ORI-Explorer: a unified cell-specific tool for origin of replication sites prediction by feature fusion.

MOTIVATION: The origins of replication sites (ORIs) are precise regions inside the DNA sequence where the replication process begins. These locations are critical for preserving the genome's integrity during cell division and guaranteeing the faithful transfer of genetic data from generation to generation. The advent of experimental techniques has aided in the discovery of ORIs in many species. Experimentation, on the other hand, is often more time-consuming and pricey than computational approaches, and it necessitates specific equipment and knowledge. Recently, ORI sites have been predicted using computational techniques like motif-based searches and artificial intelligence algorithms based on sequence characteristics and chromatin states. RESULTS: In this article, we developed ORI-Explorer, a unique artificial intelligence-based technique that combines multiple feature engineering techniques to train CatBoost Classifier for recognizing ORIs from four distinct eukaryotic species. ORI-Explorer was created by utilizing a unique combination of three traditional feature-encoding techniques and a feature set obtained from a deep-learning neural network model. The ORI-Explorer has significantly outperformed current predictors on the testing dataset. Furthermore, by employing the sophisticated SHapley Additive exPlanation method, we give crucial insights that aid in comprehending model success, highlighting the most relevant features vital for forecasting cell-specific ORIs. ORI-Explorer is also intended to aid community-wide attempts in discovering potential ORIs and developing innovative verifiable biological hypotheses. AVAILABILITY AND IMPLEMENTATION: The used datasets along with the source code are made available through https://github.com/Z-Abbas/ORI-Explorer and https://zenodo.org/record/8358679.

Improving Enhancer Identification with a Multi-Classifier Stacked Ensemble Model.

Enhancers are DNA regions that are responsible for controlling the expression of genes. Enhancers are usually found upstream or downstream of a gene, or even inside a gene's intron region, but are normally located at a distant location from the genes they control. By integrating experimental and computational approaches, it is possible to uncover enhancers within DNA sequences, which possess regulatory properties. Experimental techniques such as ChIP-seq and ATAC-seq can identify genomic regions that are associated with transcription factors or accessible to regulatory proteins. On the other hand, computational techniques can predict enhancers based on sequence features and epigenetic modifications. In our study, we have developed a multi-classifier stacked ensemble (MCSE-enhancer) model that can accurately identify enhancers. We utilized feature descriptors from various physiochemical properties as input for our six baseline classifiers and built a stacked classifier, which outperformed previous enhancer classification techniques in terms of accuracy, specificity, sensitivity, and Mathew's correlation coefficient. Our model achieved an accuracy of 81.5%, representing a 2-3% improvement over existing models.

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.

NISQE: Non-Intrusive Speech Quality Evaluator Based on Natural Statistics of Mean Subtracted Contrast Normalized Coefficients of Spectrogram.

With the evolution in technology, communication based on the voice has gained importance in applications such as online conferencing, online meetings, voice-over internet protocol (VoIP), etc. Limiting factors such as environmental noise, encoding and decoding of the speech signal, and limitations of technology may degrade the quality of the speech signal. Therefore, there is a requirement for continuous quality assessment of the speech signal. Speech quality assessment (SQA) enables the system to automatically tune network parameters to improve speech quality. Furthermore, there are many speech transmitters and receivers that are used for voice processing including mobile devices and high-performance computers that can benefit from SQA. SQA plays a significant role in the evaluation of speech-processing systems. Non-intrusive speech quality assessment (NI-SQA) is a challenging task due to the unavailability of pristine speech signals in real-world scenarios. The success of NI-SQA techniques highly relies on the features used to assess speech quality. Various NI-SQA methods are available that extract features from speech signals in different domains, but they do not take into account the natural structure of the speech signals for assessment of speech quality. This work proposes a method for NI-SQA based on the natural structure of the speech signals that are approximated using the natural spectrogram statistical (NSS) properties derived from the speech signal spectrogram. The pristine version of the speech signal follows a structured natural pattern that is disrupted when distortion is introduced in the speech signal. The deviation of NSS properties between the pristine and distorted speech signals is utilized to predict speech quality. The proposed methodology shows better performance in comparison to state-of-the-art NI-SQA methods on the Centre for Speech Technology Voice Cloning Toolkit corpus (VCTK-Corpus) with a Spearman's rank-ordered correlation constant (SRC) of 0.902, Pearson correlation constant (PCC) of 0.960, and root mean squared error (RMSE) of 0.206. Conversely, on the NOIZEUS-960 database, the proposed methodology shows an SRC of 0.958, PCC of 0.960, and RMSE of 0.114.

Biogenic adsorbents for removal of drugs and dyes: A comprehensive review on properties, modification and applications.

This comprehensive review explores the potential and versatility of biogenic materials as sustainable and environmentally benign alternatives to conventional adsorbents for the removal of drugs and dyes. Biogenic adsorbents derived from plants, animals, microorganisms, algae and biopolymers have bioactive compounds that interact with functional groups of pollutants, resulting in their binding with the sorbent. These materials can be modified mechanically, thermally and chemically to enhance their adsorption properties. Biogenic hybrid composites, which integrate the characteristics of more than one material, have also been fabricated. Additionally, microorganisms and algae are analyzed for their ability to uptake pollutants. Various influential factors that contribute to the adsorption process are also discussed. The challenge, limitations and future prospects for research are reviewed and bridging gap between large scale application and laboratory scale. This comprehensive review, involves a combination of various biogenic adsorbents, going beyond the existing literature where typically only specific adsorbents are reported. The review also covers the isotherms, kinetics, and desorption studies of biogenic adsorbents, providing an improved framework for their effective use in removing pharmaceuticals and dyes from wastewater.

XGBoost framework with feature selection for the prediction of RNA N5-methylcytosine sites.

5-methylcytosine (m5C) is indeed a critical post-transcriptional alteration that is widely present in various kinds of RNAs and is crucial to the fundamental biological processes. By correctly identifying the m5C-methylation sites on RNA, clinicians can more clearly comprehend the precise function of these m5C-sites in different biological processes. Due to their effectiveness and affordability, computational methods have received greater attention over the last few years for the identification of methylation sites in various species. To precisely identify RNA m5C locations in five different species including Homo sapiens, Arabidopsis thaliana, Mus musculus, Drosophila melanogaster, and Danio rerio, we proposed a more effective and accurate model named m5C-pred. To create m5C-pred, five distinct feature encoding techniques were combined to extract features from the RNA sequence, and then we used SHapley Additive exPlanations to choose the best features among them, followed by XGBoost as a classifier. We applied the novel optimization method called Optuna to quickly and efficiently determine the best hyperparameters. Finally, the proposed model was evaluated using independent test datasets, and we compared the results with the previous methods. Our approach, m5C- pred, is anticipated to be useful for accurately identifying m5C sites, outperforming the currently available state-of-the-art techniques.

Software defined radio frequency sensing framework for intelligent monitoring of sleep apnea syndrome.

Healthy sleep is vital to all functions in the body. It improves physical and mental health, strengthens resistance against diseases, and develops strong immunity against metabolism and chronic diseases. However, a sleep disorder can cause the inability to sleep well. Sleep apnea syndrome is a critical breathing disorder that occurs during sleeping when breathing stops suddenly and starts when awake, causing sleep disturbance. If it is not treated timely, it can produce loud snoring and drowsiness or causes more acute health problems such as high blood pressure or heart attack. The accepted standard for diagnosing sleep apnea syndrome is full-night polysomnography. However, its limitations include a high cost and inconvenience. This article aims to develop an intelligent monitoring framework for detecting breathing events based on Software Defined Radio Frequency (SDRF) sensing and verify its feasibility for diagnosing sleep apnea syndrome. We extract the wireless channel state information (WCSI) for breathing motion using channel frequency response (CFR) recorded in time at every instant at the receiver. The proposed approach simplifies the receiver structure with the added functionality of communication and sensing together. Initially, simulations are conducted to test the feasibility of the SDRF sensing design for the simulated wireless channel. Then, a real-time experimental setup is developed in a lab environment to address the challenges of the wireless channel. We conducted 100 experiments to collect the dataset of 25 subjects for four breathing patterns. SDRF sensing system accurately detected breathing events during sleep without subject contact. The developed intelligent framework uses machine learning classifiers to classify sleep apnea syndrome and other breathing patterns with an acceptable accuracy of 95.9%. The developed framework aims to build a non-invasive sensing system to diagnose patients conveniently suffering from sleep apnea syndrome. Furthermore, this framework can easily be further extended for E-health applications.

SegR-Net: A deep learning framework with multi-scale feature fusion for robust retinal vessel segmentation.

Retinal vessel segmentation is an important task in medical image analysis and has a variety of applications in the diagnosis and treatment of retinal diseases. In this paper, we propose SegR-Net, a deep learning framework for robust retinal vessel segmentation. SegR-Net utilizes a combination of feature extraction and embedding, deep feature magnification, feature precision and interference, and dense multiscale feature fusion to generate accurate segmentation masks. The model consists of an encoder module that extracts high-level features from the input images and a decoder module that reconstructs the segmentation masks by combining features from the encoder module. The encoder module consists of a feature extraction and embedding block that enhances by dense multiscale feature fusion, followed by a deep feature magnification block that magnifies the retinal vessels. To further improve the quality of the extracted features, we use a group of two convolutional layers after each DFM block. In the decoder module, we utilize a feature precision and interference block and a dense multiscale feature fusion block (DMFF) to combine features from the encoder module and reconstruct the segmentation mask. We also incorporate data augmentation and pre-processing techniques to improve the generalization of the trained model. Experimental results on three fundus image publicly available datasets (CHASE_DB1, STARE, and DRIVE) demonstrate that SegR-Net outperforms state-of-the-art models in terms of accuracy, sensitivity, specificity, and F1 score. The proposed framework can provide more accurate and more efficient segmentation of retinal blood vessels in comparison to the state-of-the-art techniques, which is essential for clinical decision-making and diagnosis of various eye diseases.

Is a correlation-based investment strategy beneficial for long-term international portfolio investors?

Using negative to low-correlated assets to manage short-term portfolio risk is not uncommon among investors, although the long-term benefits of this strategy remain unclear. This study examines the long-term benefits of the correlation strategy for portfolios based on the stock market in Asia, Central and Eastern Europe, the Middle East and North Africa, and Latin America from 2000 to 2016. Our strategy is as follows. We develop five portfolios based on the average unconditional correlation between domestic and foreign assets from 2000 to 2016. This yields five regional portfolios based on low to high correlations. In the presence of selected economic and financial conditions, long-term diversification gains for each regional portfolio are evaluated using a panel cointegration-based testing method. Consistent across all portfolios and regions, our key cointegration results suggest that selecting a low-correlated portfolio to maximize diversification gains does not necessarily result in long-term diversification gains. Our empirical method, which also permits the estimation of cointegrating regressions, provides the opportunity to evaluate the impact of oil prices, U.S. stock market fluctuations, and investor sentiments on regional portfolios, as well as to hedge against these fluctuations. Finally, we extend our data to cover the years 2017-2022 and find that our main findings are robust. Supplementary Information: The online version contains supplementary material available at 10.1186/s40854-023-00471-9.

Non-intrusive RF sensing for early diagnosis of spinal curvature syndrome disorders.

The recent developments in communication and information ease people's lives to sit in one place and access any information from anywhere. However, the longevity of sitting and sitting in different postures raises the issues of spinal curvature. It necessitates a physical examination to identify the spinal illness in its early stages. This article aims to develop an intelligent monitoring framework for detecting and monitoring spinal curvature syndrome problems based on Software Defined Radio Frequency (SDRF) sensing and verify its feasibility for diagnosing actual patients. The proposed SDRF-based system identifies irregular spinal curvature syndrome and offers feedback signals when an incorrect posture is identified. We design the system using wireless university software-defined radio peripheral (USRP) kits to transmit and receive RF signals and record the wireless channel state information (WCSI) for kyphosis, Lordosis, and scoliosis spinal disorders. The statistical measures are extracted from the WCSI and apply machine learning algorithms to identify and classify the type of disorders. We record and test the system using 11 subjects with the spinal disorders kyphosis, Lordosis, and scoliosis. We acquire the WCSI, extract various statistical measures in terms of time and frequency domain features, and evaluate machine learning classifiers to identify and classify the spinal disorder. The performance comparison of the machine learning algorithms showed overall and each spinal curvature disorder recognition accuracy of more than 99%.

RAAGR2-Net: A brain tumor segmentation network using parallel processing of multiple spatial frames.

Brain tumors are one of the most fatal cancers. Magnetic Resonance Imaging (MRI) is a non-invasive method that provides multi-modal images containing important information regarding the tumor. Many contemporary techniques employ four modalities: T1-weighted (T1), T1-weighted with contrast (T1c), T2-weighted (T2), and fluid-attenuation-inversion-recovery (FLAIR), each of which provides unique and important characteristics for the location of each tumor. Although several modern procedures provide decent segmentation results on the multimodal brain tumor image segmentation benchmark (BraTS) dataset, they lack performance when evaluated simultaneously on all the regions of MRI images. Furthermore, there is still room for improvement due to parameter limitations and computational complexity. Therefore, in this work, a novel encoder-decoder-based architecture is proposed for the effective segmentation of brain tumor regions. Data pre-processing is performed by applying N4 bias field correction, z-score, and 0 to 1 resampling to facilitate model training. To minimize the loss of location information in different modules, a residual spatial pyramid pooling (RASPP) module is proposed. RASPP is a set of parallel layers using dilated convolution. In addition, an attention gate (AG) module is used to efficiently emphasize and restore the segmented output from extracted feature maps. The proposed modules attempt to acquire rich feature representations by combining knowledge from diverse feature maps and retaining their local information. The performance of the proposed deep network based on RASPP, AG, and recursive residual (R2) block termed RAAGR2-Net is evaluated on the BraTS benchmarks. The experimental results show that the suggested network outperforms existing networks that exhibit the usefulness of the proposed modules for "fine" segmentation. The code for this work is made available online at: https://github.com/Rehman1995/RAAGR2-Net.

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

N6-methyladenosine (m6A) is a common post-transcriptional alteration that plays a critical function in a variety of biological processes. Although experimental approaches for identifying m6A sites have been developed and deployed, they are currently expensive for transcriptome-wide m6A identification. Some computational strategies for identifying m6A sites have been presented as an effective complement to the experimental procedure. However, their performance still requires improvement. In this study, we have proposed a novel tool called DL-m6A for the identification of m6A sites in mammals using deep learning based on different encoding schemes. The proposed tool uses three encoding schemes which give the required contextual feature representation to the input RNA sequence. Later these contextual feature vectors individually go through several neural network layers for shallow feature extraction after which they are concatenated to a single feature vector. The concatenated feature map is then used by several other layers to extract the deep features so that the insight features of the sequence can be used for the prediction of m6A sites. The proposed tool is firstly evaluated on the tissue-specific dataset and later on a full transcript dataset. To ensure the generalizability of the tool we assessed the proposed model by training it on a full transcript dataset and test on the tissue-specific dataset. The achieved results by the proposed model have outperformed the existing tools. The results demonstrate that the proposed tool can be of great use for the biology experts and therefore a freely accessible web-server is created which can be accessed at: http://nsclbio.jbnu.ac.kr/tools/DL-m6A/.

Global energy markets connectedness: evidence from time-frequency domain.

We examine the presence of dependence across 51 energy markets classified into different regions from Jan 2007 to June 2021. In order to examine the presence of dependence across different energy markets, we apply standard and threshold dependence measures proposed by Diebold and Yilmaz, Int J Forecast 28:57-66, (2012) and Baruník and Krehlík, J Financ Econ 16(2):271-296, (2018). We highlight the presence of strong dependence between the energy markets at both regional level and across other regions. European and American energy markets are highly connected within the region over the long-run whereas Asia-Pacific and the African energy markets offer optimal diversification opportunities. Both short- and long-run dependence exists between Chinese and the Hong Kong energy markets and between the US and Canadian energy markets. We also witness substantial increase dependence across all the energy markets during different crisis periods.

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.

Impacts of COVID-19 outbreak, macroeconomic and financial stress factors on price spillovers among green bond.

We examine the impacts of the COVID-19 pandemic and global risk factors on the upside and downside price spillovers of MSCI global, building, financial, industrial, and utility green bonds (GBs). Using copulas, CoVaR, and quantile regression approaches, we show symmetric tail dependence between MSCI global GB and both building and utility GBs. Moreover, the upper tail dependence between MSCI global GB and financial GB intensified during COVID-19. We find asymmetric risk spillovers from MSCI global GB to the remaining GBs. Finally, the COVID-19 spread, the Citi macro risk index, and the financial condition index contribute positively to the quantiles' risk spillovers. The spillover index method shows significant dynamic volatility spillovers from global GB to GB sectors that intensify during the pandemic outbreak, except for financial GB. The causality-in-mean and in-variance from COVID-19, Citi macro risk index, and US financial condition index to the downside and upside spillover effects are sensitive to quantiles.

i6mA-Caps: a CapsuleNet-based framework for identifying DNA N6-methyladenine sites.

MOTIVATION: DNA N6-methyladenine (6mA) has been demonstrated to have an essential function in epigenetic modification in eukaryotic species in recent research. 6mA has been linked to various biological processes. It's critical to create a new algorithm that can rapidly and reliably detect 6mA sites in genomes to investigate their biological roles. The identification of 6mA marks in the genome is the first and most important step in understanding the underlying molecular processes, as well as their regulatory functions. RESULTS: In this article, we proposed a novel computational tool called i6mA-Caps which CapsuleNet based a framework for identifying the DNA N6-methyladenine sites. The proposed framework uses a single encoding scheme for numerical representation of the DNA sequence. The numerical data is then used by the set of convolution layers to extract low-level features. These features are then used by the capsule network to extract intermediate-level and later high-level features to classify the 6mA sites. The proposed network is evaluated on three datasets belonging to three genomes which are Rosaceae, Rice and Arabidopsis thaliana. Proposed method has attained an accuracy of 96.71%, 94% and 86.83% for independent Rosaceae dataset, Rice dataset and A.thaliana dataset respectively. The proposed framework has exhibited improved results when compared with the existing top-of-the-line methods. AVAILABILITY AND IMPLEMENTATION: A user-friendly web-server is made available for the biological experts which can be accessed at: http://nsclbio.jbnu.ac.kr/tools/i6mA-Caps/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DCNN-4mC: Densely connected neural network based N4-methylcytosine site prediction in multiple species.

DNA N4-methylcytosine (4mC) being a significant genetic modification holds a dominant role in controlling different biological functions, i.e., DNA replication, DNA repair, gene regulations and gene expression levels. The identification of 4mC sites is important to get insight information regarding different organics mechanisms. However, getting modification prediction from experimental methods is a challenging task due to high expenses and time-consuming techniques. Therefore, computational tools can be a great option for modification identification. Various computational tools are proposed in literature but their generalization and prediction performance require improvement. For this motive, we have proposed a neural network based tool named DCNN-4mC for identifying 4mC sites. The proposed model involves a set of neural network layers with a skip connection which allows to share the shallow features with dense layers. Skip connection have allowed to gather crucial information regarding 4mC sites. In literature, different models are employed on different species hence in many cases different datasets are available for a single species. In this research, we have combined all available datasets to create a single benchmark dataset for every species. To the best of our knowledge, no model in literature is employed on more than six different species. To ensure the generalizability of DCNN-4mC we have used 12 different species for performance evaluation. The DCNN-4mC tool has attained 2% to 14% higher accuracy than state-of-the-art tools on all available datasets of different species. Furthermore, independent test datasets are also engaged and DCNN-4mC have overall yielded high performance in them as well.

BrainSeg-Net: Brain Tumor MR Image Segmentation via Enhanced Encoder-Decoder Network.

Efficient segmentation of Magnetic Resonance (MR) brain tumor images is of the utmost value for the diagnosis of tumor region. In recent years, advancement in the field of neural networks has been used to refine the segmentation performance of brain tumor sub-regions. The brain tumor segmentation has proven to be a complicated task even for neural networks, due to the small-scale tumor regions. These small-scale tumor regions are unable to be identified, the reason being their tiny size and the huge difference between area occupancy by different tumor classes. In previous state-of-the-art neural network models, the biggest problem was that the location information along with spatial details gets lost in deeper layers. To address these problems, we have proposed an encoder-decoder based model named BrainSeg-Net. The Feature Enhancer (FE) block is incorporated into the BrainSeg-Net architecture which extracts the middle-level features from low-level features from the shallow layers and shares them with the dense layers. This feature aggregation helps to achieve better performance of tumor identification. To address the problem associated with imbalance class, we have used a custom-designed loss function. For evaluation of BrainSeg-Net architecture, three benchmark datasets are utilized: BraTS2017, BraTS 2018, and BraTS 2019. Segmentation of Enhancing Core (EC), Whole Tumor (WT), and Tumor Core (TC) is carried out. The proposed architecture have exhibited good improvement when compared with existing baseline and state-of-the-art techniques. The MR brain tumor segmentation by BrainSeg-Net uses enhanced location and spatial features, which performs better than the existing plethora of brain MR image segmentation approaches.

High frequency multiscale relationships among major cryptocurrencies: portfolio management implications.

This paper examines the high frequency multiscale relationships and nonlinear multiscale causality between Bitcoin, Ethereum, Monero, Dash, Ripple, and Litecoin. We apply nonlinear Granger causality and rolling window wavelet correlation (RWCC) to 15 min-data. Empirical RWCC results indicate mostly positive co-movements and long-term memory between the cryptocurrencies, especially between Bitcoin, Ethereum, and Monero. The nonlinear Granger causality tests reveal dual causation between most of the cryptocurrency pairs. We advance evidence to improve portfolio risk assessment, and hedging strategies.

DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool.

DNA N6-methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA replication, and DNA transcription. The 6mA modification sites hold a great impact when their biological function is under consideration. Research in biochemical experiments for this purpose is carried out and they have demonstrated good results. However, they proved not to be a practical solution when accessed under cost and time parameters. This led researchers to develop computational models to fulfill the requirement of modification identification. In consensus, we have developed a computational model recommended by Chou's 5-steps rule. The Neural Network (NN) model uses convolution layers to extract the high-level features from the encoded binary sequence. These extracted features were given an optimal interpretation by using a Long Short-Term Memory (LSTM) layer. The proposed architecture showed higher performance compared to state-of-the-art techniques. The proposed model is evaluated on Mus musculus, Rice, and "Combined-species" genomes with 5- and 10-fold cross-validation. Further, with access to a user-friendly web server, publicly available can be accessed freely.