Identification of Biomarker on Biological and Gene Expression data using Fuzzy Preference Based Rough Set

Cancer is fast becoming an alarming cause of human death. However, it has been reported that if the disease is detected at an early stage, diagnosed, treated appropriately, the patient has better chances of survival long life. Machine learning technique with feature-selection contributes greatly to...

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Autores principales:	Begum Shemim, Sarkar Ram, Chakraborty Debasis, Maulik Ujjwal
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	fuc flc fgc biomarkers fprs Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/cc37f716521d4f52a18efb345ecb9518
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id	oai:doaj.org-article:cc37f716521d4f52a18efb345ecb9518
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spelling	oai:doaj.org-article:cc37f716521d4f52a18efb345ecb95182021-12-05T14:10:51ZIdentification of Biomarker on Biological and Gene Expression data using Fuzzy Preference Based Rough Set2191-026X10.1515/jisys-2019-0034https://doaj.org/article/cc37f716521d4f52a18efb345ecb95182020-07-01T00:00:00Zhttps://doi.org/10.1515/jisys-2019-0034https://doaj.org/toc/2191-026XCancer is fast becoming an alarming cause of human death. However, it has been reported that if the disease is detected at an early stage, diagnosed, treated appropriately, the patient has better chances of survival long life. Machine learning technique with feature-selection contributes greatly to the detecting of cancer, because an efficient feature-selection method can remove redundant features. In this paper, a Fuzzy Preference-Based Rough Set (FPRS) blended with Support Vector Machine (SVM) has been applied in order to predict cancer biomarkers for biological and gene expression datasets. Biomarkers are determined by deploying three models of FPRS, namely, Fuzzy Upward Consistency (FUC), Fuzzy Downward Consistency (FLC), and Fuzzy Global Consistency (FGC). The efficiency of the three models with SVM on five datasets is exhibited, and the biomarkers that have been identified from FUC models have been reported.Begum ShemimSarkar RamChakraborty DebasisMaulik UjjwalDe GruyterarticlefucflcfgcbiomarkersfprsScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 130-141 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	fuc flc fgc biomarkers fprs Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	fuc flc fgc biomarkers fprs Science Q Electronic computers. Computer science QA75.5-76.95 Begum Shemim Sarkar Ram Chakraborty Debasis Maulik Ujjwal Identification of Biomarker on Biological and Gene Expression data using Fuzzy Preference Based Rough Set
description	Cancer is fast becoming an alarming cause of human death. However, it has been reported that if the disease is detected at an early stage, diagnosed, treated appropriately, the patient has better chances of survival long life. Machine learning technique with feature-selection contributes greatly to the detecting of cancer, because an efficient feature-selection method can remove redundant features. In this paper, a Fuzzy Preference-Based Rough Set (FPRS) blended with Support Vector Machine (SVM) has been applied in order to predict cancer biomarkers for biological and gene expression datasets. Biomarkers are determined by deploying three models of FPRS, namely, Fuzzy Upward Consistency (FUC), Fuzzy Downward Consistency (FLC), and Fuzzy Global Consistency (FGC). The efficiency of the three models with SVM on five datasets is exhibited, and the biomarkers that have been identified from FUC models have been reported.
format	article
author	Begum Shemim Sarkar Ram Chakraborty Debasis Maulik Ujjwal
author_facet	Begum Shemim Sarkar Ram Chakraborty Debasis Maulik Ujjwal
author_sort	Begum Shemim
title	Identification of Biomarker on Biological and Gene Expression data using Fuzzy Preference Based Rough Set
title_short	Identification of Biomarker on Biological and Gene Expression data using Fuzzy Preference Based Rough Set
title_full	Identification of Biomarker on Biological and Gene Expression data using Fuzzy Preference Based Rough Set
title_fullStr	Identification of Biomarker on Biological and Gene Expression data using Fuzzy Preference Based Rough Set
title_full_unstemmed	Identification of Biomarker on Biological and Gene Expression data using Fuzzy Preference Based Rough Set
title_sort	identification of biomarker on biological and gene expression data using fuzzy preference based rough set
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/cc37f716521d4f52a18efb345ecb9518
work_keys_str_mv	AT begumshemim identificationofbiomarkeronbiologicalandgeneexpressiondatausingfuzzypreferencebasedroughset AT sarkarram identificationofbiomarkeronbiologicalandgeneexpressiondatausingfuzzypreferencebasedroughset AT chakrabortydebasis identificationofbiomarkeronbiologicalandgeneexpressiondatausingfuzzypreferencebasedroughset AT maulikujjwal identificationofbiomarkeronbiologicalandgeneexpressiondatausingfuzzypreferencebasedroughset
_version_	1718371690113662976

Identification of Biomarker on Biological and Gene Expression data using Fuzzy Preference Based Rough Set

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