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The application of naive Bayes model averaging to predict Alzheimer's disease from genome-wide data
Predicting patient outcomes from genome-wide measurements holds significant promise for improving clinical care. The large number of measurements (eg, single nucleotide polymorphisms (SNPs)), however, makes this task computationally challenging. This paper evaluates the performance of an algorithm t...
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Published in: | Journal of the American Medical Informatics Association : JAMIA 2011-07, Vol.18 (4), p.370-375 |
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creator | Wei, Wei Visweswaran, Shyam Cooper, Gregory F |
description | Predicting patient outcomes from genome-wide measurements holds significant promise for improving clinical care. The large number of measurements (eg, single nucleotide polymorphisms (SNPs)), however, makes this task computationally challenging. This paper evaluates the performance of an algorithm that predicts patient outcomes from genome-wide data by efficiently model averaging over an exponential number of naive Bayes (NB) models.
This model-averaged naive Bayes (MANB) method was applied to predict late onset Alzheimer's disease in 1411 individuals who each had 312,318 SNP measurements available as genome-wide predictive features. Its performance was compared to that of a naive Bayes algorithm without feature selection (NB) and with feature selection (FSNB).
Performance of each algorithm was measured in terms of area under the ROC curve (AUC), calibration, and run time.
The training time of MANB (16.1 s) was fast like NB (15.6 s), while FSNB (1684.2 s) was considerably slower. Each of the three algorithms required less than 0.1 s to predict the outcome of a test case. MANB had an AUC of 0.72, which is significantly better than the AUC of 0.59 by NB (p |
doi_str_mv | 10.1136/amiajnl-2011-000101 |
format | article |
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This model-averaged naive Bayes (MANB) method was applied to predict late onset Alzheimer's disease in 1411 individuals who each had 312,318 SNP measurements available as genome-wide predictive features. Its performance was compared to that of a naive Bayes algorithm without feature selection (NB) and with feature selection (FSNB).
Performance of each algorithm was measured in terms of area under the ROC curve (AUC), calibration, and run time.
The training time of MANB (16.1 s) was fast like NB (15.6 s), while FSNB (1684.2 s) was considerably slower. Each of the three algorithms required less than 0.1 s to predict the outcome of a test case. MANB had an AUC of 0.72, which is significantly better than the AUC of 0.59 by NB (p<0.00001), but not significantly different from the AUC of 0.71 by FSNB. MANB was better calibrated than NB, and FSNB was even better in calibration. A limitation was that only one dataset and two comparison algorithms were included in this study.
MANB performed comparatively well in predicting a clinical outcome from a high-dimensional genome-wide dataset. These results provide support for including MANB in the methods used to predict outcomes from large, genome-wide datasets.</description><identifier>ISSN: 1067-5027</identifier><identifier>EISSN: 1527-974X</identifier><identifier>DOI: 10.1136/amiajnl-2011-000101</identifier><identifier>PMID: 21672907</identifier><language>eng</language><publisher>England: BMJ Group</publisher><subject>Aged ; Aged, 80 and over ; Algorithms ; Alzheimer Disease - diagnosis ; Alzheimer Disease - genetics ; Apolipoproteins E - genetics ; Artificial Intelligence ; Bayes Theorem ; Case-Control Studies ; Genome-Wide Association Study ; Humans ; Models, Genetic ; Polymorphism, Single Nucleotide ; Prognosis ; Research and Applications ; ROC Curve</subject><ispartof>Journal of the American Medical Informatics Association : JAMIA, 2011-07, Vol.18 (4), p.370-375</ispartof><rights>2011, Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions. 2011</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c486t-4ef80c9291a329824eb66e92151eae2ce9545fc0c7921bd9bec9e60c5e610043</citedby><cites>FETCH-LOGICAL-c486t-4ef80c9291a329824eb66e92151eae2ce9545fc0c7921bd9bec9e60c5e610043</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3128400/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3128400/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21672907$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wei, Wei</creatorcontrib><creatorcontrib>Visweswaran, Shyam</creatorcontrib><creatorcontrib>Cooper, Gregory F</creatorcontrib><title>The application of naive Bayes model averaging to predict Alzheimer's disease from genome-wide data</title><title>Journal of the American Medical Informatics Association : JAMIA</title><addtitle>J Am Med Inform Assoc</addtitle><description>Predicting patient outcomes from genome-wide measurements holds significant promise for improving clinical care. The large number of measurements (eg, single nucleotide polymorphisms (SNPs)), however, makes this task computationally challenging. This paper evaluates the performance of an algorithm that predicts patient outcomes from genome-wide data by efficiently model averaging over an exponential number of naive Bayes (NB) models.
This model-averaged naive Bayes (MANB) method was applied to predict late onset Alzheimer's disease in 1411 individuals who each had 312,318 SNP measurements available as genome-wide predictive features. Its performance was compared to that of a naive Bayes algorithm without feature selection (NB) and with feature selection (FSNB).
Performance of each algorithm was measured in terms of area under the ROC curve (AUC), calibration, and run time.
The training time of MANB (16.1 s) was fast like NB (15.6 s), while FSNB (1684.2 s) was considerably slower. Each of the three algorithms required less than 0.1 s to predict the outcome of a test case. MANB had an AUC of 0.72, which is significantly better than the AUC of 0.59 by NB (p<0.00001), but not significantly different from the AUC of 0.71 by FSNB. MANB was better calibrated than NB, and FSNB was even better in calibration. A limitation was that only one dataset and two comparison algorithms were included in this study.
MANB performed comparatively well in predicting a clinical outcome from a high-dimensional genome-wide dataset. These results provide support for including MANB in the methods used to predict outcomes from large, genome-wide datasets.</description><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Algorithms</subject><subject>Alzheimer Disease - diagnosis</subject><subject>Alzheimer Disease - genetics</subject><subject>Apolipoproteins E - genetics</subject><subject>Artificial Intelligence</subject><subject>Bayes Theorem</subject><subject>Case-Control Studies</subject><subject>Genome-Wide Association Study</subject><subject>Humans</subject><subject>Models, Genetic</subject><subject>Polymorphism, Single Nucleotide</subject><subject>Prognosis</subject><subject>Research and Applications</subject><subject>ROC Curve</subject><issn>1067-5027</issn><issn>1527-974X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNqFkU1r3DAQhkVpyfcvKBTdcnIykmXJugTSkKSFQC97yE3MyuNdBdtyJe-W5NfXYbehOeU0w8w7LzPzMPZVwIUQpb7EPuDT0BUShCgAQID4xI5EJU1hjXr8POegTVGBNIfsOOenWaJlWR2wQym0kRbMEfOLNXEcxy54nEIceGz5gGFL_Ds-U-Z9bKjjuKWEqzCs-BT5mKgJfuLX3cuaQk_pPPMmZMJMvE2x5ysaYk_Fn9AQb3DCU_alxS7T2T6esMXd7eLmR_Hw6_7nzfVD4VWtp0JRW4O30gospa2loqXWZKWoBCFJT7ZSVevBm7m2bOySvCUNviItAFR5wq52tuNm2VPjaZgSdm5Mocf07CIG974zhLVbxa0rhawVwGxwvjdI8feG8uT6kD11HQ4UN9lZUKqev2k_VNZGilJCZWZluVP6FHNO1L7tI8C9YnR7jO4Vo9thnKe-_X_K28w_buVfBxecDg</recordid><startdate>20110701</startdate><enddate>20110701</enddate><creator>Wei, Wei</creator><creator>Visweswaran, Shyam</creator><creator>Cooper, Gregory F</creator><general>BMJ Group</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>5PM</scope></search><sort><creationdate>20110701</creationdate><title>The application of naive Bayes model averaging to predict Alzheimer's disease from genome-wide data</title><author>Wei, Wei ; Visweswaran, Shyam ; Cooper, Gregory F</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c486t-4ef80c9291a329824eb66e92151eae2ce9545fc0c7921bd9bec9e60c5e610043</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Algorithms</topic><topic>Alzheimer Disease - diagnosis</topic><topic>Alzheimer Disease - genetics</topic><topic>Apolipoproteins E - genetics</topic><topic>Artificial Intelligence</topic><topic>Bayes Theorem</topic><topic>Case-Control Studies</topic><topic>Genome-Wide Association Study</topic><topic>Humans</topic><topic>Models, Genetic</topic><topic>Polymorphism, Single Nucleotide</topic><topic>Prognosis</topic><topic>Research and Applications</topic><topic>ROC Curve</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wei, Wei</creatorcontrib><creatorcontrib>Visweswaran, Shyam</creatorcontrib><creatorcontrib>Cooper, Gregory F</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of the American Medical Informatics Association : JAMIA</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wei, Wei</au><au>Visweswaran, Shyam</au><au>Cooper, Gregory F</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The application of naive Bayes model averaging to predict Alzheimer's disease from genome-wide data</atitle><jtitle>Journal of the American Medical Informatics Association : JAMIA</jtitle><addtitle>J Am Med Inform Assoc</addtitle><date>2011-07-01</date><risdate>2011</risdate><volume>18</volume><issue>4</issue><spage>370</spage><epage>375</epage><pages>370-375</pages><issn>1067-5027</issn><eissn>1527-974X</eissn><abstract>Predicting patient outcomes from genome-wide measurements holds significant promise for improving clinical care. The large number of measurements (eg, single nucleotide polymorphisms (SNPs)), however, makes this task computationally challenging. This paper evaluates the performance of an algorithm that predicts patient outcomes from genome-wide data by efficiently model averaging over an exponential number of naive Bayes (NB) models.
This model-averaged naive Bayes (MANB) method was applied to predict late onset Alzheimer's disease in 1411 individuals who each had 312,318 SNP measurements available as genome-wide predictive features. Its performance was compared to that of a naive Bayes algorithm without feature selection (NB) and with feature selection (FSNB).
Performance of each algorithm was measured in terms of area under the ROC curve (AUC), calibration, and run time.
The training time of MANB (16.1 s) was fast like NB (15.6 s), while FSNB (1684.2 s) was considerably slower. Each of the three algorithms required less than 0.1 s to predict the outcome of a test case. MANB had an AUC of 0.72, which is significantly better than the AUC of 0.59 by NB (p<0.00001), but not significantly different from the AUC of 0.71 by FSNB. MANB was better calibrated than NB, and FSNB was even better in calibration. A limitation was that only one dataset and two comparison algorithms were included in this study.
MANB performed comparatively well in predicting a clinical outcome from a high-dimensional genome-wide dataset. These results provide support for including MANB in the methods used to predict outcomes from large, genome-wide datasets.</abstract><cop>England</cop><pub>BMJ Group</pub><pmid>21672907</pmid><doi>10.1136/amiajnl-2011-000101</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Aged Aged, 80 and over Algorithms Alzheimer Disease - diagnosis Alzheimer Disease - genetics Apolipoproteins E - genetics Artificial Intelligence Bayes Theorem Case-Control Studies Genome-Wide Association Study Humans Models, Genetic Polymorphism, Single Nucleotide Prognosis Research and Applications ROC Curve |
title | The application of naive Bayes model averaging to predict Alzheimer's disease from genome-wide data |
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