Selecting Biomarkers for Pluripotency and Alzheimer's Disease: The Real Strength of the GA/SVM

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Titel: Selecting Biomarkers for Pluripotency and Alzheimer's Disease: The Real Strength of the GA/SVM
Autor(en): Scheubert, Lena
Erstgutachter: Prof. Dr. Volker Sperschneider
Zweitgutachter: Prof. Dr. Georg Füllen
Zusammenfassung: Pluripotency and Alzheimer's disease are two very different biological states. Even so, they are similar in the lack of knowledge about their underlying molecular mechanisms. Identifying important genes well suited as biomarkers for these two states improves our understanding. We use different feature selection methods for the identification of important genes usable as potential biomarkers. Beside the identification of biomarkers for these two specific states we are also interested in general algorithms showing good results in biomarker detection. For this reason we compare three feature selection methods with each other. Particularly good results show a rarely noticed wrapper approach of genetic algorithm and support vector machine (GA/SVM). More detailed investigations of the results show the strength of the small gene sets selected by our GA/SVM. In our work we identify a number of promising biomarker candidates for pluripotency as well as for Alzheimer's disease. We also show that the GA/SVM is well suited for feature selection even if its potential is not yet exhausted.
URL: https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2012101610354
Schlagworte: machine learning; biomarker identification
Erscheinungsdatum: 16-Okt-2012
Enthalten in den Sammlungen:FB06 - E-Dissertationen

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