Filters








6 Hits in 5.9 sec

Recursive Fuzzy Granulation for Gene Subsets Extraction and Cancer Classification

Yuchun Tang, Yan-Qing Zhang, Zhen Huang, Xiaohua Hu, Yichuan Zhao
2008 IEEE Transactions on Information Technology in Biomedicine  
To select multiple highly informative gene subsets for cancer classification and diagnosis, a new Fuzzy Granular Support Vector Machine-Recursive Feature Elimination algorithm (FGSVM-RFE) is designed in  ...  A typical microarray gene expression dataset is usually both extremely sparse and imbalanced.  ...  For example, one well-known challenge using gene expression microarray data is to distinguish between two variants of leukemia, acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) [2]  ... 
doi:10.1109/titb.2008.920787 pmid:19000951 fatcat:qfhlexcpdfc6ffihpawqeez2zm

Review on Classification of Genes and Biomarker Identification

Seema S, Hamida Honnalli
2013 International Journal of Computer Applications  
Recent advances in the DNA microarray technology have provided the ability to examine and measure the expression levels of thousands of genes simultaneously in an organism.  ...  genes whose expression levels are good diagnostic indicators.  ...  And the best feature subsets selected by MSVM-RFE give better classification accuracy than the best feature subsets selected by SVM-RFE.  ... 
doi:10.5120/11669-7266 fatcat:zrr6ujtvfzb75fh7wrys752y5q

Classification algorithms for phenotype prediction in genomics and proteomics

Habtom, W. Ressom
2008 Frontiers in Bioscience  
In particular, the paper focuses on the use of these computational methods for gene and peak selection from microarray and mass spectrometry data, respectively.  ...  This paper gives an overview of statistical and machine learning-based feature selection and pattern classification algorithms and their application in molecular cancer classification or phenotype prediction  ...  SVM-recursive feature elimination (SVM-RFE) is a special purpose feature selection algorithm for SVMs.  ... 
doi:10.2741/2712 pmid:17981580 pmcid:PMC2204040 fatcat:xuolvcm6ajh73lfp4c7wi7osiu

A Fuzzy Brain Emotional Learning Classifier Design and Application in Medical Diagnosis

2019 Acta Polytechnica Hungarica  
Finally, the proposed method is applied for the leukemia classification and the diagnosis of heart disease.  ...  Meanwhile, different from a brain emotional learning controller, a novel definition of the reward signal is developed, which is more suitable for classification.  ...  b) Experiment Methods Previously, various types of gene selection methods are applied for classification on the Leukemia Datasets.  ... 
doi:10.12700/aph.16.4.2019.4.2 fatcat:qdgmbwybjzbmzl6vgerfmhvpku

FUZZY C-MEANS AND ENTROPY BASED GENE SELECTION BY PRINCIPAL COMPONENT ANALYSIS IN CANCER CLASSIFICATION 1

Somayeh Abbasi, Hamid Mahmoodian
2014 Journal of Theoretical and Applied Information Technology   unpublished
Colon cancer, leukemia and lung datasets have been classified based on proposed gene selection algorithm by SVM and KNN classifiers.  ...  Gene selection is a significant preprocessing of the discriminant analysis of microarray data to select the most informative genes from thousands of genes.  ...  The absolute value of PCC can be used for gene ranking. SVM-RFE Guyon et al. [4] presented a gene selection method by employing SVM classifier based on Recursive Feature Elimination (RFE).  ... 
fatcat:pqegzphyqndmnfrtslq4innjqu

A percentual learning model to discover the hierarchical latent structure of image collections

Davide Bacciu
2008
focus for the neural representations of the data.  ...  Particular emphasis is placed on validating the model as an effective tool for the unsupervised exploration of bio-medical data.  ...  To compare the quality of CoRe's feature ranking with other popular feature selection algorithms from microarray data analysis we force CoRe to identify the relevant genes in the 2-class task on the Leukemia  ... 
doi:10.6092/imtlucca/e-theses/7 fatcat:evv3d4ol7fcdhjgqma743gfn3y