Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes
المؤلفون المشاركون
Feng, Shengzhong
Liang, Yu
Wei, Yanjie
Jing, Runyu
Ran, Yi
He, Li
المصدر
International Journal of Genomics
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-01-10
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
In genetic data modeling, the use of a limited number of samples for modeling and predicting, especially well below the attribute number, is difficult due to the enormous number of genes detected by a sequencing platform.
In addition, many studies commonly use machine learning methods to evaluate genetic datasets to identify potential disease-related genes and drug targets, but to the best of our knowledge, the information associated with the selected gene set was not thoroughly elucidated in previous studies.
To identify a relatively stable scheme for modeling limited samples in the gene datasets and reveal the information that they contain, the present study first evaluated the performance of a series of modeling approaches for predicting clinical endpoints of cancer and later integrated the results using various voting protocols.
As a result, we proposed a relatively stable scheme that used a set of methods with an ensemble algorithm.
Our findings indicated that the ensemble methodologies are more reliable for predicting cancer prognoses than single machine learning algorithms as well as for gene function evaluating.
The ensemble methodologies provide a more complete coverage of relevant genes, which can facilitate the exploration of cancer mechanisms and the identification of potential drug targets.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Jing, Runyu& Liang, Yu& Ran, Yi& Feng, Shengzhong& Wei, Yanjie& He, Li. 2018. Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes. International Journal of Genomics،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1172869
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Jing, Runyu…[et al.]. Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes. International Journal of Genomics No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1172869
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Jing, Runyu& Liang, Yu& Ran, Yi& Feng, Shengzhong& Wei, Yanjie& He, Li. Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes. International Journal of Genomics. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1172869
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1172869
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