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Learning to predict single-wall carbon nanotube-recognition DNA sequences

npj Computational Materials 2019年第1期5卷 1085-1091页

作者： Yoona Yang Ming Zheng anand jagota Department of Chemical&Biomolecular Engineering Lehigh UniversityBethlehemPA 18015USA Materials Science and Engineering Division National Institute of Standards and TechnologyGaithersburgMD 20899USA Department of Bioengineering Lehigh UniversityBethlehemPA 18015USA

DNA/single-wall carbon nanotube(SWCNT)hybrids have enabled many applications because of their special ability to disperse and sort SWCNTs by their chirality and *** work has been done to discover sequences which recognize specific chiralities of SWCNT,and significant progress has been made in understanding the underlying structure and thermodynamics of these ***,de novo prediction of recognition sequences remains essentially impossible and the success rate for their discovery by search of the vast single-stranded DNA library is very ***,we report an effective way of predicting recognition sequences based on machine learning analysis of existing experimental sequence data *** input feature construction methods(position-specific,term-frequency,combined or segmented term frequency vector,and motif-based feature)were used and *** transformed features were used to train several classifier algorithms(logistic regression,support vector machine,and artificial neural network).Trained models were used to predict new sets of recognition sequences,and consensus among a number of models was used successfully to counteract the limited size of the data *** were tested using aqueous two-phase *** data thus acquired were used to retrain the models by adding an experimentally tested new set of predicted sequences to the original *** frequency of finding correct recognition sequences by the trained model increased to>50% from the~10% success rate in the original training data set.

关键词： carbon recognition thermodynamics

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