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

DISCRIMINANT STEPWISE PROCEDURE

Stepwise procedure is now probably the most popular tool for automatic feature selection. In the most cases it represents model selection approach which evaluates various feature subsets (so called wrapper). In fact it is heuristic search technique which examines the space of all possible feature subsets. This method is known in the literature under different names and variants. We organize the concepts and terminology, and show several variants of stepwise feature selection from a search strategy point of view. Short review of implementations in R will be given

Decision tree rule-based feature selection for imbalanced data

A class imbalance problem appears in many real world applications, e.g., fault diagnosis, text categorization and fraud detection. When dealing with an imbalanced dataset, feature selection becomes an important issue. To address it, this work proposes a feature selection method that is based on a decision tree rule and weighted Gini index. The effectiveness of the proposed methods is verified by classifying a dataset from Santander Bank and two datasets from UCI machine learning repository. The results show that our methods can achieve higher Area Under the Curve (AUC) and F-measure. We also compare them with filter-based feature selection approaches, i.e., Chi-Square and F-statistic. The results show that they outperform them but need slightly more computational efforts

A survey of feature selection in Internet traffic characterization

In the last decade, the research community has focused on new classification methods that rely on statistical characteristics of Internet traffic, instead of pre-viously popular port-number-based or payload-based methods, which are under even bigger constrictions. Some research works based on statistical characteristics generated large fea-ture sets of Internet traffic; however, nowadays it?s impossible to handle hun-dreds of features in big data scenarios, only leading to unacceptable processing time and misleading classification results due to redundant and correlative data. As a consequence, a feature selection procedure is essential in the process of Internet traffic characterization. In this paper a survey of feature selection methods is presented: feature selection frameworks are introduced, and differ-ent categories of methods are briefly explained and compared; several proposals on feature selection in Internet traffic characterization are shown; finally, future application of feature selection to a concrete project is proposed

Fast feature selection aimed at high-dimensional data via hybrid-sequential-ranked searches

We address the feature subset selection problem for classification tasks. We examine the performance of two hybrid strategies that directly search on a ranked list of features and compare them with two widely used algorithms, the fast correlation based filter (FCBF) and sequential forward selection (SFS). The pro-posed hybrid approaches provide the possibility of efficiently applying any subset evaluator, with a wrap-per model included, to large and high-dimensional domains. The experiments performed show that our two strategies are competitive and can select a small subset of features without degrading the classifica-tion error or the advantages of the strategies under study

Wrapper methods for multi-objective feature selection

The ongoing data boom has democratized the use of data for improved decision-making. Beyond gathering voluminous data, preprocessing the data is crucial to ensure that their most rele- vant aspects are considered during the analysis. Feature Selection (FS) is one integral step in data preprocessing for reducing data dimensionality and preserving the most relevant features of the data. FS can be done by inspecting inherent associations among the features in the data (filter methods) or using the model per- formance of a concrete learning algorithm (wrapper methods). In this work, we extensively evaluate a set of FS methods on 32 datasets and measure their effect on model performance, stability, scalability and memory usage. The results re-establish the superiority of wrapper methods over filter methods in model performance. We further investigate the unique role of wrapper methods in multi-objective FS with a focus on two traditional metrics - accuracy and Area Under the ROC Curve (AUC). On model performance, our experiments showed that optimizing for both metrics simultaneously, rather than using a single metric, led to improvements in the accuracy and AUC trade-off1 up to 5% and 10%, respectively.The project leading to this publication has received funding from the European Commission under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 955895). Besim Bilalli is partly supported by the Spanish Ministerio de Ciencia e Innovación, as well as the European Union-Next Generation EU, under the project FJC 2021-046606-I/AEI/10.13039/501100011033. Gianluca Bontempi was supported by Service Public de Wallonie Recherche undergrant n°2010235–ARIAC by DIGITALWALLONIA4.AI.Peer ReviewedPostprint (published version