Sequence based model using deep neural network and hybrid features for identification of 5-hydroxymethylcytosine modification.

RNA modifications are pivotal in the development of newly synthesized structures, showcasing a vast array of alterations across various RNA classes. Among these, 5-hydroxymethylcytosine (5HMC) stands out, playing a crucial role in gene regulation and epigenetic changes, yet its detection through conventional methods proves cumbersome and costly. To address this, we propose Deep5HMC, a robust learning model leveraging machine learning algorithms and discriminative feature extraction techniques for accurate 5HMC sample identification. Our approach integrates seven feature extraction methods and various machine learning algorithms, including Random Forest, Naive Bayes, Decision Tree, and Support Vector Machine. Through K-fold cross-validation, our model achieved a notable 84.07% accuracy rate, surpassing previous models by 7.59%, signifying its potential in early cancer and cardiovascular disease diagnosis. This study underscores the promise of Deep5HMC in offering insights for improved medical assessment and treatment protocols, marking a significant advancement in RNA modification analysis.

Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features.

Sumoylation is a post-translation modification (PTM) mechanism that involves many critical biological processes, such as gene expression, localizing and stabilizing proteins, and replicating the genome. Moreover, sumoylation sites are associated with different diseases, including Parkinson's and Alzheimer's. Due to its vital role in the biological process, identifying sumoylation sites in proteins is significant for monitoring protein functions and discovering multiple diseases. Therefore, in the literature, several computational models utilizing conventional ML methods have been introduced to classify sumoylation sites. However, these models cannot accurately classify the sumoylation sites due to intrinsic limitations associated with the conventional learning methods. This paper proposes a robust computational model (called Deep-Sumo) for predicting sumoylation sites based on a deep-learning algorithm with efficient feature representation methods. The proposed model employs a half-sphere exposure method to represent protein sequences in a feature vector. Principal Component Analysis is applied to extract discriminative features by eliminating noisy and redundant features. The discriminant features are given to a multilayer Deep Neural Network (DNN) model to predict sumoylation sites accurately. The performance of the proposed model is extensively evaluated using a 10-fold cross-validation test by considering various statistical-based performance measurement metrics. Initially, the proposed DNN is compared with the traditional learning algorithm, and subsequently, the performance of the Deep-Sumo is compared with the existing models. The validation results show that the proposed model reports an average accuracy of 96.47%, with improvement compared with the existing models. It is anticipated that the proposed model can be used as an effective tool for drug discovery and the diagnosis of multiple diseases.

Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach.

Meiotic recombination is the driving force of evolutionary development and an important source of genetic variation. The meiotic recombination does not take place randomly in a chromosome but occurs in some regions of the chromosome. A region in chromosomes with higher rate of meiotic recombination events are considered as hotspots and a region where frequencies of the recombination events are lower are called coldspots. Prediction of meiotic recombination spots provides useful information about the basic functionality of inheritance and genome diversity. This study proposes an intelligent computational predictor called iRSpots-DNN for the identification of recombination spots. The proposed predictor is based on a novel feature extraction method and an optimized deep neural network (DNN). The DNN was employed as a classification engine whereas, the novel features extraction method was developed to extract meaningful features for the identification of hotspots and coldspots across the yeast genome. Unlike previous algorithms, the proposed feature extraction avoids bias among different selected features and preserved the sequence discriminant properties along with the sequence-structure information simultaneously. This study also considered other effective classifiers named support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF) to predict recombination spots. Experimental results on a benchmark dataset with 10-fold cross-validation showed that iRSpots-DNN achieved the highest accuracy, i.e., 95.81%. Additionally, the performance of the proposed iRSpots-DNN is significantly better than the existing predictors on a benchmark dataset. The relevant benchmark dataset and source code are freely available at: https://github.com/Fatima-Khan12/iRspot_DNN/tree/master/iRspot_DNN.

Energy harvesting based routing protocol for underwater sensor networks.

Underwater sensor networks (UWSNs) are ad-hoc networks which are deployed at rivers, seas and oceans to explore and monitor the phenomena such as pollution control, seismic activities and petroleum mining etc. The sensor nodes of UWSNs have limited charging capabilities. UWSNs networks are generally operated under two deployment mechanisms i.e localization and non-localization based. However, in both the mechanisms, balanced energy utilization is a challenging issue. Inefficient usage of energy significantly affects stability period, packet delivery ratio, end-to-end delay, path loss and throughput of a network. To efficiently utilize and harvest energy, this paper present a novel scheme called EH-ARCUN (Energy Harvesting Analytical approach towards Reliability with Cooperation for UWSNs) based on cooperation with energy harvesting. The scheme employs Amplify-and-Forward (AF) technique at relay nodes for data forwarding and Fixed Combining Ratio (FCR) technique at destination node to select accurate signal. The proposed technique selects relay nodes among its neighbor nodes based on harvested energy level. Most cooperation-based UWSN routing techniques do not exhibit energy harvesting mechanism at the relay nodes. EH-ARCUN deploys piezoelectric energy harvesting at relay nodes to improve the working capabilities of sensors in UWSNs. The proposed scheme is an extension of our previously implemented routing scheme called ARCUN for UWSNs. Performance of the proposed scheme is compared with ARCUN and RACE (Reliability and Adaptive Cooperation for efficient Underwater sensor Networks) schemes in term of stability period, packet delivery ratio, network throughput and path loss. Extensive simulation results show that EH-ARCUN performs better than both previous schemes in terms of the considered parameters.

Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC.

This study examines accurate and efficient computational method for identification of 5-methylcytosine sites in RNA modification. The occurrence of 5-methylcytosine (m5C) plays a vital role in a number of biological processes. For better comprehension of the biological functions and mechanism it is necessary to recognize m5C sites in RNA precisely. The laboratory techniques and procedures are available to identify m5C sites in RNA, but these procedures require a lot of time and resources. This study develops a new computational method for extracting the features of RNA sequence. In this method, first the RNA sequence is encoded via composite feature vector, then, for the selection of discriminate features, the minimum-redundancy-maximum-relevance algorithm was used. Secondly, the classification method used has been based on a support vector machine by using jackknife cross validation test. The suggested method efficiently identifies m5C sites from non- m5C sites and the outcome of the suggested algorithm is 93.33% with sensitivity of 90.0 and specificity of 96.66 on bench mark datasets. The result exhibits that proposed algorithm shown significant identification performance compared to the existing computational techniques. This study extends the knowledge about the occurrence sites of RNA modification which paves the way for better comprehension of the biological uses and mechanism.