iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm

Abstract

One of the most common and well studied post-transcription modifications in RNAs is N6-methyladenosine (m6A) which has been involved with a wide range of biological processes. Over the past decades, N6-methyladenosine produced some positive consequences through the high-throughput laboratory techniques but still, these lab processes are time consuming and costly. Diverse computational methods have been proposed to identify m6A sites accurately. In this paper, we proposed a computational model named iMethyl-deep to identify m6A Saccharomyces Cerevisiae on two benchmark datasets M6A2614 and M6A6540 by using single nucleotide resolution to convert RNA sequence into a high quality feature representation. The iMethyl-deep obtained 89.19% and 87.44% of accuracy on M6A2614 and M6A6540 respectively which show that our proposed method outperforms the state-of-the-art predictors, at least 8.44%, 8.96%, 8.69% and 0.173 on M6A2614 and 15.47%, 28.52%, 25.54 and 0.5 on M6A6540 higher in terms of four metrics Sp, Sn, ACC and MCC respectively. Meanwhile, M6A6540 dataset never used to train a model.

DNA sequences performs as natural language processing by exploiting deep learning algorithm for the identification of N4-methylcytosine

“…Whereas, an assorted CNN model can be made by using handcrafted features. Convolution Neural Network has been utilized in several research areas such as image processing 39,40 , natural language processing 41 , and computational biology [42][43][44][45][46][47] . A grid search algorithm was implemented with different hyper-parameters values to obtain the most optimal CNN model during its learning.…”

DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning

“…Additionally, a handy crafted feature can also be fed to CNN to build a heterogeneous model. A CNN has a big impact on various fields of natural language processing, image processing [53][54][55][56] and computational biology [57,58]. To get an optimum model we applied grid search and during learning the CNN, six hyperparameters were tuned.…”

Prediction of RNA 5-Hydroxymethylcytosine Modifications Using Deep Learning

“…To overcome this constraint deep learning techniques could be effective alternative computational methods that are consequentially capable of learning the features by utilizing multiple levels of abstraction [13]- [15]. Computational models based on deep learning have proved to be very efficient and effective at image recognition [16], [17], information retrieval [18], natural language processing [19], speech recognition [20], [21], and computational biology [22]- [37]. Considering the effectiveness of deep learning methods in the field of computational biology; CNN implementation is the most popular implementation of deep learning.…”