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An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms
المؤلفون المشاركون
Hua, Hong-Li
Zhang, Fa-Zhan
Labena, Abraham Alemayehu
Dong, Chuan
Jin, Yan-Ting
Guo, Feng-Biao
المصدر
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-08-30
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets.
In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes.
We focused on 25 bacteria which have characterized essential genes.
The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test.
Proper features were utilized to construct models to make predictions in distantly related bacteria.
The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species.
The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms.
An independent dataset from Synechococcus elongatus, which was released recently, was obtained for further assessment of the performance of our model.
The AUC score of predictions is 0.7855, which is higher than other methods.
This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features.
Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Hua, Hong-Li& Zhang, Fa-Zhan& Labena, Abraham Alemayehu& Dong, Chuan& Jin, Yan-Ting& Guo, Feng-Biao. 2016. An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms. BioMed Research International،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1098840
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Hua, Hong-Li…[et al.]. An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms. BioMed Research International No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1098840
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Hua, Hong-Li& Zhang, Fa-Zhan& Labena, Abraham Alemayehu& Dong, Chuan& Jin, Yan-Ting& Guo, Feng-Biao. An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1098840
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1098840
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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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
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