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Predicting Mutation-Disease Associations Through Protein Interactions Via Deep Learning

IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4512-4523页

作者： Xue Li Ben Cao Jianmin Wang Xiangyu Meng Shuang Wang Yu Huang Enrico Petretto Tao Song School of Computer Science and Technology China University of Petroleum (East China) Qingdao Shandong China Centre for Computational Biology Duke-NUS Medical School Singapore Singapore School of Computer Science and Technology Dalian University of Technology Dalian Liaoning China Department of Integrative Biotechnology Yonsei University Incheon South Korea National Engineering Research Center for Software Engineering Peking University Beijing China

Disease is one of the primary factors affecting life activities, with complex etiologies often influenced by gene expression and mutation. Currently, wet lab experiments have analyzed the mechanisms of mutations, but these are usually limited by the costs of wet experiments and constraints in sample types and scales. Therefore, this paper constructs a real-world mutation-induced disease dataset and proposes Capsule and Graph topology networks with Multi-head attention (CGM) to predict the mutation-disease associations. CGM can accurately predict protein mutation-disease associations, and to further elucidate the pathogenicity of protein mutations, we also verified that protein mutations lead to protein structural alterations by the model, which suggests that mutation-induced conformational changes may be an important pathogenic factor. Limited by the size of the mutated protein dataset, we also performed experiments on benchmark and imbalanced datasets, where CGM mined 22 unknown protein interaction pairs from the benchmark dataset, better illustrating the potential of CGM in predicting mutation-disease associations. In summary, this paper curates a real dataset. It proposes that CGM predicts protein mutations and disease associations, providing a novel tool for further understanding of biomolecular pathways and disease mechanisms.

关键词： Proteins Diseases Protein engineering Predictive models Cancer Electronic mail Heart Deep learning Bioinformatics Tumors

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A Hybrid Deep Model for Brain Tumor Classification

A Hybrid Deep Model for Brain Tumor Classification

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International Conference on Medical Imaging and computer-Aided Diagnosis, MICAD 2021

作者： Ayaz, Hamail Ahmad, Muhammad Tormey, David McLoughlin, Ian Unnikrishnan, Saritha Faculty of Engineering and Design and Centre for Precision Engineering Materials and Manufacturing Research Institute of Technology Sligo Sligo91 YW50 Ireland Department of Computer Science National University of Computer and Emerging Sciences Islamabad Chiniot-Faisalabad Campus Chiniot35400 Pakistan Department of Computer Science and Applied Physics Galway-Mayo Institute of Technology Galway Ireland

ISBN: (纸本)9789811638794

Classification of brain tumors from Magnetic Resonance Images (MRIs) using computer-Aided Diagnosis (CAD) has faced some major challenges. Diagnosis of brain tumors such as glioma, meningioma, and pituitary mostly rely on manual evaluation by neuro-radiologists and is prone to human error and subjectivity. In recent years, Machine Learning (ML) techniques have been used to improve the accuracy of tumor diagnosis with the expense of intensive pre-processing and computational cost. Therefore, this work proposed a hybrid Convolutional Neural Network (CNN) (i.e., AlexNet followed by SqueezeNet) to extract quality tumor biomarkers for better performance of the CAD system using brain tumor MRI’s. The features extracted using AlexNet and SqueezeNet are fused to preserve the most important biomarkers in a computationally efficient manner. A total of 3064 brain tumors (708 Meningioma, 1426 Glioma, and 930 Pituitaries) MRIs have been experimented. The proposed model is evaluated using several well-known metrics, i.e., Overall accuracy (94%), Precision (92%), Recall (95%), and F1 score (93%) and outperformed many state of the art hybrid methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance

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Federated Multi-Agent Reinforcement Learning for Resource Allocation in NR-V2X Mode 2

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IEEE Internet of Things Journal 2025年

作者： Saad, Malik Muhammad Tariq, Muhammad Ashar Ajmal, Mahnoor Kim, Dongkyun Srivastava, Gautam Kyungpook National University School of Computer Science and Engineering Dongseo University Republic of Korea and the School of Computer Science Busan Daegu Korea Republic of Kyungpook National University School of Computer Science and Engineering Daegu Korea Republic of Brandon University Department of Math and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India

The Third Generation Partnership Project (3GPP) introduced Cellular Vehicle-to-Everything (C-V2X) for vehicular communications. In the standard, C-V2X Mode 4 is defined for the distributed resource selection. Subsequently, in 3GPP Release 16, NR-V2X is introduced with Mode 1 and Mode 2 for vehicular communications. Likewise C-V2X Mode 4, NR-V2X Mode 2 is used for decentralized resource scheduling. The vehicles select the resources based on their local observations by utilizing the Semi-persistent Scheduling (SPS). Since, the vehicles select the resources based on the local observation, sensing nature of SPS is challenged by the hidden node problem that lead to resource conflict. To resolve the contention, 3GPP also introduced the Physical Sidelink Feedback Channel (PSFCH) to assist the distributive resource scheduling based on the receiver feedback. However, this incurred a signaling overhead. In this work, federated learning is exploited for distributive training via offline method and distributive multi-agent-based resource scheduling is performed following the principles of NR-V2X Mode 2. Distributed training favors the model accuracy by accommodating the varying affect of the environment due to the high mobile dynamics. Simulation is conducted by integrating SUMO in conjunction with 3GPP NR-V2X standard. Performance results demonstrate a substantial improvement compared to other deep learning methods, where centralized training and random resource selection procedures are employed. This research marks a significant stride towards efficient and conflict-resilient resource allocation in vehicular communications. © 2014 IEEE.

关键词： Resource allocation

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Assessment of Wheat Productivity Enhancement by Integrated Nutrient Management (INM) using Remote Sensing

Assessment of Wheat Productivity Enhancement by Integrated N...

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Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), International Conference on

作者： Uferah Shafi Rafia Mumtaz Zahid Mahmood Muhammad Deedahwar Mazhar Qureshi§ Raza Ullah Khan¶ Syed Ishtiaq HyderSikander Khan Tanveer School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan Land Resources Research Institute National Agricultural Research Centre Islamabad Pakistan Wheat Programme Crop Sciences Institute National Agricultural Research Centre Islamabad Pakistan

Integrated nutrient management (INM) is a widely used practice to enhance crop productivity where bio-organic amendments coupled with chemical fertilizers are used. Therefore, a field experiment is performed to assess the productivity enhancement of wheat through the combined application of bio-fertilizer and chemical fertilizers including Phosphorus (P), Nitrogen (N), Potassium (K), Boric Acid (B), and Zinc Sulphate (Zn). The drone multispectral & optical data along with the Landsat-8 OLI data are collected, where various vegetation indices are computed to deeply analyze crop growth. Google Earth data is also used to estimate crop height, which gives important insights into the crop’s health. To validate the experimental results, several yield agronomic traits are recorded, such as plant height, grains/spike, number of tillers$/ \mathrm{m}^{2}$, grain weight, straw yield, and grain yield per hectare. Moreover, the percentage concentration of N, P, and K in grain and straw are estimated using atomic absorption spectroscopy. The wheat grain yield is recorded as 4.3 tonnes ha $^{-1}$ by the INM approach, which is 26% more than the farmer’s practice. This study shows that INM has great potential for supplying plant nutrients and making wheat more productive in a sustainable way.

关键词： Earth Productivity Crops Vegetation mapping Internet Optical sensors Remote sensing

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A Novel Optimized Deep Learning Model for Canola Crop Yield Prediction on Edge Devices

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 436-444页

作者： Valarezo-Plaza, Stephany Torres-Tello, Julio Singh, Keshav Shirtliffe, Steve Deivalakshmi, S. Ko, Seok-Bum University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSKS7N 5A9 Canada Agriculture and Agri Food Canada in Lethbridge Lethbridge Research and Development Centre LethbridgeABT1J 4B1 Canada University of Saskatchewan Department of Plant Sciences SaskatoonSKS7N 5A9 Canada National Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Tiruchirappalli620015 India

The escalating global demand for food, coupled with challenges in sustaining crop production, deteriorating ocean health, and depleting natural resources, underscores the critical role of agricultural technology. This article addresses the imperative of developing an optimal deep-learning model for predicting canola crop yield using hyperspectral images captured by drone flights. Our primary objective is to identify the most efficient model in terms of performance and size, considering the storage limitations on edge devices like Raspberry Pi 4 (RPi4). We start with the baseline 1D_CNN model, which achieves an R2 score of 0.82, and compress it into the proposed fs_model (fp32). To achieve the compression, we apply pruning through sparsity and feature selection using SHAP values. Further reduction in model size is accomplished by quantizing the weights of the proposed model to a lower precision, such as int16. This combined approach substantially decreases the proposed model's size by approximately 92.6% and inference time by approximately ×9013 in comparison to the baseline 1D_CNN model. In addition, we propose the novel fsp_model posit(8,3) that uses posit quantization to further reduce the computation requirements compared to the proposed fs_model (int16). Our findings indicate that the utilization of posit numbers enables us to shrink the model size to 94% of the original base model, while only reducing the R2 score by 5.7%. © 2024 IEEE.

关键词： Hyperspectral imaging

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Joint Radar-Communication Systems by Optimizing Radar Performance and Quality of Service for Communication Users

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar Systems 2024年 2卷 778-790页

作者： Tsinos, Christos G. Arora, Aakash Tsiftsis, Theodoros A. National and Kapodistrian University of Athens Department of Digital Industry Technologies Evripus Campus Euboea 34400 Greece University of Twente Radio Systems Group Faculty of Electrical Engineering Mathematics and Computer Science AE Enschede7500 Netherlands Indian Institute of Technology Delhi Centre for Applied Research in Electronics New Delhi110016 India University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece Jinan University Zhuhai Campus School of Intelligent Systems Science and Engineering Zhuhai519070 China

In this article, the problem of linear precoding and radar receive beamforming design for joint radar-communication (JRC) systems is studied. A multiple antenna base station (BS) that serves multiple single-antenna user terminals on the downlink is assumed. Furthermore, the BS employs a simultaneous radar function in the form of point-like target detection from the reflected return signals in a signal-dependent interference environment. In this work, we jointly design the JRC linear precoder and the radar receive beamformer, thus aiming to optimize the performance of the radar part while maintaining a desired quality of service (QoS) for the communication one subject to a total transmit power constraint. To that end, we formulate a challenging fractional nonconvex optimization problem via which the optimal precoder and radar receive beamformer are derived. Then, we develop algorithmic solutions based on the majorization-minimization (MM) principle and the semidefinite relaxation (SDR) methodology for the formulated optimization problem. The performance of both the proposed solutions is examined and compared to the one of a system that supports only the radar functionality via numerical results. © 2023 IEEE.

关键词： Signal to noise ratio

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Real-time Sensing and NeuroFeedback for Practicing Meditation Using simultaneous EEG and Eye Tracking

Real-time Sensing and NeuroFeedback for Practicing Meditatio...

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2022 IEEE Region 10 Symposium, TENSYMP 2022

作者： Pandey, Pankaj Gupta, Pragati Chaudhary, Shivam Miyapuram, Krishna Prasad Lomas, Derek Indian Institute of Technology Gandhinagar Computer Sciene and Engineering India National Forensic Sciences University Behavioral Sciences Gandhinagar India Centre for Cognitive and Brain Sciences Indian Institute of Technology Gandhinagar Gandhinagar India Delft University of Technology Vibe Research Lab Netherlands

ISBN: (数字)9781665466585

ISBN: (纸本)9781665466585

Next-generation intelligent health-monitoring systems have been ushered in by mobile phones with robust computation power, affordable high-resolution cameras, and embedded wearable sensors. Wearable Electroencephalography (EEG) headset to record brain signals with simultaneous eye tracking using a mobile front camera can become a powerful tool to modulate brain activity for self-enhancement. Meditation has been proved to have great effects in neuroscientific investigations for decades. However, a naive practitioner initiates the practice with high motivation and steps down after struggling to get the feedback or follow the instructions. EEG headset and Eye-tracking technology can help beginners get real-time audio and visual feedback and encourage those who fail to continue a regular meditation session. This article proposes real-time feedback framework for generating mindful moments and trace progress while practicing. Three major components are: Learning Phase, Meditation Style Specific Feedback and Evaluation Phase. We discuss three learning moments including preparatory, disentanglement and mindful. This study facilitates the design of a neurofeedback product that can offer tailored feedback. Neurotechnological revolution enables individuals to attain better equilibrium, sustained attention, meta-cognitive awareness, decreased mind-wandering, and enhanced emotional stability through various meditation practices. © 2022 IEEE.

关键词： Electroencephalography

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Multi-Spacecraft Pursuit-Evasion-Defense Strategy Based on Game Theory for On-Orbit Spacecraft Servicing

Multi-Spacecraft Pursuit-Evasion-Defense Strategy Based on G...

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IEEE Conference on Aerospace

作者： Xu Tang Dong Ye Kay-Soon Low Sha Luo Zhaowei Sun Research Center of Satellite Technology Harbin Institute of Technology Harbin China Satellite Technology and Research Centre National University of Singapore Singapore Electrical and Computer Engineering University of Canterbury Christchurch New Zealand

In the last decade, technologies such as spacecraft's navigation and guidance, rendezvous and docking have gradually matured. Moreover, research on spacecraft with autonomous maneuvering and intelligent decision-making capability for on-orbit servicing is also increasingly receiving more attention. With the rapid development of new space industry in particular large scale small satellite constellation, spacecraft that have the ability to avoid collisions, and on-orbit servicing spacecraft have become an important topic. Based on the game theory, this paper studies the process of terminal approach and rendezvous in the mission of spacecraft on-orbit servicing by taking terminal zero effort miss distance as the cost function. The established game control strategy is formulated differently from the traditional bilateral game as multiple spacecraft coexist in constellation have been taken into consideration. To reduce the control difficulty caused by the unknown terminal approaching time and the time to avoid collision, the time equation is established by calculating the corresponding time node to improve the designed game control strategies. Simulation study has been conducted and the results show the effectiveness of the proposed game control strategies.

关键词： Space vehicles Industries Simulation Small satellites Games Satellite navigation systems Cost function

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Synergistic effects of nano-enhanced waste transformer oil and hydrogen premixing on CI engine performance and emissions

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International Journal of Hydrogen Energy 2024年 56卷 484-497页

作者： Sathish, T. Karthikeyan, Sivakumar Sathyamurthy, Ravishankar Rajaram, K. Sandeep Kumar, S. Suresh Kumar, P. Giri, Jayant Amesho, Kassian T.T. Department of Mechanical Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai India Department of Mechanical Engineering SRM Valliammai Engineering College Tamil Nadu Chennai603203 India Department of Mechanical Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore641407 India Department of Mechanical Engineering University Centre for Research and Development Chandigarh University Punjab Mohali India Department of Physics Gandhigram Rural Institute Deemed to be University Tamil Nadu Dindigul624302 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh 522502 India Department of Mechanical Engineering R. V. R. and J.C. College of Engineering Andhra Pradesh Guntur522019 India Institute of Environmental Engineering National Sun Yat-Sen University Kaohsiung804 Taiwan Center for Emerging Contaminants Research National Sun Yat-Sen University Kaohsiung804 Taiwan Tshwane School for Business and Society Faculty of Management of Sciences Tshwane University of Technology Pretoria South Africa The International University of Management Centre for Environmental Studies Main Campus Dorado Park Ext 1 Windhoek Namibia Regent Business School Durban4001 South Africa Destinies Biomass Energy and Farming Pty Ltd SwakopmundP.O. Box 7387 Namibia Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India

Waste materials possess untapped potential when harnessed effectively. One such resource is waste transformer oil, derived from electrical transformers, which can be converted into a valuable fuel source for compression-ignition (CI) engines using pyrolysis techniques. In this study, we explored the utilization of a 50 % blend of waste transformer oil with diesel, augmented with Titanium dioxide nanoparticles (referred to as "nano fuel of waste transformer oil"), in an unmodified CI engine. To further enhance the performance of this nano fuel, we introduced hydrogen (at flow rates of 2.5 LPM, 5.0 LPM, and 7.5 LPM) into the inlet section during the suction phase, promoting premixing within the cylinder. Hydrogen premixing significantly improved combustion dynamics. Notably, when 7.5 LPM of hydrogen was introduced alongside waste transformer oil-derived nano fuel, a remarkable 37.30 % increase in brake thermal efficiency was achieved. Moreover, this configuration exhibited a 15.33 % reduction in brake-specific fuel consumption (BSFC), a 27.09 % decrease in carbon monoxide (CO) emissions, a 37.50 % decrease in unburned hydrocarbon (UHC) emissions, and a 28.53 % reduction in smoke opacity compared to traditional diesel operation. These results highlight the potential for enhanced CI engine performance and reduced emissions through the strategic combination of waste transformer oil-based nano-fuel and hydrogen premixing. Consequently, we recommend further exploration of hydrogen premixing in conjunction with nano-enhanced waste transformer oil for superior CI engine performance. © 2023 Hydrogen Energy Publications LLC

关键词： Insulating oil

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An automatic humor identification model with novel features from Berger's typology and ensemble models

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Decision Analytics Journal 2024年 11卷

作者： Mahajan, Rutal Zaveri, Mukesh Department of Computer Science and Engineering S.N. Patel Institute of Technology and Research Centre Gujarat Umrakh India Department of Computer Science and Engineering Sardar Vallabhbhai Patel National Institute of Technology Gujarat Surat India

Humor identification in text is essential for natural language understanding, impacting human–computer interactions and emotion recognition. This study introduces a novel approach to automatic humor identification in text using ensemble learning techniques. The objective is to classify text into two categories: humor and not humor. This research delves into humor detection in customer reviews, focusing on specific domains, and leverages diverse text features, including novel features derived from Berger's typology. State-of-the-art machine learning models, such as Support Vector Machine, Multilayer Perceptron, and Naïve Bayes, are employed. The study conducts an in-depth analysis of the Yelp review dataset, evaluating the performance of various ensemble learning methods. The proposed approach demonstrates exceptional results, achieving an 83.32% F-score with the Support Vector Machine classifier and an 85.68% accuracy using the stacking-based ensemble learning method with Random Forest on the Yelp Review dataset. Furthermore, this research includes an ablation study to assess the effectiveness of Berger's novel typology-based features in humor identification. This research contributes to the advancement of humor identification in text and highlights the effectiveness of ensemble learning techniques, especially when incorporating novel features inspired by Berger's typology. © 2024 The Author(s)

关键词： Berger's typology Ensemble learning Feature extraction Humor identification Incongruity features Machine learning

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