Integrating Biological Information for Feature Selection in Microarray Data Classification

2010 ◽  
Author(s):  
Ong Huey Fang ◽  
Norwati Mustapha ◽  
Md. Nasir Sulaiman
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Data Classification ◽  
Biological Information

scholarly journals Approach to The Selection of Significant Features in Solving Biomedical Problems of Binary Classification of Microarray Data

2020 ◽  
Vol 15 (1) ◽  
pp. 4-19 ◽  
Author(s):  
I.Y. Boyko ◽  
D.S. Anisimov ◽  
L.L. Smolyakova ◽  
M.A. Ryazanov
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Binary Classification ◽  
Biological Information ◽  
Features Selection ◽  
Data Set ◽  
Early Diagnosis Of Cancer ◽  
Latent Structures ◽  
Classification Quality ◽  
Selection Of

In modern biomedical research aimed at finding methods for early diagnosis of cancer, microarrays containing certain biological information about patients are used. Based on these data, patients are assigned to one of two classes, corresponding to the presence and absence of some diagnosis. When solving this problem, one of the steps that have a decisive influence on the quality of classification is the significant features selection. This paper proposes a criterion for the selection of significant features, based on the ledge-coefficient of correlation. The ledge-coefficient was previously used to estimate the degree of interrelation of numerical and binary features. For two sets of microarray data, comparative examples of their binary classification are presented using three feature selection algorithms, three dimensionality reduction methods, six classification models. The use of the ledge-criterion for feature selection made it possible to obtain a classification quality comparable to the results of using common methods of feature selection, such as t-test and U-test. For the data set of the peptide microarrays considered in the paper, the effectiveness of applying the projection method to latent structures had previously been identified. The use of this method in combination with the significant features’ selection using the ledge-criterion made it possible to obtain a higher classification quality measure.

scholarly journals Comparative analysis of ReliefF-SVM and CFS-SVM for microarray data classification

2021 ◽  
Vol 11 (4) ◽  
pp. 3393
Author(s):  
Mochamad Agusta Naofal Hakim ◽  
Adiwijaya Adiwijaya ◽  
Widi Astuti
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Causes Of Death ◽  
Data Classification ◽  
World Health ◽  
Support Vector ◽  
Feature Selection Technique ◽  
Cancer Disease ◽  
Cancer Symptoms ◽  
The World

Cancer is one of the main causes of death in the world where the World Health Organization (WHO) recognized cancer as among the top causes of death in 2018. Thus, detecting cancer symptoms is paramount in order to cure and subsequently reduce the casualties due to cancer disease. Many studies have been developed data mining approaches to detect symptoms of cancer through a classifying human gene data expression. One popular approach is using microarray data based on DNA. However, DNA microarray data has many dimensions that can have a detrimental effect on the accuracy of classification. Therefore, before performing classification, a feature selection technique must be used to eliminate features that do not have important information to support the classification process. The feature selection techniques used were ReliefF and correlation-based feature selection (CFS) and a classification technique used in this study is support vector machine (SVM). Several testing schemes were applied in this analysis to compare the performance of ReliefF and CFS with SVM. It showed that the ReliefF outperformed compared with CFS as microarray data classification approach.

Feature selection in independent component subspace for microarray data classification

2006 ◽  
Vol 69 (16-18) ◽  
pp. 2407-2410 ◽  
Author(s):  
Chun-Hou Zheng ◽  
De-Shuang Huang ◽  
Li Shang
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Data Classification ◽  
Independent Component

Microarray Data Classification Based on Evolutionary Multiple Classifier System

2011 ◽  
Vol 130-134 ◽  
pp. 2077-2080
Author(s):  
Zheng Gang Gu ◽  
Kun Hong Liu
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Selection Process ◽  
Data Classification ◽  
Research Area ◽  
Ensemble Method ◽  
Multiple Classifier System ◽  
Classifier System ◽  
Classifier Selection ◽  
Multiple Classifier

Designing an evolutionary multiple classifier system (MCS) is a relatively new research area. In this paper, we propose a genetic algorithm (GA) based MCS for microarray data classification. We construct a feature poll with different feature selection methods first, and then a multi-objective GA is applied to implement ensemble feature selection process so as to generate a set of classifiers. When this GA stops, a set of base classifiers are generated. Here we use all the nondominated individuals in last generation to build an ensemble system and test the proposed ensemble method and the method that apply a classifier selection process to select proper classifiers from all the individuals in last generation. The experimental results show the proposed ensemble method is roubust and can lead to promising results.

Distributed feature selection: An application to microarray data classification

2015 ◽  
Vol 30 ◽  
pp. 136-150 ◽  
Author(s):  
V. Bolón-Canedo ◽  
N. Sánchez-Maroño ◽  
A. Alonso-Betanzos
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Data Classification

scholarly journals GENE EXPRESSION DATA CLASSIFICATION COMBINING HIERARCHICAL REPRESENTATION AND EFFICIENT FEATURE SELECTION

2012 ◽  
Vol 20 (04) ◽  
pp. 349-375 ◽  
Author(s):  
MATTIA BOSIO ◽  
PAU BELLOT ◽  
PHILIPPE SALEMBIER ◽  
ALBERT OLIVERAS-VERGÉS
Keyword(s):  
Feature Selection ◽  
Error Rate ◽  
Selection Process ◽  
Enrichment Analysis ◽  
Data Classification ◽  
Gene Set Enrichment Analysis ◽  
Biological Information ◽  
Study Phase ◽  
Forward Selection ◽  
Linear Discriminant

A general framework for microarray data classification is proposed in this paper. It produces precise and reliable classifiers through a two-step approach. At first, the original feature set is enhanced by a new set of features called metagenes. These new features are obtained through a hierarchical clustering process on the original data. Two different metagene generation rules have been analyzed, called Treelets clustering and Euclidean clustering. Metagenes creation is attractive for several reasons: first, they can improve the classification since they broaden the available feature space and capture the common behavior of similar genes reducing the residual measurement noise. Furthermore, by analyzing some of the chosen metagenes for classification with gene set enrichment analysis algorithms, it is shown how metagenes can summarize the behavior of functionally related probe sets. Additionally, metagenes can point out, still undocumented, highly discriminant probe sets numerically related to other probes endowed with prior biological information in order to contribute to the knowledge discovery process. The second step of the framework is the feature selection which applies the Improved Sequential Floating Forward Selection algorithm (IFFS) to properly choose a subset from the available feature set for classification composed of genes and metagenes. Considering the microarray sample scarcity problem, besides the classical error rate, a reliability measure is introduced to improve the feature selection process. Different scoring schemes are studied to choose the best one using both error rate and reliability. The Linear Discriminant Analysis classifier (LDA) has been used throughout this work, due to its good characteristics, but the proposed framework can be used with almost any classifier. The potential of the proposed framework has been evaluated analyzing all the publicly available datasets offered by the Micro Array Quality Control Study, phase II (MAQC). The comparative results showed that the proposed framework can compete with a wide variety of state of the art alternatives and it can obtain the best mean performance if a particular setup is chosen. A Monte Carlo simulation confirmed that the proposed framework obtains stable and repeatable results.

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