Applying bioinformatics to proteomics: is machine learning the answer to biomarker discovery for PD and MSA?
Identifieur interne : 000E83 ( Main/Exploration ); précédent : 000E82; suivant : 000E84Applying bioinformatics to proteomics: is machine learning the answer to biomarker discovery for PD and MSA?
Auteurs : Hayley A. Mattison [États-Unis] ; Tessandra Stewart ; Jing ZhangSource :
- Movement disorders : official journal of the Movement Disorder Society [ 1531-8257 ] ; 2012.
English descriptors
- KwdEn :
- MESH :
- chemical , cerebrospinal fluid : Cerebrospinal Fluid Proteins.
- cerebrospinal fluid : Multiple System Atrophy, Parkinson Disease.
- diagnosis : Multiple System Atrophy, Parkinson Disease.
- methods : Proteomics.
- Female, Humans, Male.
Abstract
Bioinformatics tools are increasingly being applied to proteomic data to facilitate the identification of biomarkers and classification of patients. In the June, 2012 issue, Ishigami et al. used principal component analysis (PCA) to extract features and support vector machine (SVM) to differentiate and classify cerebrospinal fluid (CSF) samples from two small cohorts of patients diagnosed with either Parkinson's disease (PD) or multiple system atrophy (MSA) based on differences in the patterns of peaks generated with matrix-assisted desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). PCA accurately segregated patients with PD and MSA from controls when the cohorts were combined, but did not perform well when segregating PD from MSA. On the other hand, SVM, a machine learning classification model, correctly classified the samples from patients with early PD or MSA, and the peak at m/z 6250 was identified as a strong contributor to the ability of SVM to distinguish the proteomic profiles of either cohort when trained on one cohort. This study, while preliminary, provides promising results for the application of bioinformatics tools to proteomic data, an approach that may eventually facilitate the ability of clinicians to differentiate and diagnose closely related parkinsonian disorders.
DOI: 10.1002/mds.25189
PubMed: 23115026
Affiliations:
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Mattison</name><affiliation wicri:level="4"><nlm:affiliation>Department of Pathology, University of Washington, Seattle, Washington 98104, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Pathology, University of Washington, Seattle, Washington 98104</wicri:regionArea><orgName type="university">Université de Washington</orgName><placeName><settlement type="city">Seattle</settlement><region type="state">Washington (État)</region></placeName></affiliation></author><author><name sortKey="Stewart, Tessandra" sort="Stewart, Tessandra" uniqKey="Stewart T" first="Tessandra" last="Stewart">Tessandra Stewart</name></author><author><name sortKey="Zhang, Jing" sort="Zhang, Jing" uniqKey="Zhang J" first="Jing" last="Zhang">Jing Zhang</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2012">2012</date><idno type="doi">10.1002/mds.25189</idno><idno type="RBID">pubmed:23115026</idno><idno type="pmid">23115026</idno><idno type="wicri:Area/PubMed/Corpus">000B93</idno><idno type="wicri:Area/PubMed/Curation">000B93</idno><idno type="wicri:Area/PubMed/Checkpoint">000F40</idno><idno type="wicri:Area/Ncbi/Merge">003869</idno><idno type="wicri:Area/Ncbi/Curation">003869</idno><idno type="wicri:Area/Ncbi/Checkpoint">003869</idno><idno type="wicri:Area/Main/Merge">000F31</idno><idno type="wicri:Area/Main/Curation">000E83</idno><idno type="wicri:Area/Main/Exploration">000E83</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Applying bioinformatics to proteomics: is machine learning the answer to biomarker discovery for PD and MSA?</title><author><name sortKey="Mattison, Hayley A" sort="Mattison, Hayley A" uniqKey="Mattison H" first="Hayley A" last="Mattison">Hayley A. Mattison</name><affiliation wicri:level="4"><nlm:affiliation>Department of Pathology, University of Washington, Seattle, Washington 98104, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Pathology, University of Washington, Seattle, Washington 98104</wicri:regionArea><orgName type="university">Université de Washington</orgName><placeName><settlement type="city">Seattle</settlement><region type="state">Washington (État)</region></placeName></affiliation></author><author><name sortKey="Stewart, Tessandra" sort="Stewart, Tessandra" uniqKey="Stewart T" first="Tessandra" last="Stewart">Tessandra Stewart</name></author><author><name sortKey="Zhang, Jing" sort="Zhang, Jing" uniqKey="Zhang J" first="Jing" last="Zhang">Jing Zhang</name></author></analytic><series><title level="j">Movement disorders : official journal of the Movement Disorder Society</title><idno type="eISSN">1531-8257</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Cerebrospinal Fluid Proteins (cerebrospinal fluid)</term><term>Female</term><term>Humans</term><term>Male</term><term>Multiple System Atrophy (cerebrospinal fluid)</term><term>Multiple System Atrophy (diagnosis)</term><term>Parkinson Disease (cerebrospinal fluid)</term><term>Parkinson Disease (diagnosis)</term><term>Proteomics (methods)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="cerebrospinal fluid" xml:lang="en"><term>Cerebrospinal Fluid Proteins</term></keywords><keywords scheme="MESH" qualifier="cerebrospinal fluid" xml:lang="en"><term>Multiple System Atrophy</term><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Multiple System Atrophy</term><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Proteomics</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Female</term><term>Humans</term><term>Male</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Bioinformatics tools are increasingly being applied to proteomic data to facilitate the identification of biomarkers and classification of patients. In the June, 2012 issue, Ishigami et al. used principal component analysis (PCA) to extract features and support vector machine (SVM) to differentiate and classify cerebrospinal fluid (CSF) samples from two small cohorts of patients diagnosed with either Parkinson's disease (PD) or multiple system atrophy (MSA) based on differences in the patterns of peaks generated with matrix-assisted desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). PCA accurately segregated patients with PD and MSA from controls when the cohorts were combined, but did not perform well when segregating PD from MSA. On the other hand, SVM, a machine learning classification model, correctly classified the samples from patients with early PD or MSA, and the peak at m/z 6250 was identified as a strong contributor to the ability of SVM to distinguish the proteomic profiles of either cohort when trained on one cohort. This study, while preliminary, provides promising results for the application of bioinformatics tools to proteomic data, an approach that may eventually facilitate the ability of clinicians to differentiate and diagnose closely related parkinsonian disorders.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Washington (État)</li></region><settlement><li>Seattle</li></settlement><orgName><li>Université de Washington</li></orgName></list><tree><noCountry><name sortKey="Stewart, Tessandra" sort="Stewart, Tessandra" uniqKey="Stewart T" first="Tessandra" last="Stewart">Tessandra Stewart</name><name sortKey="Zhang, Jing" sort="Zhang, Jing" uniqKey="Zhang J" first="Jing" last="Zhang">Jing Zhang</name></noCountry><country name="États-Unis"><region name="Washington (État)"><name sortKey="Mattison, Hayley A" sort="Mattison, Hayley A" uniqKey="Mattison H" first="Hayley A" last="Mattison">Hayley A. Mattison</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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