Categorization of species based on their microRNAs employing sequence motifs, information-theoretic sequence feature extraction, and k-mers
subject: microRNA, Sequence motifs, pre-microRNA, machine learning, Differentiate miRNAs among species, k-mer
description: Abstract Background Diseases like cancer can manifest themselves through changes in protein abundance, and microRNAs (miRNAs) play a key role in the modulation of protein quantity. MicroRNAs are used throughout all kingdoms and have been shown to be exploited by viruses to modulate their host environment. Since the experimental detection of miRNAs is difficult, computational methods have been developed. Many such tools employ machine learning for pre-miRNA detection, and many features for miRNA parameterization have been proposed. To train machine learning models, negative data is of importance yet hard to come by; therefore, we recently started to employ pre-miRNAs from one species as positive data versus another species’ pre-miRNAs as negative examples based on sequence motifs and k-mers. Here, we introduce the additional usage of information-theoretic (IT) features. Results Pre-miRNAs from one species were used as positive and another species’ pre-miRNAs as negative training data for machine learning. The categorization capability of IT and k-mer features was investigated. Both feature sets and their combinations yielded a very high accuracy, which is as good as the previously suggested sequence motif and k-mer based method. However, for obtaining a high performance, a sufficiently large phylogenetic distance between the species and sufficiently high number of pre-miRNAs in the training set is required. To examine the contribution of the IT and k-mer features, an information gain-based feature ranking was performed. Although the top 3 are IT features, 80% of the top 100 features are k-mers. The comparison of all three individual approaches (motifs, IT, and k-mers) shows that the distinction of species based on their pre-miRNAs k-mers are sufficient. Conclusions IT sequence feature extraction enables the distinction among species and is less computationally expensive than motif calculations. However, since IT features need larger amounts of data to have enough sta…
creator: Malik Yousef, Dawit Nigatu, Dalit Levy, Jens Allmer, Werner Henkel
source: EURASIP Journal on Advances in Signal Processing
publisher: SpringerOpen
date: 2017
language: EN
identifier: DOI: 10.1186/s13634-017-0506-8
coverage: Electronics, Telecommunication