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Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma

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arXiv 2024年

作者： Bhattacharjya, Abanti Islam, Md Manowarul Uddin, Md Ashraf Talukder, Md. Alamin Azad, A.K.M. Aryal, Sunil Paul, Bikash Kumar Tasnim, Wahia Abdulllah Almoyad, Muhammad Ali Moni, Mohammad Ali School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Department of Software Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Riyadh13318 Saudi Arabia Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Bangladesh Department of Basic Medical Sciences College of Applied Medical Sciences King Khalid University Saudi Arabia Artificial Intelligence & Data Science Faculty of Health and Behavioural Sciences The University of Queensland Australia

With the advent of Information technology, the Bioinformatics research field is becoming increasingly attractive to researchers and academicians. The recent development of various Bioinformatics toolkits has facilitated the rapid processing and analysis of vast quantities of biological data for human perception. Most studies focus on locating two connected diseases and making some observations to construct diverse gene regulatory interaction networks, a forerunner to general drug design for curing illness. For instance, Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. In this study, we select EGFR-mutated lung adenocarcinoma and Hypopharyngeal cancer by finding the Lung metastases in hypopharyngeal cancer. To conduct this study, we collect Mircorarray datasets from GEO (Gene Expression Omnibus), an online database controlled by NCBI. Differentially expressed genes, common genes, and hub genes between the selected two diseases are detected for the succeeding move. Our research findings have suggested common therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). Our suggested therapeutic molecules will be fruitful for patients with those two diseases simultaneously. © 2024, CC BY.

关键词： Topology

Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 22

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Optimizing Ethanol Production in Escherichia Coli Using a Hy...

Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization Artificial Bee Colony Ethanol Flux Balance Analysis

Detection of Misinformation Related to Pandemic Diseases using Machine Learning Techniques in Social Media Platforms

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EAI Endorsed Transactions on Pervasive Health and Technology 2024年 10卷

作者： Naeem, J. Gul, O.M. Parlak, I.B. Karpouzis, K. Salman, Y.B. Kadry, S.N. Department of Computer Engineering Bahcesehir University Istanbul Turkey Informatics Institute Istanbul Technical University Istanbul Turkey Department of Computer Engineering Galatasaray University Istanbul Turkey Panteion University of Social and Political Sciences Greece Department of Software Engineering Bahcesehir University Istanbul Turkey Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon MEU Research Unit Middle East University Amman11831 Jordan

INTRODUCTION: The advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. OBJECTIVES: In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. METHODS: Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19-related discussions, our research applies Support Vector Machine (SVM), Decision tree, logistic regression, and neural networks to perform in depth analysis and classification of comments into two categories: positive and negative information. The innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. This ensemble approach significantly improves the model’s overall accuracy and adaptability. RESULTS: Experimental results underscore the efficacy of our methodology, showcasing marked improvements in detection performance compared to individual models. After applying ensemble learning, we achieve an accuracy of 91% for Facebook data, 79% for Instagram data, 80% for Twitter data and 95% for YouTube data. CONCLUSION: Our system not only aids in curbing the dissemination of COVID-19 misinformation but also provides a robust framework for addressing misinformation across various contexts on social media platforms. © 2024 J. Naeem et al., licensed to EAI.

关键词： COVID-19

Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network

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arXiv 2024年

作者： Yang, Xinquan Li, Xuguang Luo, Xiaoling Zeng, Leilei Zhang, Yudi Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China

Surgical guide plate is an important tool for the dental implant surgery. However, the design process heavily relies on the dentist to manually simulate the implant angle and depth. When deep neural networks have been applied to assist the dentist quickly locates the implant position, most of them are not able to determine the implant depth. Inspired by the video grounding task which localizes the starting and ending time of the target video segment, in this paper, we simplify the implant depth prediction as video grounding and develop a Texture Perceive Implant Depth Prediction Network (TPNet), which enables us to directly output the implant depth without complex measurements of oral bone. TPNet consists of an implant region detector (IRD) and an implant depth prediction network (IDPNet). IRD is an object detector designed to crop the candidate implant volume from the CBCT, which greatly saves the computation resource. IDPNet takes the cropped CBCT data to predict the implant depth. A Texture Perceive Loss (TPL) is devised to enable the encoder of IDPNet to perceive the texture variation among slices. Extensive experiments on a large dental implant dataset demonstrated that the proposed TPNet achieves superior performance than the existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

Joint Beamforming Design and Stream Allocation for Non-Coherent Joint Transmission in Cell-Free MIMO Networks

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arXiv 2024年

作者： Wang, Xi Zhao, Xiaotong Wang, Juncheng Li, You Shi, Qingjiang The School of Software Engineering Tongji University Shanghai201804 China The Department of Computer Science Hong Kong Baptist University Hong Kong Huawei Technologies Co. Ltd. Chengdu611730 China The Shenzhen Research Institute of Big Data Shenzhen518172 China

We consider joint beamforming and stream allocation to maximize the weighted sum rate (WSR) for non-coherent joint transmission (NCJT) in user-centric cell-free MIMO networks, where distributed access points (APs) are organized in clusters to transmit different signals to serve each user equipment (UE). We for the first time consider the common limits of maximum number of receive streams at UEs in practical networks, and formulate a joint beamforming and transmit stream allocation problem for WSR maximization under per-AP transmit power constraints. Since the integer number of transmit streams determines the dimension of the beamformer, the joint optimization problem is mixed-integer and nonconvex with coupled decision variables that is inherently NP-hard. In this paper, we first propose a distributed low-interaction reduced weighted minimum mean square error (RWMMSE) beamforming algorithm for WSR maximization with fixed streams. Our proposed RWMMSE algorithm requires significantly less interaction across the network and has the current lowest computational complexity that scales linearly with the number of transmit antennas, without any compromise on WSR. We draw insights on the joint beamforming and stream allocation problem to decouple the decision variables and relax the mixed-integer constraints. We then propose a joint beamforming and linear stream allocation algorithm, termed as RWMMSE-LSA, which yields closed-form updates with linear stream allocation complexity and is guaranteed to converge to the stationary points of the original joint optimization problem. Simulation results demonstrate substantial performance gain of our proposed algorithms over the current best alternatives in both WSR performance and convergence time. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

Predictive Modeling For Real-Time Personalized Health Monitoring in Muscular Dystrophy Management

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arXiv 2024年

作者： Akkaoui, Mohammed Serigina, Zlata Ren, Zheng Yuan, Fang School of Information Science & Engineering Dalian Polytechnic University Dalian China The School of International Education Fujian Medical University Fuzhou China Department of Software and Big Data Technology Dalian Neusoft University of Information Dalian China School of Engineering Practice & Innovation-Entrepreneurship Education Dalian Polytechnic University Dalian China

Muscular Dystrophy is a group of genetic disorders that progressively affect the strength and functioning of muscles, thereby affecting millions of people worldwide. The lifetime nature of MD requires continuous follow-up care due to its progressive nature. This conceptual paper proposes an Internet of Things-based system to support the management of MD through remote, multi-dimensional monitoring of patients in order to provide real-time health status updates. Traditional methods have failed to give actionable data in real time, hence denying healthcare providers the opportunity to make evidence-based decisions. Technology-driven approaches are urgently needed to provide deep insights into disease progression and patient health. It aims to enhance treatment strategies, enabling patients to better manage their condition and giving healthcare professionals more confidence in their management decisions. Copyright © 2024, The Authors. All rights reserved.

关键词： Electronic health record

The Impact of 5G Technology on Agile Project Management: A Cross-Industry Analysis

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The Impact of 5G Technology on Agile Project Management: A C...

Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Mahesh Kumar Mishra Krishnamoorthy Selvaraj Kathari Santosh Mohd Aarif S.Suma Christal Mary B Kiran Bala Delta System and Software Inc McKinney Texas USA Dept of ERP Encloud service LLC Keller Texas US Department of MBA CMR Institute of Technology Bengaluru Bengaluru India IOSRP Noida UP India Department of Information Technology Panimalar Engineering College Poonamalle Chennai Department of Artificial Intelligence and Data Science K.Ramakrishnan college of Engineering Trichy

Project management and organisation are completely transformed by the introduction of 5G technology, which has brought in a new era of connection. This research investigates how 5G networks will affect Enterprise Resource Planning (ERP), Agile Project Management (APM) and the larger context of the Technology-Organization-Environment (TOE) paradigm through a cross-industry examination. Agile approaches may benefit from improved adaptive design, cooperation and connectivity as a result of 5G technology being included into project management procedures. A team of developers can work together more easily and make decisions more quickly with the real-time data transfer made possible by 5G networks' higher capacity and speed. This study looks at how Agile methods for managing projects are more effective and flexible when using 5G's low latency and high data throughput. This research explores how Enterprise Resource Planning (ERP) systems may be affected by 5G. With 5G networks' enhanced capabilities, current information synchronisation is made possible in new ways, enabling ERP systems to function with increased precision and speed. This study investigates how supply chain administration, resource allocation and general organisational agility are improved by 5G-enabled ERP. This study evaluates how various sectors' organisational structures, procedures, and objectives are affected by the deployment of 5G infrastructure. This research outlines critical elements that influence the successful implementation of 5G into project management and ERP systems, including technology preparedness, organisational culture, and external factors. This research provides important insights into the potential for transformation of 5G technology through a thorough cross-industry assessment. It also gives organisations an established basis on which to adapt their Agile Project Management and Enterprise Resource Planning practises in the dynamic context of today's digital world.

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A WiFi-aware method for mobile data offloading with deadline constraints

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A WiFi-aware method for mobile data offloading with deadline...

作者： Tang, Wenda Wu, Chaobing Qi, Lianyong Zhang, Xuyun Xu, Xiaolong Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China School of Information Science and Engineering Chinese Academy of Education Big Data Qufu Normal University Rizhao China Department of Electrical and Computer Engineering The University of Auckland Auckland New Zealand Jiangsu Engineering Centre of Network Monitoring School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

With an increasing number of public WiFi hotspots have been constructed in metropolitan areas, citizens can leverage these WiFi hotspots to surf the Internet with their smart devices almost everywhere. Compared to cellular networks, most people prefer using WiFi because this can not only save money by reducing the cellular data traffic but also save energy to prolong the battery lifetime of mobile devices. Mobile offloading techniques give the opportunity for smart devices to offload cellular data traffic to WiFi networks whenever they are connected WiFi networks. Although offloading to WiFi networks can cause communication delay, many delay-tolerant applications still prefer doing so in order to save the cellular data traffic. Existing offloading approaches only take the application delay tolerance into consideration and therefore easy to induce some tasks missing deadlines. In this paper, we propose a novel WiFi-aware method for mobile data offloading through WiFi networks with deadline constraints. Specifically, it takes the WiFi distribution in city areas into consideration while making offloading decision. The simulation results on real-world data have shown that our method improves the percentage of tasks that can meet their deadlines, and reduces the average task completion time by comparing with other outstanding methods. © 2019 John Wiley & Sons, Ltd.

关键词： Wireless local area networks (WLAN)

A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

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arXiv 2024年

作者： Moubayed, Abdallah Injadat, MohammadNoor Alhindawi, Nouh Samara, Ghassan Abuasal, Sara Alazaidah, Raed School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Electrical and Computer Engineering Department Western University LondonON Canada Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students’ progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students’ performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Students