A multi-objective strategy in genetic algorithms for gene selection of gene expression data

2009 ◽  
Vol 13 (2) ◽  
pp. 410-413 ◽  
Author(s):  
Mohd Saberi Mohamad ◽  
Sigeru Omatu ◽  
Safaai Deris ◽  
Muhammad Faiz Misman ◽  
Michifumi Yoshioka
Keyword(s):  
Gene Expression ◽  
Genetic Algorithms ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Expression Data ◽  
Multi Objective ◽  
Selection Of

scholarly journals Gene Selection using a Hybrid RFE Along with LASSO for Cancer Classification

2019 ◽  
Vol 9 (1S3) ◽  
pp. 83-96
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Classification Model ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Expression Data ◽  
Cancer Prediction ◽  
Selection Operator ◽  
Selection Of

Gene expression profiling using microarray technology has done with the chip based phenomena. For studying gene expression data are more helpful in knowing various diseases and more useful in finding diseases. Recently in the bioinformatics field, cancer prediction using gene expression data had made the assuring area. Samples having the gene attributes will not surely give the efficient amount of classification. Overcoming these contribution, a strong method is required for selecting the relevant gene features for building the classification model effectively. Basically least absolute shrinkage and selection operator (LASSO) and Recursive feature elimination (RFE) are automatic gene feature selection methods used for classification. Here in our proposed work, we use these two methods as a hybrid one for selecting the features and later it applied into the Support vector machine (SVM) for easy classification. It made best when compared to the existing techniques by their performance measures, were regulated on six publically available cancer datasets. Just out it gives the good awareness in the selection of features.

scholarly journals Incorporating Pathway Information into Feature Selection towards Better Performed Gene Signatures

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Suyan Tian ◽  
Chi Wang ◽  
Bing Wang
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Selection Process ◽  
Biological Knowledge ◽  
Expression Data ◽  
Selection Methods ◽  
Its Gene ◽  
Active Research

To analyze gene expression data with sophisticated grouping structures and to extract hidden patterns from such data, feature selection is of critical importance. It is well known that genes do not function in isolation but rather work together within various metabolic, regulatory, and signaling pathways. If the biological knowledge contained within these pathways is taken into account, the resulting method is a pathway-based algorithm. Studies have demonstrated that a pathway-based method usually outperforms its gene-based counterpart in which no biological knowledge is considered. In this article, a pathway-based feature selection is firstly divided into three major categories, namely, pathway-level selection, bilevel selection, and pathway-guided gene selection. With bilevel selection methods being regarded as a special case of pathway-guided gene selection process, we discuss pathway-guided gene selection methods in detail and the importance of penalization in such methods. Last, we point out the potential utilizations of pathway-guided gene selection in one active research avenue, namely, to analyze longitudinal gene expression data. We believe this article provides valuable insights for computational biologists and biostatisticians so that they can make biology more computable.

A model for gene selection and classification of gene expression data

2007 ◽  
Vol 11 (2) ◽  
pp. 219-222 ◽  
Author(s):  
Mohd Saberi Mohamad ◽  
Sigeru Omatu ◽  
Safaai Deris ◽  
Siti Zaiton Mohd Hashim
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Expression Data

Gene Selection Using Parallel Lion Optimization Method in Microarray Data for Cancer Classification

2019 ◽  
Vol 9 (6) ◽  
pp. 1294-1300 ◽  
Author(s):  
A. Sampathkumar ◽  
P. Vivekanandan
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Microarray Data ◽  
Gene Selection ◽  
Optimization Method ◽  
Vital Role ◽  
Expression Data ◽  
Complicated Process ◽  
Numerous Data ◽  
Relevant Gene

In the field of bioinformatics research, a large volume of genetic data has been generated. Availability of higher throughput devices at lower cost has contributed to this generation of huge volumetric data. Handling such numerous data has become extremely challenging for selecting the relevant disease-causing gene. The development of microarray technology provides higher chances of cancer diagnosis, by enabling to measure the expression level of multiple genes at the same stretch. Selecting the relevant gene by using classifiers for investigation of gene expression data is a complicated process. Proper identification of gene from the gene expression datasets plays a vital role in improving the accuracy of classification. In this article, identification of the highly relevant gene from the gene expression data for cancer treatment is discussed in detail. By using modified meta-heuristic approach, known as 'parallel lion optimization' (PLOA) for selecting genes from microarray data that can classify various cancer sub-types with more accuracy. The experimental results depict that PLOA outperforms than LOA and other well-known approaches, considering the five benchmark cancer gene expression dataset. It returns 99% classification accuracy for the dataset namely Prostate, Lung, Leukemia and Central Nervous system (CNS) for top 200 genes. Prostate and Lymphoma dataset PLOA is 99.19% and 99.93% respectively. On evaluating the result with other algorithm, the higher level of accuracy in gene selection is achieved by the proposed algorithm.

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