Hybrid Feature Selection of Breast Cancer Gene Expression Microarray Data Based on Metaheuristic Methods: A Comprehensive Review
نویسندگان
چکیده
Breast cancer (BC) remains the most dominant among women worldwide. Numerous BC gene expression microarray-based studies have been employed in classification and prognosis. The availability of microarray data together with advanced methods has enabled accurate precise classification. Nevertheless, datasets suffer from a large number levels, limited sample size, irrelevant features. Additionally, are often asymmetrical, where samples different classes is not balanced. These limitations make it difficult to determine actual features that contribute existence profiles. Various feature selection exist, they being widely applied. objective search for relevant, discriminant subset basic space. In this review, we aim compile review latest hybrid based on bio-inspired metaheuristic wrapper other types cancer.
منابع مشابه
Prediction of blood cancer using leukemia gene expression data and sparsity-based gene selection methods
Background: DNA microarray is a useful technology that simultaneously assesses the expression of thousands of genes. It can be utilized for the detection of cancer types and cancer biomarkers. This study aimed to predict blood cancer using leukemia gene expression data and a robust ℓ2,p-norm sparsity-based gene selection method. Materials and Methods: In this descriptive study, the microarray ...
متن کاملFeature Selection for Cancer Classification Using Microarray Gene Expression Data
The DNA microarray technology enables us to measure the expression levels of thousands of genes simultaneously, providing great chance for cancer diagnosis and prognosis. The number of genes often exceeds tens of thousands, whereas the number of subjects available is often no more than a hundred. Therefore, it is necessary and important to perform gene selection for classification purpose. A go...
متن کاملFeature Selection and Classification of Microarray Gene Expression Data of Ovarian Carcinoma Patients using Weighted Voting Support Vector Machine
We can reach by DNA microarray gene expression to such wealth of information with thousands of variables (genes). Analysis of this information can show genetic reasons of disease and tumor differences. In this study we try to reduce high-dimensional data by statistical method to select valuable genes with high impact as biomarkers and then classify ovarian tumor based on gene expression data of...
متن کاملFeature Selection and Classification of MAQC-II Breast Cancer and Multiple Myeloma Microarray Gene Expression Data
Microarray data has a high dimension of variables but available datasets usually have only a small number of samples, thereby making the study of such datasets interesting and challenging. In the task of analyzing microarray data for the purpose of, e.g., predicting gene-disease association, feature selection is very important because it provides a way to handle the high dimensionality by explo...
متن کاملA hybrid method of feature selection for microarray gene expression data
Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. Compared to the number of genes involved, available training data sets generally have a fairly small sample size in cancer type classification. These training data limitations constitute a challenge to certain classification methodologies. A reliable selection me...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Symmetry
سال: 2022
ISSN: ['0865-4824', '2226-1877']
DOI: https://doi.org/10.3390/sym14101955