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An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms
Joint Authors
Hua, Hong-Li
Zhang, Fa-Zhan
Labena, Abraham Alemayehu
Dong, Chuan
Jin, Yan-Ting
Guo, Feng-Biao
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-30
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract 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.
American Psychological Association (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
Modern Language Association (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
American Medical Association (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
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1098840