Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies
المؤلفون المشاركون
Wichmann, H. E.
Hofner, Benjamin
Friedrichs, Stefanie
Manitz, Juliane
Burger, Patricia
Amos, Christopher I.
Risch, Angela
Kneib, Thomas
Bickeböller, Heike
Chang-Claude, Jenny
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-17، 17ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-07-13
دولة النشر
مصر
عدد الصفحات
17
التخصصات الرئيسية
الملخص EN
The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways).
We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously.
We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm.
A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability.
We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths.
Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset.
Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios.
Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense.
Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing.
Additionally, it enables the prediction of clinical outcomes.
Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Friedrichs, Stefanie& Manitz, Juliane& Burger, Patricia& Amos, Christopher I.& Risch, Angela& Chang-Claude, Jenny…[et al.]. 2017. Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-17.
https://search.emarefa.net/detail/BIM-1142257
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Friedrichs, Stefanie…[et al.]. Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-17.
https://search.emarefa.net/detail/BIM-1142257
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Friedrichs, Stefanie& Manitz, Juliane& Burger, Patricia& Amos, Christopher I.& Risch, Angela& Chang-Claude, Jenny…[et al.]. Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-17.
https://search.emarefa.net/detail/BIM-1142257
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1142257
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر