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Studying Microarray...
Studying Microarray Gene Expression Data of Schizophrenic Patients for Derivation of a Diagnostic Signature through the Aid of Machine Learning
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- Logotheti, Marianthi, 1986- (författare)
- Örebro universitet,Institutionen för medicinska vetenskaper,Metabolic Engineering and Bioinformatics Group, Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece
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- Pilalis, Eleftherios (författare)
- Metabolic Engineering and Bioinformatics Group, Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece; e-NIOS Applications PC, Athens, Greece
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- Venizelos, Nikolaos, 1946- (författare)
- Neuropsychiatric Research Laboratory, Faculty of Medicine and Health, School of Health and Medical Sciences, Örebro University, Örebro, Sweden
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- Kolisis, Fragiskos (författare)
- Laboratory of Biotechnology, School of Chemical Engineering, National Technical University of Athens, Athens, Greece
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- Chatziioannou, Aristotelis (författare)
- Metabolic Engineering and Bioinformatics Group, Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece; e-NIOS Applications PC, Athens, Greece
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(creator_code:org_t)
- MedCrave, 2016
- 2016
- Engelska.
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Ingår i: Biometrics & Biostatistics International Journal. - : MedCrave. - 2378-315X. ; 4:5
- Relaterad länk:
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http://medcraveonlin...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- Schizophrenia is a complex psychiatric disease that is affected by multiple genes, some of which could be used as biomarkers for specific diagnosis of the disease. In this work, we explore the power of machine learning methodologies for predicting schizophrenia, through the derivation of a biomarker gene signature for robust diagnostic classification purposes. Postmortem brain gene expression data from the anterior prefrontal cortex of schizophrenia patients were used as training data for the construction of the classifiers. Several machine learning algorithms, such as support vector machines, random forests, and extremely randomized trees classifiers were developed and their performance was tested. After applying the feature selection method of support vector machines recursive feature elimination a 21-gene model was derived. Using these genes for developing classification models, the random forests algorithm outperformed all examined algorithms achieving an area under the curve of 0.98 and sensitivity of 0.89, discriminating schizophrenia from healthy control samples with high efficiency. The 21-gene model that was derived from the feature selection is suggested for classifying schizophrenic patients, as it was successfully applied on an independent dataset of postmortem brain samples from the superior temporal cortex, and resulted in a classification model that achieved an area under the curve score of 0.91. Additionally, the functional analysis of the statistically significant genes indicated many mechanisms related to the immune system.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Andra medicinska och farmaceutiska grundvetenskaper (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Other Basic Medicine (hsv//eng)
Nyckelord
- Classification
- Schizophrenia
- Machine learning
- Gene expression
- Microarray studies
- Support vector machines
- Adaboost
- Biomedicin
- Biomedicine
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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