Assessment of Reliability of Microarray Data Using Fuzzy C-Means Classification
A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. Therefore, elimination of unreliable signal intensities will e...
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| creator | Alci, Musa Asyali, Musa H. |
| description | A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. Therefore, elimination of unreliable signal intensities will enhance reproducibility and reliability of gene expression ratios produced from the microarray data. In this study, we applied Fuzzy c-Means classification method to separate microarray data into low (or unreliable) and high (or reliable) signal intensity populations. We compared results of fuzzy classification with that of classification based on normal mixture modeling. Both approaches were validated against reference sets of biological data consisting of only true positives and negatives. We observed that both methods performed equally well in terms of sensitivity and specificity. However, a comparison of the computation times indicated that the fuzzy approach is computationally more efficient. |
| doi_str_mv | 10.1007/978-3-540-30499-9_206 |
| format | Conference Proceeding |
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Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. Therefore, elimination of unreliable signal intensities will enhance reproducibility and reliability of gene expression ratios produced from the microarray data. In this study, we applied Fuzzy c-Means classification method to separate microarray data into low (or unreliable) and high (or reliable) signal intensity populations. We compared results of fuzzy classification with that of classification based on normal mixture modeling. Both approaches were validated against reference sets of biological data consisting of only true positives and negatives. We observed that both methods performed equally well in terms of sensitivity and specificity. However, a comparison of the computation times indicated that the fuzzy approach is computationally more efficient.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540239314</identifier><identifier>ISBN: 3540239316</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540304991</identifier><identifier>EISBN: 9783540304999</identifier><identifier>DOI: 10.1007/978-3-540-30499-9_206</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Central Processing Unit ; Computer science; control theory; systems ; Connectionism. 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Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. Therefore, elimination of unreliable signal intensities will enhance reproducibility and reliability of gene expression ratios produced from the microarray data. In this study, we applied Fuzzy c-Means classification method to separate microarray data into low (or unreliable) and high (or reliable) signal intensity populations. We compared results of fuzzy classification with that of classification based on normal mixture modeling. Both approaches were validated against reference sets of biological data consisting of only true positives and negatives. We observed that both methods performed equally well in terms of sensitivity and specificity. However, a comparison of the computation times indicated that the fuzzy approach is computationally more efficient.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Central Processing Unit</subject><subject>Computer science; control theory; systems</subject><subject>Connectionism. Neural networks</subject><subject>Decision Boundary</subject><subject>Exact sciences and technology</subject><subject>Expectation Maximization Algorithm</subject><subject>Fuzzy Cluster</subject><subject>Microarray Data</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540239314</isbn><isbn>3540239316</isbn><isbn>3540304991</isbn><isbn>9783540304999</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2004</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNotkE1PwzAMhsOXxBj7CUi9cAwkcdosx6kwQNo0CbFz5GbJFOjaqSmH7teTbfhi2X79yn4IeeDsiTOmnrWaUqC5ZBSY1JpqI1hxQe4gtU4dfklGvOCcAkh9RSZp4TgToIHLazJKKkG1knBLJjF-sxSC6VzpEVnNYnQx7lzTZ63PPl0dsAp16IdjuQy2a7HrcMhesMdsHUOzzea_h8OQlXTpsIlZWWOMwQeLfWibe3LjsY5u8p_HZD1__Srf6WL19lHOFnQvhO4pAgquGC-k21i74RIkh42WyJQHL6xO13kFImfO86KSBZ-i5XmlvGNWoIMxeTz77jFarH2HjQ3R7Luww24wyViKPH08JnDWxTRqtq4zVdv-RMOZObI1CZUBk2CZE0lzYgt_qpRnLw</recordid><startdate>2004</startdate><enddate>2004</enddate><creator>Alci, Musa</creator><creator>Asyali, Musa H.</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2004</creationdate><title>Assessment of Reliability of Microarray Data Using Fuzzy C-Means Classification</title><author>Alci, Musa ; Asyali, Musa H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p229t-a3a2170164edccd143413d94a07f3f2c9957f73250ef16b4618ac15b7fe0c2ae3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Central Processing Unit</topic><topic>Computer science; control theory; systems</topic><topic>Connectionism. Neural networks</topic><topic>Decision Boundary</topic><topic>Exact sciences and technology</topic><topic>Expectation Maximization Algorithm</topic><topic>Fuzzy Cluster</topic><topic>Microarray Data</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Alci, Musa</creatorcontrib><creatorcontrib>Asyali, Musa H.</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Alci, Musa</au><au>Asyali, Musa H.</au><au>Pal, Nikhil Ranjan</au><au>Parui, Swapan Kumar</au><au>Pal, Srimanta</au><au>Mudi, Rajani K.</au><au>Kasabov, Nik</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Assessment of Reliability of Microarray Data Using Fuzzy C-Means Classification</atitle><btitle>Lecture notes in computer science</btitle><date>2004</date><risdate>2004</risdate><spage>1322</spage><epage>1327</epage><pages>1322-1327</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540239314</isbn><isbn>3540239316</isbn><eisbn>3540304991</eisbn><eisbn>9783540304999</eisbn><abstract>A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. Therefore, elimination of unreliable signal intensities will enhance reproducibility and reliability of gene expression ratios produced from the microarray data. In this study, we applied Fuzzy c-Means classification method to separate microarray data into low (or unreliable) and high (or reliable) signal intensity populations. We compared results of fuzzy classification with that of classification based on normal mixture modeling. Both approaches were validated against reference sets of biological data consisting of only true positives and negatives. We observed that both methods performed equally well in terms of sensitivity and specificity. 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| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Lecture notes in computer science, 2004, p.1322-1327 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_16442574 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Central Processing Unit Computer science control theory systems Connectionism. Neural networks Decision Boundary Exact sciences and technology Expectation Maximization Algorithm Fuzzy Cluster Microarray Data |
| title | Assessment of Reliability of Microarray Data Using Fuzzy C-Means Classification |
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