Abstract
MicroRNAs (miRNAs), are short RNA sequences involved in targeting post transcriptional gene regulation. These mature miRNAs are derived from longer sequence precursors (pre-miRNAs) (70nt-100nt in mammalian) and have been shown to integrate multiple genes into biologically networks. Previously, we have shown that pre-miRNAs can be categorized into their species of origin using sequence-based features (such as frequency of k-mer) and machine learning.
In this study, we introduce a new set of features which are extracted from the precursor sequence that based Hamming distance between k-mer and pre-miRNAs sequence. These new set of features reveal an interesting result where in some cases it outperforms the k-mer frequency.
In the Hamming distance, we consider k-mers words with k = 4 and k = 5 while in k-mer frequency we consider k = 1, 2, 3. Hamming distance allows mismatches (flexible match) while k-mer frequency require the appearance of the whole word with length k. The Hamming flexibility allows getting more accurate representation to some clades and results in improving the performance.
This study suggests that there is no one universal feature set that applicable to all microRNA clades, so one needs to examine a different set of features and apply a function that associates the best set of feature to each clade.
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The work was supported by Zefat Academic College to MY.
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Yousef, M. (2019). Hamming Distance and K-mer Features for Classification of Pre-cursor microRNAs from Different Species. In: Benavente-Peces, C., Slama, S., Zafar, B. (eds) Proceedings of the 1st International Conference on Smart Innovation, Ergonomics and Applied Human Factors (SEAHF). SEAHF 2019. Smart Innovation, Systems and Technologies, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-030-22964-1_19
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DOI: https://doi.org/10.1007/978-3-030-22964-1_19
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