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Network embedding framework for driver gene discovery by combining functional and structural information

BMC GENOMICS 2023年第1期24卷 426-426页

作者： Chu, Xin Guan, Boxin Dai, Lingyun Liu, Jin-xing Li, Feng Shang, Junliang Qufu Normal Univ Sch Comp Sci Rizhao 27826 Peoples R China

Comprehensive analysis of multiple data sets can identify potential driver genes for various cancers. In recent years, driver gene discovery based on massive mutation data and gene interaction networks has attracted increasing attention, but there is still a need to explore combining functional and structural information of genes in protein interaction networks to identify driver genes. Therefore, we propose a network embedding framework combining functional and structural information to identify driver genes. Firstly, we combine the mutation data and gene interaction networks to construct mutation integration network using network propagation algorithm. Secondly, the struc2vec model is used for extracting gene features from the mutation integration network, which contains both gene's functional and structural information. Finally, machine learning algorithms are utilized to identify the driver genes. Compared with the previous four excellent methods, our method can find gene pairs that are distant from each other through structural similarities and has better performance in identifying driver genes for 12 cancers in the cancer genome atlas. At the same time, we also conduct a comparative analysis of three gene interaction networks, three gene standard sets, and five machine learning algorithms. Our framework provides a new perspective for feature selection to identify novel driver genes.

关键词： Driver gene Gene interaction network Network embedding Mutation data classification algorithm

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Predicting Employee Turnover in IT Industries using Correlation and Chi-Square Visualization

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第12期13卷 71-75页

作者： Priambodo, Bagus Jumaryadi, Yuwan Rahayu, Sarwati Ani, Nur Ratnasari, Anita Salamah, Umniy Putra, Zico Pratama Otong, Muhamad Univ Mercu Buana Fac Comp Sci Jakarta Indonesia Univ Nusa Mandiri Fac Informat Technol Jakarta Indonesia Univ Sultan Ageng Tirtayasa Dept Elect Engn Serang Indonesia

Employee turnover in the IT industry is among the highest compared to other industries. Knowing factors that influence the turnover may help reduce this issue in future. One of these factors is job satisfaction, which are composed of two important factors, status and seniority. In this study, the correlation and chi-square visualization are utilized to determine the factors that affect employee turnover. The experiment was carried out to predict turnover using a private IT consultant dataset comparing three classification algorithms (decision tree, Naive Bayes, and Random Forest). The result shows that job duration and positioning are factors that influence employee turnover in a software company.

关键词： Employee turnover turnover factors chi-square classification algorithm

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Machine learning-based processing of unbalanced data sets for computer algorithms

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OPEN COMPUTER SCIENCE 2023年第1期13卷

作者： Zhou, Qingwei Qi, Yongjun Tang, Hailin Wu, Peng Guangdong Baiyun Univ Fac Megadata & Comp Guangzhou 510450 Guangdong Peoples R China Mongolian Univ Sci & Technol Sch Informat & Commun Technol Ulan Bator 13341 Mongolia Sichuan Tourism Univ Sch Informat & Engn Chengdu 610000 Sichuan Peoples R China

The rapid development of technology allows people to obtain a large amount of data, which contains important information and various noises. How to obtain useful knowledge from data is the most important thing at this stage of machine learning (ML). The problem of unbalanced classification is currently an important topic in the field of data mining and ML. At present, this problem has attracted more and more attention and is a relatively new challenge for academia and industry. The problem of unbalanced classification involves classifying data when there is insufficient data or severe category distribution deviations. Due to the inherent complexity of unbalanced data sets, more new algorithms and tools are needed to effectively convert a large amount of raw data into useful information and knowledge. Unbalanced data set is a special case of classification problem, in which the distribution between classes is uneven, and it is difficult to classify data accurately. This article mainly introduces the research on the processing method of computer algorithms based on the processing method of unbalanced data sets based on ML, aiming to provide some ideas and directions for the processing of computer algorithms based on unbalanced data sets based on ML. This article proposes a research strategy for processing unbalanced data sets based on ML, including data preprocessing, decision tree data classification algorithm, and C4.5 algorithm, which are used to conduct research experiments on processing methods for unbalanced data sets based on ML. The experimental results in this article show that the accuracy rate of the decision tree C4.5 algorithm based on ML is 94.80%, which can be better used for processing unbalanced data sets based on ML.

关键词： machine learning unbalanced data sets classification algorithm data processing computer algorithm decision tree classification

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Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients

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PATHOLOGY RESEARCH AND PRACTICE 2023年 242卷 154311页

作者： Jeyananthan, Pratheeba Univ Jaffna Fac Engn Jaffna Sri Lanka

SARS-CoV-2 pandemic is the current threat of the world with enormous number of deceases. As most of the countries have constraints on resources, particularly for intensive care and oxygen, severity prediction with high accuracy is crucial. This prediction will help the medical society in the selection of patients with the need for these constrained resources. Literature shows that using clinical data in this study is the common trend and molecular data is rarely utilized in this prediction. As molecular data carry more disease related information, in this study, three different types of RNA molecules (lncRNA, miRNA and mRNA) of SARS-COV-2 patients are used to predict the severity stage and treatment stage of those patients. Using seven different machine learning al-gorithms along with several feature selection techniques shows that in both phenotypes, feature importance selected features provides the best accuracy along with random forest classifier. Further to this, it shows that in the severity stage prediction miRNA and lncRNA give the best performance, and lncRNA data gives the best in treatment stage prediction. As most of the studies related to molecular data uses mRNA data, this is an interesting finding.

关键词： Severity prediction classification algorithm lncRNA miRNA and mRNA COVID-19 molecular data Feature selection Treatment stage

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A DDoS Attack Mitigation Framework for IoT Networks using Fog Computing

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Procedia Computer Science 2021年 182卷 13-20页

作者： Muhammad Aminu Lawal Riaz Ahmed Shaikh Syed Raheel Hassan Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia

The advent of 5G which strives to connect more devices with high speed and low latencies has aided the growth IoT network. Despite the benefits of IoT, its applications in several facets of our lives such as smart health, smart homes, smart cities, etc. have raised several security concerns such as Distributed Denial of Service (DDoS) attacks. In this paper, we propose a DDoS mitigation framework for IoT using fog computing to ensure fast and accurate attack detection. The fog provides resources for effective deployment of the mitigation framework, this solves the deficits in resources of the resource-constrained IoT devices. The mitigation framework uses an anomaly-based intrusion detection method and a database. The database stores signatures of previously detected attacks while the anomaly-based detection scheme utilizes k-NN classification algorithm for detecting the DDoS attacks. By using a database containing the attack signatures, attacks can be detected faster when the same type of attack is executed again. The evaluations using a DDoS based dataset show that the k-NN classification algorithm proposed for our framework achieves a satisfactory accuracy in detecting DDoS attacks.

关键词： Internet of Things (IoT) Anomaly mitigation DDoS Fog computing classification algorithm

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Diabetes Prediction Using Enhanced SVM and Deep Neural Network Learning Techniques: An algorithmic Approach for Early Screening of Diabetes

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INTERNATIONAL JOURNAL OF HEALTHCARE INFORMATION SYSTEMS AND INFORMATICS 2021年第4期16卷 1页

作者： Nagaraj, P. Deepalakshmi, P. Kalasalingam Acad Res & Educ Dept Comp Sci & Engn Sch Comp Krishnankoil India Kalasalingam Acad Res & Educ Krishnankoil India

Diabetes, caused by the rise in level of glucose in the blood, has many devices to identify it from blood samples. Diabetes, when unnoticed, may bring many serious diseases like heart attack and kidney disease. In this way, there is a requirement for solid research and learning model enhancement in the field of gestational diabetes identification and analysis. SVM is one of the powerful classification models in machine learning, and similarly, deep neural networks are powerful under deep learning models. In this work, the authors applied enhanced support vector machine and deep learning model deep neural network for diabetes prediction and screening. The proposed method uses a deep neural network obtaining its input from the output of enhanced support vector machine, thus having a combined efficacy. The dataset considered includes 768 patients' data with eight major features and a target column with result "Positive" or "Negative." Experiment is done with Python, and the outcome of the demonstration shows that the deep learning model gives more efficiency for diabetes prediction.

关键词： classification algorithm Deep Learning Model Deep Neural Networks Diabetes Machine Learning Support Vector Machine

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On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection

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SENSORS 2016年第12期16卷 2051-2051页

作者： Manfredini, Esteban Garbin Dovis, Fabio Politecn Torino Dept Elect & Telecommun I-10129 Turin Italy

In this paper, the Extended Coupled Amplitude Delay Lock Loop (ECADLL) architecture, previously introduced as a solution able to deal with a multipath environment, is revisited and improved to tailor it to spoofing detection purposes. Exploiting a properly-defined decision algorithm, the architecture is able to effectively detect a spoofer attack, as well as distinguish it from other kinds of interference events. The new algorithm is used to classify them according to their characteristics. We also introduce the use of a ratio metric detector in order to reduce the detection latency and the computational load of the architecture.

关键词： signal processing GNSS receiver spoofing detection classification algorithm

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Method for Detection of Airborne UEs Based on LTE Radio Measurements 28

Method for Detection of Airborne UEs Based on LTE Radio Meas...

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28th Annual IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

作者： Wigard, Jeroen Amorim, Rafhael Nguyen, Huan C. Kovacs, Istvan Z. Mogensen, Preben Nokia Bell Labs Aalborg Denmark Aalborg Univ Wireless Commun Networks Aalborg Denmark

ISBN: (纸本)9781538635315

Unmanned Aerial Vehicles (UAVs) are expected to be connected through cellular networks. As the radio characteristics are different for airborne UEs compared to terrestrial UEs, it is beneficial to identify whether a UE is airborne (on a UAV) or on the ground, such that interference and mobility management can be optimized for UAVs separately from terrestrial UEs. In this paper, we present a classification algorithm using existing LTE UE radio measurements to identify whether a UE is airborne or terrestrial. The method is verified with LTE measurements made in a rural area at different heights, including terrestrial measurements and it is shown that the method in 3 out of the 4 different measurement cases can detect a UE to be airborne with 99% likelihood, while the fourth case still can classify a UE correctly in 95% of the cases. The right classification can further be improved by taking multiple consecutive samples into account before making a classification decision.

关键词： UAV detection classification algorithm LTE radio measurement

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Patterns of brain structural connectivity differentiate normal weight from overweight subjects

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NEUROIMAGE-CLINICAL 2015年 7卷 506-517页

作者： Gupta, Arpana Mayer, Emeran A. Sanmiguel, Claudia P. Van Horn, John D. Woodworth, Davis Ellingson, Benjamin M. Fling, Connor Love, Aubrey Tillisch, Kirsten Labus, Jennifer S. Univ Calif Los Angeles Gail & Gerald Oppenheimer Family Ctr Neurobiol St IBOP Los Angeles CA USA Univ Calif Los Angeles David Geffen Sch Med Los Angeles CA 90095 USA Univ Calif Los Angeles Div Digest Dis Los Angeles CA USA Univ Calif Los Angeles Ahmanson Lovelace Brain Mapping Ctr Los Angeles CA USA Univ Calif Los Angeles GLA VHA Integrat Med Los Angeles CA USA Univ So Calif Keck Sch Med Inst Neuroimaging & Informat Los Angeles CA 90033 USA Univ Calif Los Angeles David Geffen Sch Med Radiol Los Angeles CA 90095 USA

Background: Alterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks. Aim: To apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements. Methods: Structural images (N = 120, overweight N = 63) and diffusion tensor images (DTI) (N = 60, overweight N = 30) were obtained from healthy control subjects. For the total sample the mean age for the overweight group (females = 32, males = 31) was 28.77 years (SD = 9.76) and for the normal weight group (females = 32, males = 25) was 27.13 years (SD = 9.62). Regional segmentation and parcellation of the brain images was performed using Freesurfer. Deterministic tractography was performed to measure the normalized fiber density between regions. A multivariate pattern analysis approach was used to examine whether brain measures can distinguish overweight from normal weight individuals. Results: 1. White-matter classification: The classification algorithm, based on 2 signatures with 17 regional connections, achieved 97% accuracy in discriminating overweight individuals from normal weight individuals. For both brain signatures, greater connectivity as indexed by increased fiber density was observed in overweight compared to normal weight between the reward network regions and regions of the executive control, emotional arousal, and somatosensory networks. In contrast, the opposite pattern (decreased fiber density) was found between ventromedial prefrontal cortex and the anterior insula, and between thalamus and executive control network regions. 2. Gray-matter classification: The classification algorithm, based on 2 signatures with 42 morphological features, achi

关键词： Obesity Overweight Morphological gray-matter Anatomical white-matter connectivity Reward network Multivariate analysis classification algorithm

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A Novel Machine Learning-based Framework for Detecting Religious Arabic Hatred Speech in Social Networks

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第9期13卷 767-776页

作者： Masadeh, Mahmoud Davanager, Hanumanthappa Jayappa Muaad, Abdullah Y. Yarmouk Univ Comp Engn Dept Irbid 21163 Jordan Mysore Univ Dept Studies Comp Sci Mysore 570006 Manasagangothri India Sanaa Community Coll Sanaa 5695 Yemen

Social media platforms generate a huge amount of data every day. However, liberty of speech through these networks could easily help in spreading hatred. Hate speech is a severe concern endangering the cohesion and structure of civil societies. With the increase in hate and sarcasm among the people who contact others over the internet in this era, there is a dire need for utilizing artificial intelligence (AI) technology innovation that would face this problem. The rampant spread of hate can dangerously break society and severely damage marginalized people or groups. Thus, the identification of hate speech is essential and becoming more challenging, where the recognition of hate speech on time is crucial in stopping its dissemination. The capacity of the Arabic morphology and the scarcity of resources for the Arabic language makes the task of distinguishing hate speech even more demanding. For fast identification of Arabic hate speech in social network comments, this work presents a comprehensive framework with eight machine learning (ML) and deep learning (DL) algorithms, namely Gradient Boosting (GB), K-Nearest Neighbor (K-NN), Logistic Regression (LR), Naive Bayes (NB), Passive Aggressive Classifier (PAC), Support Vector Machine (SVM), Ara-BERT, and BERT-AJGT are implemented. Two representation techniques have been used in the proposed framework in order to extract features: a bag of words followed by BERT-based context text representations. Based on the result and discussion part, context text representation techniques with Ara-BERT and BERT-AJGT outperform all other ML models and related work with accuracy equal to 79% for both models.

关键词： Machine learning Arabic language hatred detection social network classification algorithm

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