Multiclass classification of microarray data samples with a reduced number of genes

<p>Abstract</p> <p>Background</p> <p>Multiclass classification of microarray data samples with a reduced number of genes is a rich and challenging problem in Bioinformatics research. The problem gets harder as the number of classes is increased. In addition, the performance of most classifiers is tightly linked to the effectiveness of mandatory gene selection methods. Critical to gene selection is the availability of estimates about the maximum number of genes that can be handled by any classification algorithm. Lack of such estimates may lead to either computationally demanding explorations of a search space with thousands of dimensions or classification models based on gene sets of unrestricted size. In the former case, unbiased but possibly overfitted classification models may arise. In the latter case, biased classification models unable to support statistically significant findings may be obtained.</p> <p>Results</p> <p>A novel bound on the maximum number of genes that can be handled by binary classifiers in binary mediated multiclass classification algorithms of microarray data samples is presented. The bound suggests that high-dimensional binary output domains might favor the existence of accurate and sparse binary mediated multiclass classifiers for microarray data samples.</p> <p>Conclusions</p> <p>A comprehensive experimental work shows that the bound is indeed useful to induce accurate and sparse multiclass classifiers for microarray data samples.</p

Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data

The development of molecular techniques for genetic analysis has enabled great advances in cereal breeding. However, their usefulness in hybrid breeding, particularly in assigning new lines to heterotic groups previously established, still remains unsolved. In this work we evaluate the performance of several state-of-art multiclass classifiers onto three molecular marker datasets representing a broad spectrum of maize heterotic patterns. Even though results are variable, they suggest supervised learning algorithms as a valuable complement to traditional breeding programs.Fil: Ornella, Leonardo Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Tapia, Elizabeth. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentin

Gene expression-based prediction of malignancies

Molecular classification of malignancies can potentially stratify patients into distinct subclasses not detectable using traditional classification of tumors, opening new perspectives on the diagnosis and personalized therapy of polygenic diseases. In this paper we present a brief overview of our work on gene expression based prediction of malignancies, starting from the dichotomic classification problem of normal versus tumoural tissues, to multiclasss cancer diagnosis and to functional class discovery and gene selection problems. The last part of this work present preliminary results about the applicatin of ensembles of SVMs based on bias-variance decomposition of the error to the analysis of gene expression data of malignant tissues

Extending twin support vector machine classifier for multi-category classification problems

© 2013 – IOS Press and the authors. All rights reservedTwin support vector machine classifier (TWSVM) was proposed by Jayadeva et al., which was used for binary classification problems. TWSVM not only overcomes the difficulties in handling the problem of exemplar unbalance in binary classification problems, but also it is four times faster in training a classifier than classical support vector machines. This paper proposes one-versus-all twin support vector machine classifiers (OVA-TWSVM) for multi-category classification problems by utilizing the strengths of TWSVM. OVA-TWSVM extends TWSVM to solve k-category classification problems by developing k TWSVM where in the ith TWSVM, we only solve the Quadratic Programming Problems (QPPs) for the ith class, and get the ith nonparallel hyperplane corresponding to the ith class data. OVA-TWSVM uses the well known one-versus-all (OVA) approach to construct a corresponding twin support vector machine classifier. We analyze the efficiency of the OVA-TWSVM theoretically, and perform experiments to test its efficiency on both synthetic data sets and several benchmark data sets from the UCI machine learning repository. Both the theoretical analysis and experimental results demonstrate that OVA-TWSVM can outperform the traditional OVA-SVMs classifier. Further experimental comparisons with other multiclass classifiers demonstrated that comparable performance could be achieved.This work is supported in part by the grant of the Fundamental Research Funds for the Central Universities of GK201102007 in PR China, and is also supported by Natural Science Basis Research Plan in Shaanxi Province of China (Program No.2010JM3004), and is at the same time supported by Chinese Academy of Sciences under the Innovative Group Overseas Partnership Grant as well as Natural Science Foundation of China Major International Joint Research Project (NO.71110107026)

Comparison of Two Output-Coding Strategies for Multi-Class Tumor Classification Using Gene Expression Data and Latent Variable Model as Binary Classifier

Multi-class cancer classification based on microarray data is described. A generalized output-coding scheme based on One Versus One (OVO) combined with Latent Variable Model (LVM) is used. Results from the proposed One Versus One (OVO) outputcoding strategy is compared with the results obtained from the generalized One Versus All (OVA) method and their efficiencies of using them for multi-class tumor classification have been studied. This comparative study was done using two microarray gene expression data: Global Cancer Map (GCM) dataset and brain cancer (BC) dataset. Primary feature selection was based on fold change and penalized t-statistics. Evaluation was conducted with varying feature numbers. The OVO coding strategy worked quite well with the BC data, while both OVO and OVA results seemed to be similar for the GCM data. The selection of output coding methods for combining binary classifiers for multi-class tumor classification depends on the number of tumor types considered, the discrepancies between the tumor samples used for training as well as the heterogeneity of expression within the cancer subtypes used as training data

Hierarchical information representation and efficient classification of gene expression microarray data

In the field of computational biology, microarryas are used to measure the activity of thousands of genes at once and create a global picture of cellular function. Microarrays allow scientists to analyze expression of many genes in a single experiment quickly and eficiently. Even if microarrays are a consolidated research technology nowadays and the trends in high-throughput data analysis are shifting towards new technologies like Next Generation Sequencing (NGS), an optimum method for sample classification has not been found yet. Microarray classification is a complicated task, not only due to the high dimensionality of the feature set, but also to an apparent lack of data structure. This characteristic limits the applicability of processing techniques, such as wavelet filtering or other filtering techniques that take advantage of known structural relation. On the other hand, it is well known that genes are not expressed independently from other each other: genes have a high interdependence related to the involved regulating biological process. This thesis aims to improve the current state of the art in microarray classification and to contribute to understand how signal processing techniques can be developed and applied to analyze microarray data. The goal of building a classification framework needs an exploratory work in which algorithms are constantly tried and adapted to the analyzed data. The developed algorithms and classification frameworks in this thesis tackle the problem with two essential building blocks. The first one deals with the lack of a priori structure by inferring a data-driven structure with unsupervised hierarchical clustering tools. The second key element is a proper feature selection tool to produce a precise classifier as an output and to reduce the overfitting risk. The main focus in this thesis is the binary data classification, field in which we obtained relevant improvements to the state of the art. The first key element is the data-driven structure, obtained by modifying hierarchical clustering algorithms derived from the Treelets algorithm from the literature. Several alternatives to the original reference algorithm have been tested, changing either the similarity metric to merge the feature or the way two feature are merged. Moreover, the possibility to include external sources of information from publicly available biological knowledge and ontologies to improve the structure generation has been studied too. About the feature selection, two alternative approaches have been studied: the first one is a modification of the IFFS algorithm as a wrapper feature selection, while the second approach involved an ensemble learning focus. To obtain good results, the IFFS algorithm has been adapted to the data characteristics by introducing new elements to the selection process like a reliability measure and a scoring system to better select the best feature at each iteration. The second feature selection approach is based on Ensemble learning, taking advantage of the microarryas feature abundance to implement a different selection scheme. New algorithms have been studied in this field, improving state of the art algorithms to the microarray data characteristic of small sample and high feature numbers. In addition to the binary classification problem, the multiclass case has been addressed too. A new algorithm combining multiple binary classifiers has been evaluated, exploiting the redundancy offered by multiple classifiers to obtain better predictions. All the studied algorithm throughout this thesis have been evaluated using high quality publicly available data, following established testing protocols from the literature to offer a proper benchmarking with the state of the art. Whenever possible, multiple Monte Carlo simulations have been performed to increase the robustness of the obtained results.En el campo de la biología computacional, los microarrays son utilizados para medir la actividad de miles de genes a la vez y producir una representación global de la función celular. Los microarrays permiten analizar la expresión de muchos genes en un solo experimento, rápidamente y eficazmente. Aunque los microarrays sean una tecnología de investigación consolidada hoy en día y la tendencia es en utilizar nuevas tecnologías como Next Generation Sequencing (NGS), aun no se ha encontrado un método óptimo para la clasificación de muestras. La clasificación de muestras de microarray es una tarea complicada, debido al alto número de variables y a la falta de estructura entre los datos. Esta característica impide la aplicación de técnicas de procesado que se basan en relaciones estructurales, como el filtrado con wavelet u otras técnicas de filltrado. Por otro lado, los genes no se expresen independientemente unos de otros: los genes están inter-relacionados según el proceso biológico que les regula. El objetivo de esta tesis es mejorar el estado del arte en la clasi cación de microarrays y contribuir a entender cómo se pueden diseñar y aplicar técnicas de procesado de señal para analizar microarrays. El objetivo de construir un algoritmo de clasi cación, necesita un estudio de comprobaciones y adaptaciones de algoritmos existentes a los datos analizados. Los algoritmo desarrollados en esta tesis encaran el problema con dos bloques esenciales. El primero ataca la falta de estructura, derivando un árbol binario usando herramientas de clustering no supervisado. El segundo elemento fundamental para obtener clasificadores precisos reduciendo el riesgo de overfitting es un elemento de selección de variables. La principal tarea en esta tesis es la clasificación de datos binarios en la cual hemos obtenido mejoras relevantes al estado del arte. El primer paso es la generación de una estructura, para eso se ha utilizado el algoritmo Treelets disponible en la literatura. Múltiples alternativas a este algoritmo original han sido propuestas y evaluadas, cambiando las métricas de similitud o las reglas de fusión durante el proceso. Además, se ha estudiado la posibilidad de usar fuentes de información externas, como ontologías de información biológica, para mejorar la inferencia de la estructura. Se han estudiado dos enfoques diferentes para la selección de variables: el primero es una modificación del algoritmo IFFS y el segundo utiliza un esquema de aprendizaje con “ensembles”. El algoritmo IFFS ha sido adaptado a las características de microarrays para obtener mejores resultados, añadiendo elementos como la medida de fiabilidad y un sistema de evaluación para seleccionar la mejor variable en cada iteración. El método que utiliza “ensembles” aprovecha la abundancia de features de los microarrays para implementar una selección diferente. En este campo se han estudiado diferentes algoritmos, mejorando alternativas ya existentes al escaso número de muestras y al alto número de variables, típicos de los microarrays. El problema de clasificación con más de dos clases ha sido también tratado al estudiar un nuevo algoritmo que combina múltiples clasificadores binarios. El algoritmo propuesto aprovecha la redundancia ofrecida por múltiples clasificadores para obtener predicciones más fiables. Todos los algoritmos propuestos en esta tesis han sido evaluados con datos públicos y de alta calidad, siguiendo protocolos establecidos en la literatura para poder ofrecer una comparación fiable con el estado del arte. Cuando ha sido posible, se han aplicado simulaciones Monte Carlo para mejorar la robustez de los resultados