BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species

Abstract

MicroRNAs (miRNAs) are a set of short (21–24 nt) noncoding RNAs that play significant regulatory roles in cells. In the past few years, research on miRNA-related problems has become a hot field of bioinformatics because of miRNAs' essential biological function. miRNA-related bioinformatics analysis is beneficial in several aspects, including the functions of miRNAs and other genes, the regulatory network between miRNAs and their target mRNAs, and even biological evolution. Distinguishing miRNA precursors from other hairpin-like sequences is important and is an essential procedure in detecting novel microRNAs. In this study, we employed backpropagation (BP) neural network together with 98-dimensional novel features for microRNA precursor identification. Results show that the precision and recall of our method are 95.53% and 96.67%, respectively. Results further demonstrate that the total prediction accuracy of our method is nearly 13.17% greater than the state-of-the-art microRNA precursor prediction software tools.

Precursor microRNA Identification Using Deep Convolutional Neural Networks

“…Secondary structure is a very beneficial feature for pre-miRNA identification [22,21,9]. In this paper, we represent predicted secondary structure using a pairing matrix format as given in Fig.…”

“…1AB for an example), with standard methods such as RNAfold [18], GTfold [32], and CyloFold [6]. Then they extract a lot of handcrafted features, some of which are Watson-Crick nucleotide pairing (A-U, C-G), loop length [43,42], sequence length [43], dinucleotide pair frequencies [43,20,42,4,34], trinucleotide pair frequencies (constituting 64 features) [43,20], melting temperature [43], mini-mum free energy [44,43,9]. These features are used as inputs to machine learning methods such as support vector machines (SVM) [42,4,46], random forests [34], neural networks [37,43,35,20] and hidden Markov models [1].…”

“…For example, Ref. [20] uses 98 of the aforementioned features for the input of their neural network. DP-miRNA [43] also uses neural network with 58 extracted characteristic features based on sequence composition and secondary structure predicted by RNAfold software, folding measures including various formulations of physical energy.…”

New 3D graphical representation for RNA structure analysis and its application in the pre-miRNA identification of plants

“…Numerous classication prediction algorithms, which yield different results, have been utilized to recognize pre-miRNA. ML-based algorithms include support vector machines (SVM), 1,8,[16][17][18][19][20][21][22][23][24][25][26] back-propagation and self-organizing map (SOM) neural networks, [27][28][29] and random forest (RF). [30][31][32] Difficulties in using ML-based methods are attributed to the selection of representative samples that adequately describe the sample space of an entire positive dataset (pre-miRNA) and negative dataset counterexamples (pseudo pre-miRNA).…”

An improved method for identification of small non-coding RNAs in bacteria using support vector machine

“…Machine learning techniques have been extensively used to identify different classes of small non-coding RNA molecules, namely microRNAs (miRNAs)232425262728293031323334 and transfer RNAs2324. However, SVM2526272829303132 rather than random forest (RF)3334 and neural network (NN)3536 were used more widely. Several excellent reviews are also available on this topic37383940.…”