Fuzzy Expert System based on a Novel Hybrid Stem Cell (HSC) Algorithm for Classification of Micro Array Data.
In the growing scenario, microarray data is extensively used since it provides a more comprehensive understanding of genetic variants among diseases. As the gene expression samples have high dimensionality it becomes tedious to analyze the samples manually. Hence an automated system is needed to ana...
| Published in: | Journal of Medical Systems Vol. 42; no. 4; pp. 1 - 2 |
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| Main Authors: | , |
| Format: | research Journal Article |
| Published: |
Springer Nature
Apr2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=128680933&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128680933 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Apr2018 vid: 42 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 128680933 128680933 128680933 10.1007/s10916-018-0910-0 128680933 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Fuzzy Expert System based on a Novel Hybrid Stem Cell (HSC) Algorithm for Classification of Micro Array Data. aug: au: Vijay, S. Arul Antran GaneshKumar, P. affil: Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, India sug: subj: Artificial Intelligence Algorithms Microarray Analysis Oligonucleotide Array Sequence Analysis Automation Simulations Gene Expression Classification Hereditary Diseases Diagnosis Hereditary Diseases Prevention and Control Diabetes Mellitus, Type 2 Familial and Genetic Insulin Sensitivity Familial and Genetic Insulin Resistance Familial and Genetic Colonic Neoplasms Familial and Genetic Prostatic Neoplasms Familial and Genetic Validity Human ab: In the growing scenario, microarray data is extensively used since it provides a more comprehensive understanding of genetic variants among diseases. As the gene expression samples have high dimensionality it becomes tedious to analyze the samples manually. Hence an automated system is needed to analyze these samples. The fuzzy expert system offers a clear classification when compared to the machine learning and statistical methodologies. In fuzzy classification, knowledge acquisition would be a major concern. Despite several existing approaches for knowledge acquisition much effort is necessary to enhance the learning process. This paper proposes an innovative Hybrid Stem Cell (HSC) algorithm that utilizes Ant Colony optimization and Stem Cell algorithm for designing fuzzy classification system to extract the informative rules to form the membership functions from the microarray dataset. The HSC algorithm uses a novel Adaptive Stem Cell Optimization (ASCO) to improve the points of membership function and Ant Colony Optimization to produce the near optimum rule set. In order to extract the most informative genes from the large microarray dataset a method called Mutual Information is used. The performance results of the proposed technique evaluated using the five microarray datasets are simulated. These results prove that the proposed Hybrid Stem Cell (HSC) algorithm produces a precise fuzzy system than the existing methodologies. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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