Feature selection and classification for high-dimensional biological data under cross-validation framework

This research focuses on using statistical learning methods on high-dimensional biological data analysis. In our implementation of high-dimensional biological data analysis, we primarily utilize the statistical learning methods in selecting important predictors and to build predictive classification models. Traditionally, cross-validation methods have been used in order to determine the tuning or threshold parameter for the feature selection. We propose improvements over the methods by adding repeated and nested cross validation techniques. Also, several types of machine learning methods such as lasso, support vector machine and random forest have been used by many previous studies. Those methods have their own merits and demerits. We also propose ensemble feature selection out of the results of the three machine learning methods by capturing their strengths in order to find the more stable feature subset and to optimize the prediction accuracy. We utilize DNA microarray gene expression datasets to describe our methods. We have summarized our work in the following order: (1) the structure of high dimensional biological datasets and the statistical methods to analyze such data; (2) several statistical and machine learning algorithms to analyze high-dimensional biological datasets; (3) improved cross-validation and ensemble learning method to achieve better prediction accuracy and (4) examples using the DNA microarray data to describe our metho

The robust selection of predictive genes via a simple classifier

Identifying genes that direct the mechanism of a disease from expression data is extremely useful in understanding how that mechanism works. This in turn may lead to better diagnoses and potentially can lead to a cure for that disease. This task becomes extremely challenging when the data are characterised by only a small number of samples and a high number of dimensions, as it is often the case with gene expression data. Motivated by this challenge, we present a general framework that focuses on simplicity and data perturbation. These are the keys for the robust identification of the most predictive features in such data. Within this framework, we propose a simple selective na¨ıve Bayes classifier discovered using a global search technique, and combine it with data perturbation to increase its robustness to small sample sizes. An extensive validation of the method was carried out using two applied datasets from the field of microarrays and a simulated dataset, all confounded by small sample sizes and high dimensionality. The method has been shown capable of identifying genes previously confirmed or associated with prostate cancer and viral infections

Identification of disease-causing genes using microarray data mining and gene ontology

Background: One of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes. Methods: We propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results. Results: The proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth. Conclusions: The proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics

A Regularized Method for Selecting Nested Groups of Relevant Genes from Microarray Data

Gene expression analysis aims at identifying the genes able to accurately predict biological parameters like, for example, disease subtyping or progression. While accurate prediction can be achieved by means of many different techniques, gene identification, due to gene correlation and the limited number of available samples, is a much more elusive problem. Small changes in the expression values often produce different gene lists, and solutions which are both sparse and stable are difficult to obtain. We propose a two-stage regularization method able to learn linear models characterized by a high prediction performance. By varying a suitable parameter these linear models allow to trade sparsity for the inclusion of correlated genes and to produce gene lists which are almost perfectly nested. Experimental results on synthetic and microarray data confirm the interesting properties of the proposed method and its potential as a starting point for further biological investigationsComment: 17 pages, 8 Post-script figure