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Using a hybrid neural network architecture for DNA sequence representation: A study on N4-methylcytosine sites

Computers in Biology and Medicine 2024年 178卷 108664-108664页

作者： Nguyen, Van-Nui Ho, Trang-Thi Doan, Thu-Dung Le, Nguyen Quoc Khanh University of Information and Communication Technology Thai Nguyen University Thai Nguyen Viet Nam Department of Computer Science and Information Engineering TamKang University New Taipei251301 Taiwan International Degree Program in Animal Vaccine Technology International College National Pingtung University of Science and Technology Pingtung Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei110 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei110 Taiwan AIBioMed Research Group Taipei Medical University Taipei110 Taiwan Translational Imaging Research Center Taipei Medical University Hospital Taipei110 Taiwan

N4-methylcytosine (4mC) is a modified form of cytosine found in DNA, contributing to epigenetic regulation. It exists in various genomes, including the Rosaceae family encompassing significant fruit crops like apples, cherries, and roses. Previous investigations have examined the distribution and functional implications of 4mC sites within the Rosaceae genome, focusing on their potential roles in gene expression regulation, environmental adaptation, and evolution. This research aims to improve the accuracy of predicting 4mC sites within the genome of Fragaria vesca, a Rosaceae plant species. Building upon the original 4mc-w2vec method, which combines word embedding processing and a convolutional neural network (CNN), we have incorporated additional feature encoding techniques and leveraged pre-trained natural language processing (NLP) models with different deep learning architectures including different forms of CNN, recurrent neural networks (RNN) and long short-term memory (LSTM). Our assessments have shown that the best model is derived from a CNN model using fastText encoding. This model demonstrates enhanced performance, achieving a sensitivity of 0.909, specificity of 0.77, and accuracy of 0.879 on an independent dataset. Furthermore, our model surpasses previously published works on the same dataset, thus showcasing its superior predictive capabilities. © 2024 Elsevier Ltd

关键词： Embeddings

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Enhancing Protein Sequence Classification with a Fuzzy Neural Network: A Study in Anticancer Peptide Identification

Enhancing Protein Sequence Classification with a Fuzzy Neura...

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International Conference on Fuzzy Theory and it's Applications (iFUZZY)

作者： Nguyen Quoc Khanh Le Van-Nui Nguyen Thi-Tuyen Nguyen Thi-Xuan Tran Trang-Thi Ho Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan University of Information and Communication Technology Thai Nguyen University Thai Nguyen Vietnam University of Economics and Business Administration Thai Nguyen University Thai Nguyen Vietnam Department of Computer Science and Information Engineering TamKang University New Taipei Taiwan

ISBN: (数字)9798350352788

ISBN: (纸本)9798350352795

In bioinformatics, classifying protein sequences into anticancer peptides (ACPs) and non-ACPs is crucial yet challenging due to the inherent uncertainties of biological data. This study introduces a novel fuzzy neural network (FNN) model that integrates fuzzy logic within neural network architectures, enhancing the handling of ambiguity and improving classification accuracy. Our model, tested against several conventional machine learning models and recent studies, demonstrated superior specificity (83.28%) and overall accuracy (79.14%), marking a significant advancement in the identification of therapeutically relevant peptides. The integration of fuzzy logic not only optimized the performance but also increased the interpretability of the results, making it a valuable tool for complex bioinformatic analyses. These findings underscore the potential of fuzzy systems to refine predictive capabilities in computational biology, aligning perfectly with the themes of enhancing fuzzy theory applications in practical and impactful ways.

关键词： Fuzzy logic Uncertainty Accuracy Biological system modeling Peptides Machine learning Fuzzy neural networks

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Prediction and analysis of atmospheric visibility in five terrain types with artificial intelligence

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Heliyon 2023年第8期9卷 e19281页

作者： Liang, Chen-Wei Chang, Chia-Chun Hsiao, Chun-Yun Liang, Chen-Jui Department of Biomechatronic Engineering National Ilan University Yilan Taiwan Master Program in UAV Application and Smart Agriculture National Ilan University Yilan Taiwan Department of Environmental Engineering and Science Feng Chia University Taichung Taiwan International School of Technology and Management Feng Chia University Taichung Taiwan Artificial Intelligence Technology and Application Bachelor Program Feng Chia University Taichung Taiwan

Scattering visiometers are widely used to measure atmospheric visibility;however, visibility is difficult to measure accurately because the extinction coefficient decays exponentially with visual range according to the Koschmid's law. Moreover, models for predicting visibility are lacking due to the lack of accurate visibility observations to verify. This study formulated an artificial intelligence method for measuring atmospheric visibility in five topographical regions: hills, basins, plains, alluvial plains, and rift valleys. Four air pollution factors and five meteorological factors were selected as independent variables for predicting visibility by using three artificial intelligence models, namely a support vector machine (SVM) model, a multilayer perceptron (MLP) model, and an extreme gradient boosting (XGBoost) model. The GridSearchCV function was used to automatically tune model hyperparameters to determine the optimal parameter values of the three models for the five target areas. The predictions of the aforementioned three models underwent considerable considerably scale shrinking relative to observed values. The inappropriately low predicted visibility values might have been caused by the use of inaccurate observations for training. To solve this problem, formulas of scale ratio and downshift were used to adjust the predicted values. Statistical measurements of model performance measures by five quantitative methods (e.g., correlation coefficient, mean absolute error) showed that adjusted predictions were in strong agreement with the observation data for the five target areas. Therefore, the adjusted prediction has high reliability. Because of obvious differences in the topography, weather, and air quality of the five target areas, different models provided optimal predictions for different areas. In densely populated western Taiwan, the MLP model is most suitable for predicting visibility on hills whereas the XGBoost model is most suitable for predictin

关键词： Air pollutants Extinction coefficient Fog-haze Koschmieder's law Machine learning Terrain effects

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with artificial intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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Hybrid ACO and PSO to minimize makespan in job shop scheduling problems

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AIP Conference Proceedings 2024年第1期3222卷

作者： Priljen Habeahan Erna Budhiarti Nababan Mahyuddin K. M. Nasution Informatics Engineering Master Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Data Science and Artificial Intelligence Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

The lowest time search in the dataset that E. Taillard utilized employs a heuristic approach based on tabu search techniques to get the predicted solution. Glover’s study gives a broad description of tabu search, which is commonly encountered in Taillard’s job shop scheduling difficulties and Widmer et al.’s flow shop sorting challenges. Although tabu search is relatively simple to use and typically yields excellent results, it takes a long time to complete. In this research a hybrid ACO and PSO was carried out to minimize makespan in the Job Shop Scheduling Problem which was used as sourced from benchmark data which is secondary data obtained from E. Taillard “Benchmarks for basic scheduling problems” which consists of job shop matrix data (job × machine) measuring 4 × 4, 5 × 5, 7 × 7, 10 × 10, 15 × 15, 20 × 20, 30 × 15, 30 × 20, 50 × 15 and 50 × 20. Hybrid is carried out by calculating the Pbest value, namely the process position of each job on the machine to get the best solution using the PSO algorithm. Next, calculate the Gbest (Global best) value for the position of each job on the best machine on the entire machine using the PSO algorithm and initialize the ACO parameters using the PBest and Gbest values. The results of research on datasets with sizes 10×10, 15×15, 20×20, 30×15, 30×20, 50×15 and 50×20 produce smaller makespan compared to the lower bound on the dataset with an average minimum makespan improvement value of 1.184.

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Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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Science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

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Reconstructing Suspended Sediment Concentrations in the Mekong River Basin Via Semi-Supervised-Based Deep Neural Networks

SSRN

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SSRN 2024年

作者： Nguyen, Thi-Thu-Ha Vu, Duc Quang Doan, Ngoc Phu Thi Khanh Chi, Huynh Li, Peixin Van Binh, Doan An, Yimeng Tuan Dung, Pham Hoang, Tuan A. Thai Son, Mai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City70000 Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City70000 Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat Town820000 Viet Nam

The Mekong River Basin (MRB) is one of the highest biodiversity areas in the world and is the vital source of livelihoods for more than 60 millions of people over six countries in Southeast Asia. However, it is suffering from a severe sediment starvation problem caused by climate change and human activities, particularly dam constructions. Understanding long-term sediment changes thus plays a crucial role for management and contingency plans. Current sediment concentration data in the MRB are extremely sporadic, making it a non-trivial task to analyze them. This paper focuses on the sediment reconstruction to provide long-term reliable suspended sediment concentration (SSC) data using Machine Learning (ML) perspectives. In particular, a novel semi-supervised ML model, which can effectively deal with data sparsity, is introduced to predict/reconstruct missing sediment data. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the performances of the proposed method compared to other state-of-the-art supervised techniques and traditional simulation methods commonly used in previous sediment forecasting/reconstruction studies. To the best of our knowledge, this approach is the first semi-supervised method to reconstruct sediment data in the field. Though this study focuses on the MRB, sediment sparsity is a very common problem in other river systems worldwide. Thus, this method can be widely applied for them for more effective sediment reconstruction/prediction in general. © 2024, The Authors. All rights reserved.

关键词： Biodiversity

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Prediction of anterior cruciate ligament injury from MRI using deep learning

Prediction of anterior cruciate ligament injury from MRI usi...

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International Forum on Medical Imaging in Asia 2021, IFMIA 2021

作者： Truong, Nguyen Khanh Hung Nguyen, Thuan Phuoc Kha, Quang Hien Le, Ngoc Hoang Le, Van Tuan Cao, Thi Luu, Ho Thanh Lam Le, Nguyen Quoc Khanh Kang, Jiunn-Horng International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Orthopedic and Trauma Cho Ray Hospital Ho Chi Minh city Viet Nam Mechatronics Engineering Ho Chi Minh City University of Technology Ho Chi Minh city Viet Nam Graduate Institute of Biomedical Materials and Tissue Engineering College of Biomedical Engineering Taipei Medical University Taipei Taiwan Orthopedic and Rehabilitation Department University of Medicine and Pharmacy at Ho Chi Minh city Ho Chi Minh city Viet Nam Children's Hospital 2 Ho Chi Minh City Viet Nam Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan Department of Physical Medicine and Rehabilitation Taipei Medical University Hospital Taipei Taiwan Department of Physical Medicine and Rehabilitation School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

ISBN: (数字)9781510644212

ISBN: (纸本)9781510644205

Knee injury emerges as one of the most common diseases, causing dislocation of knee joints, immobility, etc., in which the anterior cruciate ligament (ACL) injury is the most common one. The development of various artificial intelligence (AI) frameworks gained enormous attention in many areas, including injury prediction and health management via medical image analysis. The objective of the current study is to focus on a comprehensive high accurate prediction of ACL injury based on MRI medical images, and also demonstrate the ability of AI in practical and outline conceptual prediction and diagnosis frameworks for other types of knee injuries in the future. Our dataset comprised of knee MRI reports from Cho Ray Hospital, Vietnam which are composed of ACL and non-ACL injury patients. The MRI images were used as supporting data in the deep learning classification model with DenseNet-121 algorithm. The successful establishment of an ACL injury diagnosis model from MRI will pave the way for us to develop more diagnostic models of other injuries in the body as well as the prediction of bone diseases. © 2021 SPIE.

关键词： Magnetic resonance imaging

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Radiomics in liver research: A paradigm shift in disease detection and staging

European Journal of Radiology Artificial Intelligence

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European Journal of Radiology artificial intelligence 2025年 2卷 100016-100016页

作者： Le, Minh Huu Nhat Kha, Hien Quang Tran, Nghia Minh Nguyen, Phat Ky Huynh, Han H. Huynh, Phat Kim Lam, Han Le, Nguyen Quoc Khanh International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei Taiwan AIBioMed Research Group Taipei Medical University Taipei 110 Taiwan Translational Science and Precision Medicine Talent Cultivation Program College of Medical Science and Technology Taipei Medical University Taipei 110 Taiwan International Master Program for Translational Science College of Medical Science and Technology Taipei Medical University Taipei 110 Taiwan Department of Industrial and Systems Engineering North Carolina A&T State University USA Industrial & Management Systems Engineering Department University of South Florida Tampa FL 33620 USA In-Service Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei 110 Taiwan Translational Imaging Research Center Taipei Medical University Hospital Taipei 110 Taiwan

AbstractThis narrative review explores radiomics in the broader context of liver disease research, an interdisciplinary field bridging medical imaging, oncology, and data science. It begins with an introduction to data analysis techniques, from foundational methods like descriptive statistics, comparative and regression analysis, and survival analysis, to advanced approaches like machine learning (ML), meta-analysis, and bioinformatics. Contextualizing these methodologies within liver disease research enhances our understanding of liver diseases. The focus is on radiomics, an emerging field, which is first placed in historical context before its applications are explained across fatty liver diseases (FLD), fibrosis, cirrhosis, and liver cancers. The role of radiomics in early detection, disease staging, and outcome prediction, particularly as a non-invasive alternative to liver biopsy, is analyzed in-depth. Current imaging modalities for liver cancer diagnosis are also examined, highlighting their advantages and limitations. Alongside radiomics, these imaging tools enable comprehensive liver assessment. Lastly, the integration of radiomics with other data types, especially genomic data, is explored, emphasizing its role in a holistic approach to liver disease management. The review concludes with a forward-looking perspective on future directions and advancements in radiomics and liver cancer research, addressing the challenges and opportunities in the field. It underscores the transformative impact of these developments on liver cancer research, patient care, personalized medicine, and clinical decision-making.

关键词： AFP alpha-fetoprotein AI artificial intelligence AST aspartate transaminase AUC area under the curve CCrCC clear cell renal cell carcinoma CEUS contrast-enhanced ultrasound CNNs convolutional neural networks DCNN deep convoluted neural network DOR diagnostic odds ratio FLD fatty liver disease GWAS genome-wide association studies HCC hepatocellular carcinoma MAFLD metabolic associated fatty liver disease ML machine learning MRE magnetic resonance elastography NAFLD nonalcoholic fatty liver disease NASH nonalcoholic steatohepatitis NPV negative predictive value PPV positive predictive value VCTE vibration-controlled transient elastography Radiomics Liver disease Cancer Non-invasive diagnostic techniques Presicion medicine Deep learning Machine learning

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Cold atmospheric plasma-enabled platelet vesicle incorporated iron oxide nano-propellers for thrombolysis

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Materials Today Bio 2023年 23卷 100876页

作者： Jheng, Pei-Ru Chiang, Chia-Che Kang, Jiunn-Horng Fan, Yu-Jui Wu, Kevin C.-W. Chen, Yan-Ting Liang, Jia-Wei Bolouki, Nima Lee, Jyh-Wei Hsieh, Jang-Hsing Chuang, Er-Yuan Graduate Institute of Nanomedicine and Medical Engineering Graduate Institute of Biomedical Materials and Tissue Engineering Institute of Biomedical Optomechatronics International Ph.D. Program in Biomedical Engineering School of Biomedical Engineering College of Biomedical Engineering Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Department of Physical Medicine and Rehabilitation Taipei Medical University Hospital Taipei Taiwan Institute of Biomedical Engineering & Nanomedicine National Health Research Institute Keyan Road Zhunan Miaoli City350 Taiwan Department of Chemical Engineering National Taiwan University No.1 Sec. 4 Roosevelt Rd Taipei10617 Taiwan Department of Plasma Physics and Technology Faculty of Science Masaryk University Brno Czech Republic Center for Plasma and Thin Film Technologies Ming Chi University of Technology New Taipei City Taiwan Department of Materials Engineering Ming Chi University of Technology New Taipei City Taiwan Cell Physiology and Molecular Image Research Center Taipei Medical University-Wan Fang Hospital Taipei11696 Taiwan

A new approach to treating vascular blockages has been developed to overcome the limitations of current thrombolytic therapies. This approach involves biosafety and multimodal plasma-derived theranostic platelet vesicle incorporating iron oxide constructed nano-propellers platformed technology that possesses fluorescent and magnetic features and manifold thrombus targeting modes. The platform is capable of being guided and visualized remotely to specifically target thrombi, and it can be activated using near-infrared phototherapy along with an actuated magnet for magnetotherapy. In a murine model of thrombus lesion, this proposed multimodal approach showed an approximately 80 % reduction in thrombus residues. Moreover, the new strategy not only improves thrombolysis but also boosts the rate of lysis, making it a promising candidate for time-sensitive thrombolytic therapy. © 2023 The Authors

关键词： Infrared devices

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