Post-transcriptional modification such as N6-methyladenosine (m6A) has a crucial role in the stability and regulation of gene expression. Therefore, the identification of m6A is highly required for understanding the functional mechanisms of biological processes. Several machine learning techniques based on handy craft feature extraction methods have been proposed to facilitate the laborious work. However, due to the inefficient feature extraction, these techniques increase the computational complexity and thereby affect the identification accuracy of m6A.This paper proposes a fast and reliable predictive model for the identification of m6A sites. The proposed model is based on the convolutional neural network (CNN) which extracts the most significant features from the RNA sequences encoded by concatenating one-hot and nucleotide chemical properties. The proposed model is applied and tested on multiple species benchmark datasets and evaluated against the state-of-art predictive models. The results indicate that the proposed model achieves high accuracy of 93.6 %, 93.8 %, 85.0 % and 92.5 % on the datasets of Homo sapiens (H.sapien), Mus musculus (M.musculs), Saccharomyces cerevisiae (S.cerevisiae), and Arabidopsis thaliana (A.thaliana), respectively.The proposed model could be used to facilitate the researcher’s community in m6A identification. In addition, an easy to use web server is made available at https://home.jbnu.ac.kr/NSCL/pm6acnn.htm.

Piwi-interacting RNAs (piRNAs) play a pivotal role in maintaining genome integrity by repression of transposable elements, gene stability, and association with various disease progressions. Cost-efficient computational methods for the identification of piRNA disease associations promote the efficacy of disease-specific drug development. In this regard, we developed a simple, robust, and efficient deep learning method for identifying the piRNA disease associations known as piRDA. The proposed architecture extracts the most significant and abstract information from raw sequences represented in a simplicated piRNA disease pair without any involvement of features engineering. Two-step positive unlabeled learning and bootstrapping technique are utilized to abstain from the false-negative and biased predictions dealing with positive unlabeled data. The performance of proposed method piRDA is evaluated using k-fold cross-validation. The piRDA is significantly improved in all the performance evaluation measures for the identification of piRNA disease associations in comparison to state-of-the-art method. Moreover, it is thus projected conclusively that the proposed computational method could play a significant role as a supportive and practical tool for primitive disease mechanisms and pharmaceutical research such as in academia and drug design. Eventually, the proposed model can be accessed using publicly available and user-friendly web tool athttp://nsclbio.jbnu.ac.kr/tools/piRDA/.

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R²) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs.

Piwi-interacting RNAs (piRNAs) are a distinct sub-class of small non-coding RNAs that are mainly responsible for germline stem cell maintenance, gene stability, and maintaining genome integrity by repression of transposable elements. piRNAs are also expressed aberrantly and associated with various kinds of cancers. To identify piRNAs and their role in guiding target mRNA deadenylation, the currently available computational methods require urgent improvements in performance. To facilitate this, we propose a robust predictor based on a lightweight and simplified deep learning architecture using a convolutional neural network (CNN) to extract significant features from raw RNA sequences without the need for more customized features. The proposed model’s performance is comprehensively evaluated using $k$ -fold cross-validation on a benchmark dataset. The proposed model significantly outperforms existing computational methods in the prediction of piRNAs and their role in target mRNA deadenylation. In addition, a user-friendly and publicly-accessible web server is available at http://nsclbio.jbnu.ac.kr/tools/2S-piRCNN/ .

It is becoming increasingly clear that RNA 5-hydroxymethylcytosine (5hmC), which plays an important role in several biological processes, is one of the most important objects of study in the field of RNA epigenetics. Biochemical experiments using various sequencing-based technologies are capable of achieving high-throughput identification of 5hmC, but current methods are labor-intensive, costly, and time-consuming. There is an imperative need to develop more efficient and robust computational methods to replace, or at least complement, such high-throughput methods. Although one such machine learning-based model to achieve this has already been developed, its performance is limited. In this study, we developed iRhm5CNN, an efficient and reliable computational predictive model for the identification of RNA 5hmC sites. Our model is based on a convolution neural network (CNN) that extracts the most reliable feature from the RNA sequence inevitably. The results of our experiments show significant outperformance across all evaluation metrics of our proposed architecture when compared to the only existing state of the art computational model in all the evaluation metrics. The proposed model can be accessed for free at http://nsclbio.jbnu.ac.kr/tools/iRhm5CNN/.