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Big data-driven target identification by machine learning: DRD2 as a therapeutic target for psoriasis

Journal of Dermatological science 2025年第1期119卷 9-17页

作者： Sakai, Takashi Sawada, Ryusuke Ichinose, Otoha Terabayashi, Takeshi Hatano, Yutaka Yamanishi, Yoshihiro Ishizaki, Toshimasa Department of Dermatology Faculty of Medicine Oita University Oita Japan Department of Pharmacology Okayama University Graduate School of Medicine Dentistry and Pharmaceutical Sciences Okayama Japan Department of Bioscience and Bioinformatics Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Department of Pharmacology Faculty of Medicine Oita University Oita Japan Department of Complex Systems Science Graduate School of Informatics Nagoya University Aichi Japan

Background: The development of medical treatments has traditionally relied on researchers leveraging scientific knowledge to hypothesize disease mechanisms and identify therapeutic agents. However, the depletion of novel therapeutic targets has become a significant challenge, resulting in stagnation within pharmaceutical research. Objective: To address the scarcity of therapeutic targets, we developed a machine learning (ML)-based system capable of predicting therapeutic target molecules for diseases. To validate its utility, we applied this system to psoriasis, aiming to identify novel treatment strategies. Methods: Our approach utilized a large clinical database to calculate reporting odds ratios for all drugs associated with the prevention of diseases of interest. We identified target proteins by analyzing large chemical structure databases to discover proteins commonly associated with preventive drug candidates. Experimental validation was conducted by administering a predicted therapeutic candidate in an imiquimod-induced psoriasis mouse model. Results: The ML-based predictions identified drugs for Parkinson's disease as potential preventive candidates for psoriasis. Further analysis highlighted dopamine receptor D2 (DRD2) as a therapeutic target. Administration of a DRD2 agonist alleviated psoriasis symptoms in mice, evidenced by the downregulation of mRNA expression in the IL-17 pathway and reduced serum tumor necrosis factor-α levels. Conclusion: This study demonstrates the utility of a novel ML-based system for identifying therapeutic targets, as shown by its successful application in uncovering the role of DRD2 in psoriasis. Beyond psoriasis, this system offers significant potential for exploring pathological mechanisms and discovering therapeutic targets across various diseases. © 2025 The Authors

关键词： artificial intelligence big data dopamine receptor D2 machine learning psoriasis

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Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage

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computers, Materials & Continua 2022年第7期72卷 775-796页

作者： Zeyad Ghaleb Al-Mekhlafi Ebrahim Mohammed Senan Taha H.Rassem Badiea Abdulkarem Mohammed Nasrin M.Makbol Adwan Alownie Alanazi Tariq S.Almurayziq Fuad A.Ghaleb Department of Information and Computer Science College of Computer Science and EngineeringUniversity of Ha’ilHa’il81481Saudi Arabia Department of Computer Science&Information Technology Dr.Babasaheb Ambedkar Marathwada UniversityAurangabad431004India Faculty of Science and Technology Bournemouth UniversityPooleBH125BBUK Department of Computer Engineering College of Computer Science and EngineeringUniversity of Ha’ilHa’il81481Saudi Arabia College of Computer Science and Engineering Hodeidah UniversityHodeidah966Yemen School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaJohor BahruJohor81310Malaysia

Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart *** this work,a dataset containing medical,physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning,deep learning and a hybrid technique between deep learning and machine learning on theMagnetic Resonance Imaging(MRI)dataset for cerebral *** the first dataset(medical records),two features,namely,diabetes and obesity,were created on the basis of the values of the corresponding *** t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data ***,the Recursive Feature Elimination algorithm(RFE)was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant *** features are fed into the various classification algorithms,namely,Support Vector Machine(SVM),K Nearest Neighbours(KNN),Decision Tree,Random Forest,and Multilayer *** algorithms achieved superior *** Random Forest algorithm achieved the best performance amongst the algorithms;it reached an overall accuracy of 99%.This algorithm classified stroke cases with Precision,Recall and F1 score of 98%,100%and 99%,*** the second dataset,the MRI image dataset was evaluated by using the AlexNet model and AlexNet+SVM hybrid *** hybrid model AlexNet+SVM performed is better than the AlexNet model;it reached accuracy,sensitivity,specificity and Area Under the Curve(AUC)of 99.9%,100%,99.80%and 99.86%,respectively.

关键词： Stroke cerebral haemorrhage deep learning machine learning t-SNE and RFE algorithms

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Enhancing Rehabilitation Outcomes: A Hybrid Cognitive-Machine Learning Approach 9

Enhancing Rehabilitation Outcomes: A Hybrid Cognitive-Machin...

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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Jadala, Vijaya Chandra Raju, S. Hrushikesava Adinarayna, S. Choudhury, Nabanita Jumlesha, Shaik Sesadri, U. School of Computer Science and Artificial Intelligence Sr University Ananthasagar Hasanparthy Hanumakonda Telangana Warangal506371 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Green Fields Vaddeswaram Guntur522502 India College of Engineering Andhra University Department of Computer Science and Systems Engineering Visakhapatnam531162 India Assam down town University Faculty of Computer Technology Assam Guwahati India AP Tirupati517501 India Vardhaman College of Engineering Department of Cse Telangana Hyderabad India

ISBN: (纸本)9798350377972

Traditional rehabilitation methods often focus on a single impairment, leading to challenges such as low motivation and inadequate responses to the diverse needs of patients, time taken for appointments, and lack of personal monitoring of health status. To address these issues, The model employs meta-cognitive strategies, dynamic adaptive training, and modified Convolutional Neural Networks (CNN) within an ensemble framework, targeting multiple impairments while tailoring interventions to individual patient requirements. A hybrid model proposed would integrate cognitive and machine learning methodologies to enhance personalized rehabilitation. By selectively employing meta-cognitive strategies, dynamic adaptive training, and modified Convolutional Neural Networks (CNN) within an ensemble framework, the proposed system aims to bridge existing gaps in rehabilitation practices. The hybrid system is designed to efficiently manage patient data, reduce the complexity of rehabilitation tasks, and optimize computational resources. The use of stacking as an ensemble method further mitigates biases and uncertainties associated with individual models, thereby improving the reliability of the rehabilitation framework. Preliminary outcomes indicate that this hybrid approach achieves superior accuracy and efficiency compared to traditional methods, ultimately enhancing adaptability to the varied needs and preferences of patients. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring the Usage of Mobile Phones in the Assessment Process: A Case Study 47

Exploring the Usage of Mobile Phones in the Assessment Proce...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Kovačević, Željko Bubnic, Bostjan Kosar, Tomaž Zagreb University Of Applied Sciences Department Of Computer Science And Information Technology Zagreb Croatia University Of Maribor Faculty Of Electrical Engineering And Computer Science Maribor Slovenia

ISBN: (纸本)9798350382495

Technology is changing how students learn in the 21st century significantly. Integrating mobile devices in teaching, learning, and assessment processes has emerged as an important strategy for improving teaching methodologies and students' learning. However, adopting mobile devices presents several challenges, including understanding the students' performance, impact on time, and efficiency in the assessment process. This study explores this area with an empirical study of using mobile devices and compares the results to those using traditional desktop computers. In the educational environment, we investigated students' effective use while interacting with mobile and desktop versions of the Second Introductory programming summative assessment. Multinational/multi-institutional research involved a comparative analysis between two student groups undertaking identical tasks on mobile phones and desktop computers. Our results suggest that the students' results are not affected significantly by using the mobile version of the assessment application. These results highlight the usability of mobile devices as a flexible and effective tool for various educational applications, strengthening their role in the modern learning environment. © 2024 IEEE.

关键词： Object oriented programming

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A Model for Predicting Chronic Kidney Diseases Based on Medical Data Using Reinforcement Learning

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SN computer science 2024年第4期5卷 353页

作者： Nramban Kannan, Senthil Kumar Aseervatham, Joshi Moholkar, Kavita Palanimuthu, Mithun Marappan, Saranya Narendran, Muthusamy Sathar, Banu Sengan, Sudhakar Department of Computer Science & amp Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai 600123 India Department of Computer Science and Business Systems Engineering JSPM’S Rajarshi Shahu College of Engineering Savitribai Phule Pune University Pune 411033 India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai 600119 India Department of Information Technology Paavai Engineering College Tamil Nadu Namakkal 637018 India Department Information Technology KCG College of Technology Tamil Nadu Chennai 600097 India Department of Master of Computer Application T. J. Institute of Technology Chennai 600100 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627451 India

Kidney diseases (KD) are a global public health concern affecting millions. Early detection and prediction are crucial for effective treatment. Artificial intelligence (AI) techniques have been used in KDP to analyze past medical records, applying patients’ Electronic Medical Record (EHR) data. However, conventional statistical analysis methods conflict with fully comprehending the complexity of EHR data. AI algorithms have helped early KDP learn and identify complex data patterns. However, challenges include training heterogeneous historical data, protecting privacy and security, and developing monitoring system regulations. This study addresses the primary challenge of training heterogeneous datasets for real-world evaluation. Early detection and diagnosis of chronic kidney disease (CKD) is crucial for improved outcomes, reduced healthcare costs, and reliable treatment. Early treatments are crucial for CKD, as it often develops without apparent symptoms. Predictive models, particularly those using reinforcement learning (RL), can identify significant trends in complex healthcare information, which standard techniques may struggle with. The study makes KDP more accurate and reliable using RL methods on clinical data. This lets doctors find diseases earlier and treat them better by looking at static and changing health measurements. Machine learning (ML) algorithms can enhance the accuracy of AI systems over time, enhancing their effectiveness in detecting and diagnosing diseases. In the current investigation, the RL-ANN model is implemented for performing enforceable CKD by assessing the outcomes of multiple neural networks, which include FNN, RNN, and CNN, according to parameters such as accuracy, sensitivity, specificity, prediction error, prediction rate, and kidney failure rate (KFR). The recommended RL-ANN method has a lower failure rate of 70% based on the KFR data. Further, the proposed approach earned 95% in PR and 70% in analysis of errors. However, the RL

关键词： Alerting systems and diagnostic assistance Chronic kidney disease Machine learning Medical history RL-ANN

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Preventing disinformation spread across network clusters by strategic edge deletions: a Lagrangian heuristic

Preventing disinformation spread across network clusters by ...

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2025 Joint National Conference on Cybersecurity, ITASEC and SERICS 2025

作者： Cerulli, Martina Gaudioso, Manlio Serra, Domenico Sorgente, Carmine Department of Computer Science University of Salerno Fisciano Italy Department of Computer Engineering Modeling Electronics and Systems University of Calabria Rende Italy Department of Mathematics University of Salerno Fisciano Italy

The rapid spread of fake news through dense clusters in networks jeopardizes trust and stability. Traditional approaches targeting harmful content often overlook the structural network dynamics that drive disinformation. The Cluster Deletion Problem addresses this by removing edges that bridge communities, disrupting disinformation flow while preserving intra-group communication. Indeed, given a graph representing the considered network, the Cluster Deletion Problem aims to transform it into a cluster graph via minimal edge deletions. In this paper, we address this problem with a tailored Lagrangian heuristic based on a dual descent algorithm, incorporating both a subgradient method and an ad-hoc polynomial-time repair heuristic. The proposed approach is tested on existing instances, in most cases providing better lower bounds with respect to the ones existing in the literature. © 2025 Copyright for this paper by its authors

关键词： Lagrange multipliers

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Periodic Physical Activity Information Segmentation, Counting and Recognition From Video

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IEEE Access 2023年 11卷 23019-23031页

作者： Cheng, Sheng-Hsien Sarwar, Muhammad Atif Daraghmi, Yousef-Awwad Ik, Tsi-Ui Li, Yih-Lang National Yang Ming Chiao Tung University College of Computer Science Department of Computer Science Hsinchu30010 Taiwan Palestine Technical University-Kadoorie Department of Computer Systems Engineering TulkaremP310 Palestine

The research on complex human body motion including sports and workout activity recognition is a major challenge and long-lasting problem for the computer vision community. Recent development in deep learning algorithms to track people's workout activity characteristics based on video sensors can be used to infer the human body pose for further analysis. Specifically, tracking complex body movements while performing multi-pose physical exercise helps individuals provide fine granularity feedback including activity repetition counting and activity recognition. Therefore, this research proposes a system that provides a repetition counter and activity recognition of physical exercise from video frames (extracted 3D human skeleton using VIBE) based on the deep semantic features and repetitive segmentation algorithm. The proposed system locates both ends of the activity's action and segments the activity into multiple unit actions which improves activity recognition, time intervals, # of sets, and other quantitative values of activity. The proposed system is evaluated on the physical activities dataset named "NOL-18 Exercise"through extensive experiments. The proposed system results show that the accuracy of the repetitive action segmentation is 96.27% with 0.23% time error, and action recognition reaches 99.06%. The system can be employed to fitness or rehabilitation centers and used for treating patients. © 2013 IEEE.

关键词： Sports

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Towards Electronic Voting Using Face Verification Technology 19

Towards Electronic Voting Using Face Verification Technology

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19th International Conference on Emerging Technologies, ICET 2024

作者： Hashmi, Huma Ahmed, Hamza Ahmar, Rana Ali Syed, Muhammad Ibrahim Khan, Khurram Mahmood, Zahid Comsats University Islamabad Department of Electrical and Computer Engineering Abbottabad Pakistan Ziel Global Limited Department of Product Design Islamabad Pakistan Gik Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering KPK Topi Pakistan

ISBN: (纸本)9798350379587

This study presents an electronic voting system using the face detection and verification methods. The proposed system consists of face detection and face identification module to detect and authenticate voters' face. Initially, voter's face is detected using facial land mark estimation module. While face verification module uses linear discriminant analysis integrated with the adaptive boosting method. Face detection and verification modules work perfectly up to pose variation up to ±15°. Detailed experiments show that proposed system achieves a mean accuracy of 94.15% and requires nearly 50 seconds to cast a single vote. The study also contributes to the development of an excellent alternative to conventional electoral system. © 2024 IEEE.

关键词： Photointerpretation

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Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

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IEEE Transactions on Automation science and engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

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Tomato Leaf Diseases Prediction Using Deep Learning Algorithms

Tomato Leaf Diseases Prediction Using Deep Learning Algorith...

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2024 International Conference on Advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Ananthi, P. Devi, K. Nirmala Gopinath, D. Shanmugapriya, P. Gowtham, S. Kongu Engineering College Department Of Computer Technology Tamilnadu Perundurai India Bangaluru India Kristu Jayanti College Department Of Computer Science Bangaluru India Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798350364828

In the realm of agriculture, infections on tomato leaves pose a worldwide danger to established tomato production, impacting a large number of farmers worldwide. To ensure healthy tomato plant growth and food security for the world's expanding population, early disease detection, treatment, and solution identification are essential. The use of computer-assisted technologies for disease detection in plant leaf is prevalent in modern agriculture. The goal was to develop an effective deep learning model for the detection of diseases in tomato plant leaf. To address memory and computational resource constraints, the proposed model incorporates a lightweight 'Region Convolutional Neural Network (R-CNN)' head and conducted experiments with various neural network architectures, including Densenet-169, optimized Mobilenet-V2 and Resnet-50, to enhance detection accuracy and computational efficiency. The research extends the capabilities of these neural network architectures by optimizing anchor proportions in the Conventional Neural Network (CNN) and modifying feature extraction topologies. These optimizations resulted in improved detection accuracy and computational efficiency and compared proposed technique against cutting-edge models to evaluate its viability and robustness. The outcomes of the research were promising. Impressive outcomes were obtained by the proposed approach regarding accuracy, F1-score, recall, and precision. © 2024 IEEE.

关键词： Computational efficiency

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