Results 41 to 50 of about 1,213,334 (312)

Combined Gene Selection Methods for Microarray Data Analysis [PDF]

, 2006
In recent years, the rapid development of DNA Microarray technology has made it possible for scientists to monitor the expression level of thousands of genes in a single experiment.
Hua Wang, Li, Jiuyong, Hong Hu, Jiuyong Li, Grant Daggard, Daggard, Grant, Hu, Hong, Wang, Hua   +7 more
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Selection, Gene Interaction, and Flexible Gene Networks [PDF]

Cold Spring Harbor Symposia on Quantitative Biology, 2009
Recent results from a variety of different kinds of experiments, mainly using behavior as an assay, and ranging from laboratory selection experiments to gene interaction studies, show that a much wider range of genes can affect phenotype than those identified as "core genes" in classical mutant screens.
openaire   +2 more sources

On the Effectiveness of Gene Selection for Microarray Classification Methods [PDF]

, 2010
Microarray data usually contains a high level of noisy gene data, the noisy gene data include incorrect, noise and irrelevant genes. Before Microarray data classification takes place, it is desirable to eliminate as much noisy data as possible.
Zhang, Zhongwei, Li, Jiuyong, Hong Hu, Hu, H., Jiuyong Li, Hu, Hong, Li, J., Hong Zhou, Zhou, Hong, Zhongwei Zhang, Zhang, Z., Zhou, H.   +11 more
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Mutational Slime Mould Algorithm for Gene Selection

Biomedicines, 2022
A large volume of high-dimensional genetic data has been produced in modern medicine and biology fields. Data-driven decision-making is particularly crucial to clinical practice and relevant procedures.
Feng Qiu, Pan Zheng, Ali Asghar Heidari, Guoxi Liang, Huiling Chen, Faten Khalid Karim, Hela Elmannai, Haiping Lin   +7 more
doaj   +1 more source

A Robust Gene Selection Method for Microarray-based Cancer Classification [PDF]

, 2010
Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust ...
Gotoh, Osamu, Wang, Xiaosheng, Osamu Gotoh, Xiaosheng Wang   +3 more
core   +2 more sources

Gene selection and classification for cancer microarray data based on machine learning and similarity measures

BMC Genomics, 2011
Background Microarray data have a high dimension of variables and a small sample size. In microarray data analyses, two important issues are how to choose genes, which provide reliable and good prediction for disease status, and how to determine the ...
Liu Qingzhong, Sung Andrew H, Chen Zhongxue, Liu Jianzhong, Chen Lei, Qiao Mengyu, Wang Zhaohui, Huang Xudong, Deng Youping   +8 more
doaj   +1 more source

Improving the Classification of Alzheimer’s Disease Using Hybrid Gene Selection Pipeline and Deep Learning

Frontiers in Genetics, 2021
Alzheimer’s is a progressive, irreversible, neurodegenerative brain disease. Even with prominent symptoms, it takes years to notice, decode, and reveal Alzheimer’s.
Nivedhitha Mahendran, P. M. Durai Raj Vincent, Kathiravan Srinivasan, Chuan-Yu Chang   +3 more
doaj   +1 more source

Lineage tree analysis of immunoglobulin variable-region gene mutations in autoimmune diseases: chronic activation, normal selection [PDF]

, 2006
Autoimmune diseases show high diversity in the affected organs, clinical manifestations and disease dynamics. Yet they all share common features, such as the ectopic germinal centers found in many affected tissues.
Neta S. Zuckerman, Zuckerman, N.S., Michal Barak, Roshini S. Abraham, Ramit Mehr, Mehr, Ramit, Steiman-Shimony, Avital, David I. Stott, Edelman, H., Hutzler, A., Edelman, Hanna, Zuckerman, Neta S., Stott, David I., Shahaf, G., Deborah Dunn-Walters, Abraham, Roshim S., Shahaf, Gitit, Stott, D.I., Gitit Shahaf, Dunn-Walters, D., Avital Steiman-Shimony, Hutzler, Anat, Barak, M., Steiman-Shimony, A., Barak, Michal, Mehr, R., Dunn-Walters, Deborah, Anat Hutzler, Hanna Edelman, Abraham, R.S.   +29 more
core   +1 more source

Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization

Applied Sciences, 2018
In recent years, gene selection for cancer classification based on the expression of a small number of gene biomarkers has been the subject of much research in genetics and molecular biology.
Shengbing Wu, Hongkun Jiang, Haiwei Shen, Ziyi Yang   +3 more
doaj   +1 more source

IMPROVED GENE SELECTION FOR CLASSIFICATION OF MICROARRAYS [PDF]

Biocomputing 2003, 2002
In this paper we derive a method for evaluating and improving techniques for selecting informative genes from microarray data. Genes of interest are typically selected by ranking genes according to a test-statistic and then choosing the top k genes. A problem with this approach is that many of these genes are highly correlated.
Jäger, J., Sengupta, R., Ruzzo, W.
openaire   +4 more sources