RT Journal Article
SR Electronic
T1 Comparison of SVM and Spectral Embedding in Promoter Biobricks’ Categorizing and Clustering
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 553412
DO 10.1101/553412
A1 Shangjie Zou
YR 2019
UL http://biorxiv.org/content/early/2019/02/18/553412.abstract
AB Background In organisms’ genomes, promoters are short DNA sequences on the upstream of structural genes, with the function of controlling genes’ transcription. Promoters can be roughly divided into two classes: constitutive promoters and inducible promoters. Promoters with clear functional annotations are practical synthetic biology biobricks. Many statistical and machine learning methods have been introduced to predict the functions of candidate promoters. Spectral Eigenmap has been proved to be an effective clustering method to classify biobricks, while support vector machine (SVM) is a powerful machine learning algorithm, especially when dataset is small.Methods The two algorithms: spectral embedding and SVM are applied to the same dataset with 375 prokaryotic promoters. For spectral embedding, a Laplacian matrix is built with edit distance, followed by K-Means Clustering. The sequences are represented by numeric vector to serve as dataset for SVM trainning.Results SVM achieved a high predicting accuracy of 93.07% in 10-fold cross validation for classification of promoters’ transcriptional functions. Laplacian eigenmap (spectral embedding) based on editing distance may not be capable for extracting discriminative features for this task.Availability Codes, datasets and some important matrices are available on github https://github.com/shangjieZou/Promoter-transcriptional-predictor/tree/source-code