IDENTIFYING MOLECULAR FUNCTIONS OF DYNEIN MOTOR PROTEINS USING EXTREME GRADIENT BOOSTING ALGORITHM WITH MACHINE LEARNING

The majority of cytoplasmic proteins and vesicles move actively primarily to dynein motor proteins, which are the cause of muscle contraction. Moreover, identifying how dynein are used in cells will rely on structural knowledge. Cytoskeletal motor proteins have different molecular roles and structures, and they belong to three superfamilies of dynamin, actin and myosin. Loss of function of specific molecular motor proteins can be attributed to a number of human diseases, such as Charcot-Charcot-Dystrophy and kidney disease.  It is crucial to create a precise model to identify dynein motor proteins in order to aid scientists in understanding their molecular role and designing therapeutic targets based on their influence on human disease. Therefore, we develop an accurate and efficient computational methodology is highly desired, especially when using cutting-edge machine learning methods. In this article, we proposed a machine learning-based superfamily of cytoskeletal motor protein locations prediction method called extreme gradient boosting (XGBoost). We get the initial feature set All by extraction the protein features from the sequence and evolutionary data of the amino acid residues named BLOUSM62. Through our successful eXtreme gradient boosting (XGBoost), accuracy score 0.8676%, Precision score 0.8768%, Sensitivity score 0.760%, Specificity score 0.9752% and MCC score 0.7536%.  Our method has demonstrated substantial improvements in the performance of many of the evaluation parameters compared to other state-of-the-art methods. This study offers an effective model for the classification of dynein proteins and lays a foundation for further research to improve the efficiency of protein functional classification

UBI-XGB: IDENTIFICATION OF UBIQUITIN PROTEINS USING MACHINE LEARNING MODEL

A recent line of research has focused on Ubiquitination, a pervasive and proteasome-mediated protein degradation that controls apoptosis and is crucial in the breakdown of proteins and the development of cell disorders, is a major factor.  The turnover of proteins and ubiquitination are two related processes. We predict ubiquitination sites; these attributes are lastly fed into the extreme gradient boosting (XGBoost) classifier. We develop reliable predictors computational tool using experimental identification of protein ubiquitination sites is typically labor- and time-intensive. First, we encoded protein sequence features into matrix data using Dipeptide Deviation from Expected Mean (DDE) features encoding techniques. We also proposed 2nd features extraction model named dipeptide composition (DPC) model. It is vital to develop reliable predictors since experimental identification of protein ubiquitination sites is typically labor- and time-intensive. In this paper, we proposed computational method as named Ubipro-XGBoost, a multi-view feature-based technique for predicting ubiquitination sites. Recent developments in proteomic technology have sparked renewed interest in the identification of ubiquitination sites in a number of human disorders, which have been studied experimentally and clinically.  When more experimentally verified ubiquitination sites appear, we developed a predictive algorithm that can locate lysine ubiquitination sites in large-scale proteome data. This paper introduces Ubipro-XGBoost, a machine learning method. Ubipro-XGBoost had an AUC (area under the Receiver Operating Characteristic curve) of 0.914% accuracy, 0.836% Sensitivity, 0.992% Specificity, and 0.839% MCC on a 5-fold cross validation based on DPC model, and 2nd 0.909% accuracy, 0.839% Sensitivity, 0.979% Specificity, and 0. 0.829% MCC on a 5-fold cross validation based on DDE model. The findings demonstrate that the suggested technique, Ubipro-XGBoost, outperforms conventional ubiquitination prediction methods and offers fresh advice for ubiquitination site identification

A Survey on Security Issues and Attacks of Fog Computing

There is a link between the cloud and the Internet of Things (IoT). The layer that makes up the dispersed network environment is exactly what it is. Cloud computing is brought out to the edge of the network through the type of networking topology referred as fog computing. Users can benefit greatly from fog computing. Fog's primary role, similar to cloud computing, is to allow people mobility. Fog computing is becoming more and more popular, whereas at the same time, security dangers are growing every day. Users' identification & verification are crucial. The fact of fog computing cannot effectively utilize the security and privacy solutions provided by cloud computing must be emphasized. The risks, issues, and solutions linked to security in fog computing are outlined throughout this study. The poll then includes information on ongoing research projects as well as open security and safety concerns for fog computing

DeepImmuno-PSSM: Identification of Immunoglobulin based on Deep learning and PSSM-Profiles

Immunoglobulin has a close connection to a number of disorders and is important in both biological and medicinal contexts. Therefore, it is crucial for illness research to employ efficient techniques to increase the categorization accuracy of immunoglobulins. Computational models have been used in a small number of research to address this important issue, but the accuracy of the predictions is not good enough. As a result, we use a cutting-edge deep learning technique with convolutional neural networks to enhance the performance results. In this study, the immunoglobulin features were extracted using the dipeptide acid composition (DPC) with the position-specific scoring matrix (DPC-PSSM) and position-specific scoring matrix-transition probability composition (PSSM-TPC) methods. we apply extracted features information from the DPC-PSSM profiles and PSSM-TPC profile by using a 1D-convolutional neural network (CNN) over an input shape.  The outcomes demonstrated that the DeepImmuno-PSSM method based on sequential minimal optimization was able to properly predict DPC-PSSM accuracy score 93.44% obtained and of the immunoglobulins using the greatest feature subcategory produced by the PSSM-TPC feature mining approach accuracy score 89.92% obtained. Our findings indicate that we are able to provide a useful model for enhancing immunoglobulin proteins' capacity for prediction. Additionally, it implies that employing sequence data in deep learning and PSSM-based features may open up new path for biochemical modelling