Probabilistic Neural Network Inferences on Oligonucleotide Classification Based on Oligo: Target Interaction
| Autor: | S. S. Vinodchandra, Abdul Rahiman Anusha |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Regulation of gene expression Oligonucleotide Computer science business.industry Feature vector Probabilistic logic RNA Pattern recognition DNA sequencing 03 medical and health sciences Probabilistic neural network 030104 developmental biology microRNA Artificial intelligence business |
| Zdroj: | Intelligent Information and Database Systems ISBN: 9783319544298 ACIIDS (2) |
| DOI: | 10.1007/978-3-319-54430-4_70 |
| Popis: | Oligonucleotides are small non-coding regulatory RNA or DNA sequences that bind to specific mRNA locations to impart gene regulation. Identification of oligonucleotides from other small non-coding RNA sequences such as miRNAs, piRNAs etc. is still challenging as oligos exhibit a notable overlap in sequence length and properties with these RNA categories. This work focuses on a probabilistic oligonucleotide classification method based on its distinct underlying feature vectors to identify oligos from other regulatory classes. We propose a computational approach developed using a probabilistic neural network (PNN) based on oligo: target binding characteristics. The performance measure showed promising results when compared with other existing computational methods. Role and contribution of extracted features was estimated using the receiver operating curves. Our study suggests the potentiality of probabilistic approaches over non-probabilistic techniques in oligonucleotide classification problems. |
| Databáze: | OpenAIRE |
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