Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction

<b>Background</b> The widely used k top scoring pair (k-TSP) algorithm is a simple yet powerful parameter-free classifier. It owes its success in many cancer microarray datasets to an effective feature selection algorithm that is based on relative expression ordering of gene pairs. However, its general robustness does not extend to some difficult datasets, such as those involving cancer outcome prediction, which may be due to the relatively simple voting scheme used by the classifier. We believe that the performance can be enhanced by separating its effective feature selection component and combining it with a powerful classifier such as the support vector machine (SVM). More generally the top scoring pairs generated by the k-TSP ranking algorithm can be used as a dimensionally reduced subspace for other machine learning classifiers.<p></p> <b>Results</b> We developed an approach integrating the k-TSP ranking algorithm (TSP) with other machine learning methods, allowing combination of the computationally efficient, multivariate feature ranking of k-TSP with multivariate classifiers such as SVM. We evaluated this hybrid scheme (k-TSP+SVM) in a range of simulated datasets with known data structures. As compared with other feature selection methods, such as a univariate method similar to Fisher's discriminant criterion (Fisher), or a recursive feature elimination embedded in SVM (RFE), TSP is increasingly more effective than the other two methods as the informative genes become progressively more correlated, which is demonstrated both in terms of the classification performance and the ability to recover true informative genes. We also applied this hybrid scheme to four cancer prognosis datasets, in which k-TSP+SVM outperforms k-TSP classifier in all datasets, and achieves either comparable or superior performance to that using SVM alone. In concurrence with what is observed in simulation, TSP appears to be a better feature selector than Fisher and RFE in some of the cancer datasets.<p></p> <b>Conclusions</b> The k-TSP ranking algorithm can be used as a computationally efficient, multivariate filter method for feature selection in machine learning. SVM in combination with k-TSP ranking algorithm outperforms k-TSP and SVM alone in simulated datasets and in some cancer prognosis datasets. Simulation studies suggest that as a feature selector, it is better tuned to certain data characteristics, i.e. correlations among informative genes, which is potentially interesting as an alternative feature ranking method in pathway analysis

A Survey of Feature Selection Strategies for DNA Microarray Classification

Classification tasks are difficult and challenging in the bioinformatics field, that used to predict or diagnose patients at an early stage of disease by utilizing DNA microarray technology. However, crucial characteristics of DNA microarray technology are a large number of features and small sample sizes, which means the technology confronts a "dimensional curse" in its classification tasks because of the high computational execution needed and the discovery of biomarkers difficult. To reduce the dimensionality of features to find the significant features that can employ feature selection algorithms and not affect the performance of classification tasks. Feature selection helps decrease computational time by removing irrelevant and redundant features from the data. The study aims to briefly survey popular feature selection methods for classifying DNA microarray technology, such as filters, wrappers, embedded, and hybrid approaches. Furthermore, this study describes the steps of the feature selection process used to accomplish classification tasks and their relationships to other components such as datasets, cross-validation, and classifier algorithms. In the case study, we chose four different methods of feature selection on two-DNA microarray datasets to evaluate and discuss their performances, namely classification accuracy, stability, and the subset size of selected features. Keywords: Brief survey; DNA microarray data; feature selection; filter methods; wrapper methods; embedded methods; and hybrid methods. DOI: 10.7176/CEIS/14-2-01 Publication date:March 31st 202

Exploiting the ensemble paradigm for stable feature selection: A case study on high-dimensional genomic data

Ensemble classification is a well-established approach that involves fusing the decisions of multiple predictive models. A similar “ensemble logic” has been recently applied to challenging feature selection tasks aimed at identifying the most informative variables (or features) for a given domain of interest. In this work, we discuss the rationale of ensemble feature selection and evaluate the effects and the implications of a specific ensemble approach, namely the data perturbation strategy. Basically, it consists in combining multiple selectors that exploit the same core algorithm but are trained on different perturbed versions of the original data. The real potential of this approach, still object of debate in the feature selection literature, is here investigated in conjunction with different kinds of core selection algorithms (both univariate and multivariate). In particular, we evaluate the extent to which the ensemble implementation improves the overall performance of the selection process, in terms of predictive accuracy and stability (i.e., robustness with respect to changes in the training data). Furthermore, we measure the impact of the ensemble approach on the final selection outcome, i.e. on the composition of the selected feature subsets. The results obtained on ten public genomic benchmarks provide useful insight on both the benefits and the limitations of such ensemble approach, paving the way to the exploration of new and wider ensemble schemes

Computational Hybrid Systems for Identifying Prognostic Gene Markers of Lung Cancer

Lung cancer is the most fatal cancer around the world. Current lung cancer prognosis and treatment is based on tumor stage population statistics and could not reliably assess the risk for developing recurrence in individual patients. Biomarkers enable treatment options to be tailored to individual patients based on their tumor molecular characteristics. To date, there is no clinically applied molecular prognostic model for lung cancer. Statistics and feature selection methods identify gene candidates by ranking the association between gene expression and disease outcome, but do not account for the interactions among genes. Computational network methods could model interactions, but have not been used for gene selection due to computational inefficiency. Moreover, the curse of dimensionality in human genome data imposes more computational challenges to these methods.;We proposed two hybrid systems for the identification of prognostic gene signatures for lung cancer using gene expressions measured with DNA microarray. The first hybrid system combined t-tests, Statistical Analysis of Microarray (SAM), and Relief feature selections in multiple gene filtering layers. This combinatorial system identified a 12-gene signature with better prognostic performance than published signatures in treatment selection for stage I and II patients (log-rank P\u3c0.04, Kaplan-Meier analyses). The 12-gene signature is a more significant prognostic factor (hazard ratio=4.19, 95% CI: [2.08, 8.46], P\u3c0.00006) than other clinical covariates. The signature genes were found to be involved in tumorigenesis in functional pathway analyses.;The second proposed system employed a novel computational network model, i.e., implication networks based on prediction logic. This network-based system utilizes gene coexpression networks and concurrent coregulation with signaling pathways for biomarker identification. The first application of the system modeled disease-mediated genome-wide coexpression networks. The entire genomic space were extensively explored and 21 gene signatures were discovered with better prognostic performance than all published signatures in stage I patients not receiving chemotherapy (hazard ratio\u3e1, CPE\u3e0.5, P \u3c 0.05). These signatures could potentially be used for selecting patients for adjuvant chemotherapy. The second application of the system modeled the smoking-mediated coexpression networks and identified a smoking-associated 7-gene signature. The 7-gene signature generated significant prognostication specific to smoking lung cancer patients (log-rank P\u3c0.05, Kaplan-Meier analyses), with implications in diagnostic screening of lung cancer risk in smokers (overall accuracy=74%, P\u3c0.006). The coexpression patterns derived from the implication networks in both applications were successfully validated with molecular interactions reported in the literature (FDR\u3c0.1).;Our studies demonstrated that hybrid systems with multiple gene selection layers outperform traditional methods. Moreover, implication networks could efficiently model genome-scale disease-mediated coexpression networks and crosstalk with signaling pathways, leading to the identification of clinically important gene signatures

Exploring the Concept of the Digital Educator During COVID-19

T In many machine learning classification problems, datasets are usually of high dimensionality and therefore require efficient and effective methods for identifying the relative importance of their attributes, eliminating the redundant and irrelevant ones. Due to the huge size of the search space of the possible solutions, the attribute subset evaluation feature selection methods are not very suitable, so in these scenarios feature ranking methods are used. Most of the feature ranking methods described in the literature are univariate methods, which do not detect interactions between factors. In this paper, we propose two new multivariate feature ranking methods based on pairwise correlation and pairwise consistency, which have been applied for cancer gene expression and genotype-tissue expression classification tasks using public datasets. We statistically proved that the proposed methods outperform the state-of-the-art feature ranking methods Clustering Variation, Chi Squared, Correlation, Information Gain, ReliefF and Significance, as well as other feature selection methods for attribute subset evaluation based on correlation and consistency with the multi-objective evolutionary search strategy, and with the embedded feature selection methods C4.5 and LASSO. The proposed methods have been implemented on the WEKA platform for public use, making all the results reported in this paper repeatable and replicabl

Phenotype prediction and feature selection in genome-wide association studies

Genome wide association studies (GWAS) search for correlations between single nucleotide polymorphisms (SNPs) in a subject genome and an observed phenotype. GWAS can be used to generate models for predicting phenotype based on genotype, as well as aiding in identification of specific genes affecting the biological mechanism underlying the phenotype. In this investigation, phenotype prediction models are constructed from GWAS training data and are evaluated for performance on test data. Three methods are used to rank SNPs by their correlation with the phenotype: the univariate Wald test, a multivariate, support vector machine (SVM) based technique, and a hybrid method where a subset of top ranked SNPs from the Wald test are used to train the SVM. Both case- control studies and quantitative phenotypes are examined. For each method and data set, a series of least squares linear regression models is generated from nested subsets of the best SNPs from each ranking method. The accuracy of these models is determined on a test data set, and a plot of prediction performance against the number of top ranked SNPs considered is generated. The SVM and hybrid methods are found to be consistently superior to the Wald test in ranking predictive SNPs. The hybrid method allows a useful trade-off between increasing accuracy vs. using fewer SNPs to be optimized as desired