasets. Moreover, statistical significance tests have been conducted to establish the statistical superiority of the proposed clustering method. Furthermore, relevant gene markers have been identified using the clustering result produced by the proposed clustering method and demonstrated visually. Biological relationships among the gene markers are also studied based on gene ontology. The results obtained are found to be promising and can possibly have important impact in the area of unsupervised cancer classification as well as gene marker identification for multiple cancer subtypes.

Classification of multi class dataset using wavelet power spectrum

Data mining techniques are widely used in many fields. One of the applications of data mining in the field of the Bioinformatics is classification of tissue samples. In the present work, a wavelet power spectrum based approach has been presented for feature selection and successful classification of the multi class dataset. The proposed method was applied on SRBCT and the breast cancer datasets which are multi class cancer datasets. The selected features are almost those selected in previous works. The method was able to produce almost 100% accurate classification results. The method is very simple and robust to noise. No extensive preprocessing is required. The classification was performed with comparatively very lesser number of features than those used in the original works. No information is lost due to the initial pruning of the data usually performed using a threshold in other methods. The method utilizes the inherent nature of the data in performing various tasks. So, the method can be used for a wide range of data.

Classification based upon gene expression data: bias and precision of error rates

Gene expression data offer a large number of potentially useful predictors for the classification of tissue samples into classes, such as diseased and non-diseased. The predictive error rate of classifiers can be estimated using methods such as cross-validation. We have investigated issues of interpretation and potential bias in the reporting of error rate estimates. The issues considered here are optimization and selection biases, sampling effects, measures of misclassification rate, baseline error rates, two-level external cross-validation and a novel proposal for detection of bias using the permutation mean. Reporting an optimal estimated error rate incurs an optimization bias. Downward bias of 3-5% was found in an existing study of classification based on gene expression data and may be endemic in similar studies. Using a simulated non-informative dataset and two example datasets from existing studies, we show how bias can be detected through the use of label permutations and avoided using two-level external cross-validation. Some studies avoid optimization bias by using single-level cross-validation and a test set, but error rates can be more accurately estimated via two-level cross-validation. In addition to estimating the simple overall error rate, we recommend reporting class error rates plus where possible the conditional risk incorporating prior class probabilities and a misclassification cost matrix. We also describe baseline error rates derived from three trivial classifiers which ignore the predictors. R code which implements two-level external cross-validation with the PAMR package, experiment code, dataset details and additional figures are freely available for non-commercial use from http://www.maths.qut.edu.au/profiles/wood/permr.jsp

Genomic Alterations in Ectopic and Eutopic Endometria of Women with Endometriosis

Background/Aims: Ectopic and eutopic endometria of women with endometriosis have been shown to contain genomic alterations. In this study, we sought to identify genomic alterations in both ectopic and eutopic endometria of 5 women with endometriosis and to examine whether the two tissues share any genomic alterations. We also attempted to classify tissue samples based on the alteration profiles. Methods: Laser capture microdissection was used to harvest epithelial cells. High-resolution comparative genomic hybridization microarrays were used to identify genomic alterations in eutopic and ectopic endometria from 5 women with endometriosis. The results were validated by real-time RT-PCR and loss of heterozygosity analysis. Results: All 5 patients had genomic alterations in their eutopic and ectopic endometria. The ectopic and eutopic endometria shared a sizable portion of genomic alterations. Cluster analysis of the genomic alteration profile correctly and consistently classified tissue samples from the 5 patients into two groups: peritoneal implants and ovarian cysts. Conclusions: The correct classification of tissue samples into two groups suggests that these two subtypes of endometriosis may have distinct genomic alteration profiles and are thus distinct entities, as previously proposed. The shared alterations are likely the ones that harbor genes responsible for an increased propensity of endometrial debris to implant to the ectopic sites and for early events that lead to the establishment of lesions. Alternatively, these shared alterations may harbor genes that are dysregulated in both eutopic and ectopic endometria. The identified genomic alterations should help to zero in genes involved in the pathogenesis of endometriosis in future studies.

Selection bias in working with the top genes in supervised classification of tissue samples

Currently there is much interest in using microarray gene-expression data to form prediction rules for the diagnosis of patient outcomes. A process of gene selection is usually carried out first to find those genes that are most useful according to some criterion for distinguishing between the given classes of tissue samples. However, there is a bias (selection bias) introduced in the estimate of the final version of a prediction rule that has been formed from a smaller subset of the genes that have been selected according to some optimality criterion. In this paper, we focus on the bias that arises when a full data set is not available in the first instance and the prediction rule is formed subsequently by working with the top-ranked genes from the full set. We demonstrate how large the subset of top genes must be before this selection bias is not of practical consequence.

Classification of microarray data with factor mixture models

The classification of few tissue samples on a very large number of genes represents a non-standard problem in statistics but a usual one in microarray expression data analysis. In fact, the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. We consider high-density oligonucleotide microarray data, where the expression level is associated to an 'absolute call', which represents a qualitative indication of whether or not a transcript is detected within a sample. The 'absolute call' is generally not taken in consideration in analyses. In contrast to frequently used cluster analysis methods to analyze gene expression data, we consider a problem of classification of tissues and of the variables selection. We adopted methodologies formulated by Ghahramani and Hinton and Rocci and Vichi for simultaneous dimensional reduction of genes and classification of tissues; trying to identify genes (denominated 'markers') that are able to distinguish between two known different classes of tissue samples. In this respect, we propose a generalization of the approach proposed by McLachlan et al. by advising to estimate the distribution of log LR statistic for testing one versus two component hypothesis in the mixture model for each gene considered individually, using a parametric bootstrap approach. We compare conditional (on 'absolute call') and unconditional analyses performed on dataset described in Golub et al. We show that the proposed techniques improve the results of classification of tissue samples with respect to known results on the same benchmark dataset. The software of Ghahramani and Hinton is written in Matlab and available in 'Mixture of Factor Analyzers' on http://www.gatsby.ucl.ac.uk/~zoubin/software.html while the software of Rocci and Vichi is available upon request from the authors.

Using uncorrelated discriminant analysis for tissue classification with gene expression data

The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high (in the thousands) compared to the number of data samples (in the tens or low hundreds); that is, the data dimension is large compared to the number of data points (such data is said to be undersampled). To cope with performance and accuracy problems associated with high dimensionality, it is commonplace to apply a preprocessing step that transforms the data to a space of significantly lower dimension with limited loss of the information present in the original data. Linear Discriminant Analysis (LDA) is a well-known technique for dimension reduction and feature extraction, but it is not applicable for undersampled data due to singularity problems associated with the matrices in the underlying representation. This paper presents a dimension reduction and feature extraction scheme, called Uncorrelated Linear Discriminant Analysis (ULDA), for undersampled problems and illustrates its utility on gene expression data. ULDA employs the Generalized Singular Value Decomposition method to handle undersampled data and the features that it produces in the transformed space are uncorrelated, which makes it attractive for gene expression data. The properties of ULDA are established rigorously and extensive experimental results on gene expression data are presented to illustrate its effectiveness in classifying tissue samples. These results provide a comparative study of various state-of-the-art classification methods on well-known gene expression data sets.

Clustering and classification based on the L1 data depth

Clustering and classification are important tasks for the analysis of microarray gene expression data. Classification of tissue samples can be a valuable diagnostic tool for diseases such as cancer. Clustering samples or experiments may lead to the discovery of subclasses of diseases. Clustering genes can help identify groups of genes that respond similarly to a set of experimental conditions. We also need validation tools for clustering and classification. Here, we focus on the identification of outliers—units that may have been misallocated, or mislabeled, or are not representative of the classes or clusters. We present two new methods: DDclust and DDclass, for clustering and classification. These non-parametric methods are based on the intuitively simple concept of data depth. We apply the methods to several gene expression and simulated data sets. We also discuss a convenient visualization and validation tool—the relative data depth plot.