DL-m6A: Identification of N6-Methyladenosine Sites in Mammals Using Deep Learning Based on Different Encoding Schemes

NaII-Pred: An ensemble-learning framework for the identification and interpretation of sodium ion inhibitors by fusing multiple feature representation

High-affinity ligand peptides for ion channels are essential for controlling the flow of ions across the plasma membrane. These peptides are now being investigated as possible therapeutic possibilities for a variety of illnesses, including cancer and cardiovascular disease. So, the identification and interpretation of ligand peptide inhibitors to control ion flow across cells become pivotal for exploration. In this work, we developed an ensemble-based model, NaII-Pred, for the identification of sodium ion inhibitors. The ensemble model was trained, tested, and evaluated on three different datasets. The NaII-Pred method employs six different descriptors and a hybrid feature set in conjunction with five conventional machine learning classifiers to create 35 baseline models. Through an ensemble approach, the top five baseline models trained on the hybrid feature set were integrated to yield the final predictive model, NaII-Pred. Our proposed model, NaII-Pred, outperforms the baseline models and the current predictors on both datasets. We believe NaII-Pred will play a critical role in screening and identifying potential sodium ion inhibitors and will be an invaluable tool.

m6Aexpress-enet: Predicting the regulatory expression m6A sites by an enet-regularization negative binomial regression model

As the most abundant mRNA modification, m6A controls and influences many aspects of mRNA metabolism including the mRNA stability and degradation. However, the role of specific m6A sites in regulating gene expression still remains unclear. In additional, the multicollinearity problem caused by the correlation of methylation level of multiple m6A sites in each gene could influence the prediction performance. To address the above challenges, we propose an elastic-net regularized negative binomial regression model (called m6Aexpress-enet) to predict which m6A site could potentially regulate its gene expression. Comprehensive evaluations on simulated datasets demonstrate that m6Aexpress-enet could achieve the top prediction performance. Applying m6Aexpress-enet on real MeRIP-seq data from human lymphoblastoid cell lines, we have uncovered the complex regulatory pattern of predicted m6A sites and their unique enrichment pathway of the constructed co-methylation modules. m6Aexpress-enet proves itself as a powerful tool to enable biologists to discover the mechanism of m6A regulatory gene expression. Furthermore, the source code and the step-by-step implementation of m6Aexpress-enet is freely accessed at https://github.com/tengzhangs/m6Aexpress-enet.

BiPSTP: Sequence feature encoding method for identifying different RNA modifications with bidirectional position-specific trinucleotides propensities

RNA modification, a posttranscriptional regulatory mechanism, significantly influences RNA biogenesis and function. The accurate identification of modification sites is paramount for investigating their biological implications. Methods for encoding RNA sequence into numerical data play a crucial role in developing robust models for predicting modification sites. However, existing techniques suffer from limitations, including inadequate information representation, challenges in effectively integrating positional and sequential information, and the generation of irrelevant or redundant features when combining multiple approaches. These deficiencies hinder the effectiveness of machine learning models in addressing the performance challenges associated with predicting RNA modification sites. Here, we introduce a novel RNA sequence feature representation method, named BiPSTP, which utilizes bidirectional trinucleotide position-specific propensities. We employ the parameter ξ to denote the interval between the current nucleotide and its adjacent forward or backward dinucleotide, enabling the extraction of positional and sequential information from RNA sequences. Leveraging the BiPSTP method, we have developed the prediction model mRNAPred using support vector machine classifier to identify multiple types of RNA modification sites. We evaluate the performance of our BiPSTP method and mRNAPred model across 12 distinct RNA modification types. Our experimental results demonstrate the superiority of the mRNAPred model compared to state-of-art models in the domain of RNA modification sites identification. Importantly, our BiPSTP method enhances the robustness and generalization performance of prediction models. Notably, it can be applied to feature extraction from DNA sequences to predict other biological modification sites.

MRM-BERT: a novel deep neural network predictor of multiple RNA modifications by fusing BERT representation and sequence features

RNA modifications play crucial roles in various biological processes and diseases. Accurate prediction of RNA modification sites is essential for understanding their functions. In this study, we propose a hybrid approach that fuses a pre-trained sequence representation with various sequence features to predict multiple types of RNA modifications in one combined prediction framework. We developed MRM-BERT, a deep learning method that combined the pre-trained DNABERT deep sequence representation module and the convolutional neural network (CNN) exploiting four traditional sequence feature encodings to improve the prediction performance. MRM-BERT was evaluated on multiple datasets of 12 commonly occurring RNA modifications, including m6A, m5C, m1A and so on. The results demonstrate that our hybrid model outperforms other models in terms of area under receiver operating characteristic curve (AUC) for all 12 types of RNA modifications. MRM-BERT is available as an online tool (http://117.122.208.21:8501) or source code (https://github.com/abhhba999/MRM-BERT), which allows users to predict RNA modification sites and visualize the results. Overall, our study provides an effective and efficient approach to predict multiple RNA modifications, contributing to the understanding of RNA biology and the development of therapeutic strategies.

Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques

Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen-Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model's decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies.

Enhancing Taxonomic Categorization of DNA Sequences with Deep Learning: A Multi-Label Approach

The application of deep learning for taxonomic categorization of DNA sequences is investigated in this study. Two deep learning architectures, namely the Stacked Convolutional Autoencoder (SCAE) with Multilabel Extreme Learning Machine (MLELM) and the Variational Convolutional Autoencoder (VCAE) with MLELM, have been proposed. These designs provide precise feature maps for individual and inter-label interactions within DNA sequences, capturing their spatial and temporal properties. The collected features are subsequently fed into MLELM networks, which yield soft classification scores and hard labels. The proposed algorithms underwent thorough training and testing on unsupervised data, whereby one or more labels were concurrently taken into account. The introduction of the clade label resulted in improved accuracy for both models compared to the class or genus labels, probably owing to the occurrence of large clusters of similar nucleotides inside a DNA strand. In all circumstances, the VCAE-MLELM model consistently outperformed the SCAE-MLELM model. The best accuracy attained by the VCAE-MLELM model when the clade and family labels were combined was 94%. However, accuracy ratings for single-label categorization using either approach were less than 65%. The approach's effectiveness is based on MLELM networks, which record connected patterns across classes for accurate label categorization. This study advances deep learning in biological taxonomy by emphasizing the significance of combining numerous labels for increased classification accuracy.

EMDL_m6Am: identifying N6,2'-O-dimethyladenosine sites based on stacking ensemble deep learning

BACKGROUND

N6, 2'-O-dimethyladenosine (m6Am) is an abundant RNA methylation modification on vertebrate mRNAs and is present in the transcription initiation region of mRNAs. It has recently been experimentally shown to be associated with several human disorders, including obesity genes, and stomach cancer, among others. As a result, N6,2'-O-dimethyladenosine (m6Am) site will play a crucial part in the regulation of RNA if it can be correctly identified.

RESULTS

This study proposes a novel deep learning-based m6Am prediction model, EMDL_m6Am, which employs one-hot encoding to expressthe feature map of the RNA sequence and recognizes m6Am sites by integrating different CNN models via stacking. Including DenseNet, Inflated Convolutional Network (DCNN) and Deep Multiscale Residual Network (MSRN), the sensitivity (Sn), specificity (Sp), accuracy (ACC), Mathews correlation coefficient (MCC) and area under the curve (AUC) of our model on the training data set reach 86.62%, 88.94%, 87.78%, 0.7590 and 0.8778, respectively, and the prediction results on the independent test set are as high as 82.25%, 79.72%, 80.98%, 0.6199, and 0.8211.

CONCLUSIONS

In conclusion, the experimental results demonstrated that EMDL_m6Am greatly improved the predictive performance of the m6Am sites and could provide a valuable reference for the next part of the study. The source code and experimental data are available at: https://github.com/13133989982/EMDL-m6Am .

iCpG-Pos: an accurate computational approach for identification of CpG sites using positional features on single-cell whole genome sequence data

MOTIVATION

The investigation of DNA methylation can shed light on the processes underlying human well-being and help determine overall human health. However, insufficient coverage makes it challenging to implement single-stranded DNA methylation sequencing technologies, highlighting the need for an efficient prediction model. Models are required to create an understanding of the underlying biological systems and to project single-cell (methylated) data accurately.

RESULTS

In this study, we developed positional features for predicting CpG sites. Positional characteristics of the sequence are derived using data from CpG regions and the separation between nearby CpG sites. Multiple optimized classifiers and different ensemble learning approaches are evaluated. The OPTUNA framework is used to optimize the algorithms. The CatBoost algorithm followed by the stacking algorithm outperformed existing DNA methylation identifiers.

AVAILABILITY AND IMPLEMENTATION

The data and methodologies used in this study are openly accessible to the research community. Researchers can access the positional features and algorithms used for predicting CpG site methylation patterns. To achieve superior performance, we employed the CatBoost algorithm followed by the stacking algorithm, which outperformed existing DNA methylation identifiers. The proposed iCpG-Pos approach utilizes only positional features, resulting in a substantial reduction in computational complexity compared to other known approaches for detecting CpG site methylation patterns. In conclusion, our study introduces a novel approach, iCpG-Pos, for predicting CpG site methylation patterns. By focusing on positional features, our model offers both accuracy and efficiency, making it a promising tool for advancing DNA methylation research and its applications in human health and well-being.

Attention-Based Graph Neural Network for Molecular Solubility Prediction

Drug discovery (DD) research is aimed at the discovery of new medications. Solubility is an important physicochemical property in drug development. Active pharmaceutical ingredients (APIs) are essential substances for high drug efficacy. During DD research, aqueous solubility (AS) is a key physicochemical attribute required for API characterization. High-precision in silico solubility prediction reduces the experimental cost and time of drug development. Several artificial tools have been employed for solubility prediction using machine learning and deep learning techniques. This study aims to create different deep learning models that can predict the solubility of a wide range of molecules using the largest currently available solubility data set. Simplified molecular-input line-entry system (SMILES) strings were used as molecular representation, models developed using simple graph convolution, graph isomorphism network, graph attention network, and AttentiveFP network. Based on the performance of the models, the AttentiveFP-based network model was finally selected. The model was trained and tested on 9943 compounds. The model outperformed on 62 anticancer compounds with metric Pearson correlation R ² and root-mean-square error values of 0.52 and 0.61, respectively. AS can be improved by graph algorithm improvement or more molecular properties addition.

Dynamic regulation and key roles of ribonucleic acid methylation

Ribonucleic acid (RNA) methylation is the most abundant modification in biological systems, accounting for 60% of all RNA modifications, and affects multiple aspects of RNA (including mRNAs, tRNAs, rRNAs, microRNAs, and long non-coding RNAs). Dysregulation of RNA methylation causes many developmental diseases through various mechanisms mediated by N ⁶-methyladenosine (m⁶A), 5-methylcytosine (m⁵C), N ¹-methyladenosine (m¹A), 5-hydroxymethylcytosine (hm⁵C), and pseudouridine (Ψ). The emerging tools of RNA methylation can be used as diagnostic, preventive, and therapeutic markers. Here, we review the accumulated discoveries to date regarding the biological function and dynamic regulation of RNA methylation/modification, as well as the most popularly used techniques applied for profiling RNA epitranscriptome, to provide new ideas for growth and development.

XML-CIMT: Explainable Machine Learning (XML) Model for Predicting Chemical-Induced Mitochondrial Toxicity

Organ toxicity caused by chemicals is a serious problem in the creation and usage of chemicals such as medications, insecticides, chemical products, and cosmetics. In recent decades, the initiation and development of chemical-induced organ damage have been related to mitochondrial dysfunction, among several adverse effects. Recently, many drugs, for example, troglitazone, have been removed from the marketplace because of significant mitochondrial toxicity. As a result, it is an urgent requirement to develop in silico models that can reliably anticipate chemical-induced mitochondrial toxicity. In this paper, we have proposed an explainable machine-learning model to classify mitochondrially toxic and non-toxic compounds. After several experiments, the Mordred feature descriptor was shortlisted to be used after feature selection. The selected features used with the CatBoost learning algorithm achieved a prediction accuracy of 85% in 10-fold cross-validation and 87.1% in independent testing. The proposed model has illustrated improved prediction accuracy when compared with the existing state-of-the-art method available in the literature. The proposed tree-based ensemble model, along with the global model explanation, will aid pharmaceutical chemists in better understanding the prediction of mitochondrial toxicity.