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

Machine Learning Classifiers: A Brief Primer

Machine learning is a prominent and an intensively studied field in the artificial intelligence area which assists to enhance the performance of classification. In this paper, the main idea is to provide the classification and comparative analysis of data mining algorithms. To support this idea, six supervised machine learning (ML) algorithms, C4.5 (J48), K-Nearest Neighbor (KNN), Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), and One Rule (OneR) along with the five UCI Datasets of ML Repository, are being applied that demonstrates the robustness and effectiveness of numerous approaches. Whereas, for analytical procedures, significant parameters have been considered: Accuracy, Area Under Curve (AUC), precision, recall, and F-measure values. Hence, the primary objective of this study is to obtain binary classification and efficiency by conducting the performance evaluation. We present experimental results that demonstrate the effectiveness of our approach to well-known competitive approaches

Internet of Things as Intimating for Pregnant Women’s Healthcare: An Impending Privacy Issues

Ingeniously, the innovations are taking place in current medical era, where IoT is a key and its related technology plays a dynamic role in pregnant women care taking inside hospital and outside. IoT ensure the effective and efficient care of pregnant women in any environment because intelligent tiny devices like RF-Tags, sensors are attached with pregnant women, and all the activities of pregnant women can be monitored by professional medical staff from anywhere and anytime. The usage of these advanced technologies in pregnant women care environment, absolutely eradicates the pregnancy complications and harmful incidents, but also raises privacy, religious, ethical, legal and social issues. The purpose of this paper is to discuss the usage of IoT in pregnant women healthcare environments and articulates endorsements to promote research then can guarantee that the pregnant women’s privacy is preserved. DOI : http://dx.doi.org/10.11591/telkomnika.v12i6.422

A combined approach of base and meta learners for hybrid system

The ensemble learning method is considered a meaningful yet challenging task. To enhance the performance of binary classification and predictive analysis, this paper proposes an effective ensemble learning approach by applying multiple models to produce efficient and effective outcomes. In these experimental studies, three base learners, J48, Multilayer Perceptron (MP), and Support Vector Machine (SVM) are being utilized. Moreover, two meta-learners, Bagging and Rotation Forest are being used in this analysis. Firstly, to produce effective results and capture productive data, the base learner, the J48 decision tree is aggregated with the rotation forest. Secondly, machine learning and ensemble learning classification algorithms along with the five UCI Datasets are being applied to progress the robustness of the system. Whereas, the recommended mechanism is evaluated by implementing five performance standards concerning the accuracy, AUC (Area Under Curve), precision, recall and F-measure values. In this regard, extensive strategies and various approaches were being studied and applied to obtain improved results from the current literature; however, they were insufficient to provide successful results. We present experimental results which demonstrate the efficiency of our approach to well-known competitive approaches. This method can be applied to image identification and machine learning problems, such as binary classification

Molecular Genomic Study of Inhibin Molecule Production through Granulosa Cell Gene Expression in Inhibin-Deficient Mice

Inhibin is a molecule that belongs to peptide hormones and is excreted through pituitary gonadotropins stimulation action on the granulosa cells of the ovaries. However, the differential regulation of inhibin and follicle-stimulating hormone (FSH) on granulosa cell tumor growth in mice inhibin-deficient females is not yet well understood. The objective of this study was to evaluate the role of inhibin and FSH on the granulosa cells of ovarian follicles at the premature antral stage. This study stimulated immature wild-type (WT) and Inhibin-α knockout (Inha−/−) female mice with human chorionic gonadotropin (hCG) and examined hCG-induced gene expression changes in granulosa cells. Also, screening of differentially expressed genes (DEGs) was performed in the two groups under study. In addition, related modules to external traits and key gene drivers were determined through Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm. The results identified a number of 1074 and 931 DEGs and 343 overlapping DEGs (ODEGs) were shared in the two groups. Some 341 ODEGs had high relevance and consistent expression direction, with a significant correlation coefficient (r2 = 0.9145). Additionally, the gene co-expression network of selected 153 genes showed 122 nodes enriched to 21 GO biological processes (BP) and reproduction and 3 genes related to genomic pathways. By using principal component analysis (PCA), the 14 genes in the regulatory network were fixed and the cumulative proportion of fitted top three principal components was 94.64%. In conclusion, this study revealed the novelty of using ODEGs for investigating the inhibin and FSH hormone pathways that might open the way toward gene therapy for granulosa cell tumors. Also, these genes could be used as biomarkers for tracking the changes in inhibin and FSH hormone from the changes in the nutrition pattern