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Towards Precision Cancer Therapy: Prottrans and Multi-Window Scanning Convolutional Neural Networks Transforming Protein-Peptide Interaction Predictions

SSRN

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SSRN 2024年

作者： Van Le, The Zi Zhan, Jun Yu Ou, Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan

This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-view window scanning CNNs, our approach yields significant improvements, with ProtTrans standing out based on 2.1 billion protein sequences and 393 billion amino acids. The integrated model achieves an impressive AUC of 0.855 and 0.822 on Set_1 and Set_2 datasets, outperforming previous methods. Beyond this, we explore the application of this model in cancer therapy, particularly in identifying peptide interactions for selective targeting of cancer cells. The findings of this study contribute to bioinformatics, providing valuable insights for drug discovery and therapeutic development. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Deepepiil13: Deep Learning for Prediction of Il-13 Inducing Epitopes Using Pre-Trained Language Models and Multi-Window Convolutional Neural Networks

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SSRN 2024年

作者： Chuang, Cheng-Che Ou, Yu-Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan

Interleukin-13 (IL-13) is a key cytokine involved in allergic inflammation and the cytokine storm associated with severe COVID-19. Identifying antigenic epitopes capable of inducing IL-13 holds therapeutic potential for developing epitope-based vaccines and immunomodulatory treatments. However, conventional methods for epitope prediction are often inefficient and inaccurate. This study presents DeepEpiIL13, a novel deep learning framework that integrates pre-trained language models and multi-window convolutional neural networks (CNNs) to rapidly and accurately predict IL-13-inducing epitopes from protein sequences. The ProtTrans pre-trained language model was employed to generate high-dimensional embeddings that capture rich contextual information from protein sequences. These embeddings were then inputted into a multi-window CNN architecture, enabling the efficient exploration of local and global sequence patterns associated with IL-13 induction. The proposed approach was rigorously evaluated using benchmark datasets and an independent SARS-CoV-2 dataset. DeepEpiIL13 achieved superior performance compared to traditional methods, with an impressive Matthews correlation coefficient (MCC) of 0.52 and an area under the receiver operating characteristic curve (AUC) of 0.86 on the benchmark dataset. On the independent SARS-CoV-2 dataset, DeepEpiIL13 demonstrated remarkable robustness, attaining an MCC of 0.63 and an AUC of 0.92, highlighting its potential applicability in the context of COVID-19 research. This study introduces a powerful deep learning framework for accurate epitope prediction, offering new avenues for the development of epitope-based vaccines and immunotherapies targeting IL-13-mediated disorders. The successful identification of IL-13-inducing epitopes paves the way for novel therapeutic interventions against allergic diseases, inflammatory conditions, and potentially severe viral infections like COVID-19. © 2024, The Authors. All rights reserved.

关键词： COVID-19

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Beyond Known Reality: Exploiting Counterfactual Explanations for Medical Research

arXiv

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arXiv 2023年

作者： Tanyel, Toygar Ayvaz, Serkan Keserci, Bilgin Biomedical Engineering Graduate Program Istanbul Technical University Turkey Centre for Industrial Software Maersk Mc-kinney Moeller Institute University of Southern Denmark Denmark Department of Biomedical Engineering Yildiz Technical University Turkey

The field of explainability in artificial intelligence (AI) has witnessed a growing number of studies and increasing scholarly interest. However, the lack of human-friendly and individual interpretations in explaining the outcomes of machine learning algorithms has significantly hindered the acceptance of these methods by clinicians in their research and clinical practice. To address this issue, our study uses counterfactual explanations to explore the applicability of "what if?" scenarios in medical research. Our aim is to expand our understanding of magnetic resonance imaging (MRI) features used for diagnosing pediatric posterior fossa brain tumors beyond existing boundaries. In our case study, the proposed concept provides a novel way to examine alternative decision-making scenarios that offer personalized and context-specific insights, enabling the validation of predictions and clarification of variations under diverse circumstances. Additionally, we explore the potential use of counterfactuals for data augmentation and evaluate their feasibility as an alternative approach in our medical research case. The results demonstrate the promising potential of using counterfactual explanations to improve AI-driven methods in clinical research. © 2023, CC BY-NC-SA.

关键词： Pediatrics

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Enhancing the Performance of E-Mode AlGaN/GaN HEMTs With Recessed Gates Through Low-Damage Neutral Beam Etching and Post-Metallization Annealing

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 150-155页

作者： Chen, Yi-Ho Ohori, Daisuke Aslam, Muhammad Lee, Yao-Jen Li, Yiming Samukawa, Seiji National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Graduate Degree Program of College of Electrical and Computer Engineering Hsinchu300093 Taiwan Tohoku University Institute of Fluid Science Sendai980-8577 Japan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Pioneer Semiconductor Innovation Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Hsinchu300093 Taiwan

This study investigated the electrical properties of AlGaN/GaN high-electron-mobility transistors (HEMTs) with varied recess depths under the gate electrode. We demonstrated a recess depth of approximately 6 nm, which was achieved through neutral beam etching (NBE) technique with a low etch rate of 1.8 nm/min, resulting in device enhancement-mode (E-mode) behavior with threshold voltage (Vth) of 0.49 V. The effects of post-metallization annealing (PMA) on the device performance were also examined. The results revealed that PMA treatment improves the DC characteristics of the devices, including maximum drain current (IDMAX), transconductance (gm), subthreshold swing (SS), on-off ratio, and off-state leakage current, with maximum enhancement percentage of 18.3% for IDMAX, 3758% for on-off ratio, and 54.3% for SS. Moreover, this study compared the recess depths of metal-insulator-semiconductor high-electron-mobility transistors (MIS-HEMTs) with the SiN dielectric layer. The results showed that MIS-HEMTs exhibit more negative Vth values, which can be attributed to the controlled surface states achieved through passivation. © 2020 IEEE.

关键词： Etching

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A Transparent Ultrasound Array for Real-Time Optical,Ultrasound,and Photoacoustic Imaging

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biomedical engineering Frontiers 2022年第1期3卷 284-297页

作者： Haoyang Chen Sumit Agrawal Mohamed Osman Josiah Minotto Shubham Mirg Jinyun Liu Ajay Dangi Quyen Tran Thomas Jackson Sri-Rajasekhar Kothapalli Department of Biomedical Engineering The Pennsylvania State UniversityUniversity ParkPA 16802USA School of Electrical Engineering and Computer Science The Pennsylvania State UniversityUniversity ParkPA 16802USA Penn State Cancer Institute The Pennsylvania State UniversityHersheyPA 17033USA Graduate Program in Acoustics The Pennsylvania State UniversityUniversity ParkPA 16802USA

Objective and Impact *** imaging of ultrasound and optical contrasts can help map structural,functional,and molecular biomarkers inside living subjects with high spatial *** is a need to develop a platform to facilitate this multimodal imaging capability to improve diagnostic sensitivity and ***,combining ultrasound,photoacoustic,and optical imaging modalities is challenging because conventional ultrasound transducer arrays are optically *** a result,complex geometries are used to coalign both optical and ultrasound waves in the same field of *** elegant solution is to make the ultrasound transducer transparent to ***,we demonstrate a novel transparent ultrasound transducer(TUT)linear array fabricated using a transparent lithium niobate piezoelectric material for real-time multimodal *** TUT-array consists of 64 elements and centered at~6 MHz *** demonstrate a quad-mode ultrasound,Doppler ultrasound,photoacoustic,and fluorescence imaging in real-time using the TUT-array directly coupled to the tissue mimicking *** TUT-array successfully showed a multimodal imaging capability and has potential applications in diagnosing cancer,neurological,and vascular diseases,including image-guided endoscopy and wearable imaging.

关键词： Ultrasound specificity transparent

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Constructing a Segmentation Model for Patients with Leukoaraiosis Using Deep Learning Algorithms 24

Constructing a Segmentation Model for Patients with Leukoara...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Lo, Li-Ting Tsai, Yi-Chia Chang, Yi-Jou Ting, Hsien-Wei Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Many patients with leukoaraiosis (LA) exhibit mild and difficult-to-detect symptoms in the early stages, and due to the lack of effective detection methods, the optimal timing for treatment is often missed. This not only makes later treatment more difficult but, in severe cases, can even lead to mild to severe dementia. Therefore, in addition to relying on radiologists to manually interpret magnetic resonance imaging (MRI) scans, finding more effective detection methods has become extremely urgent. This study aimed to combine image processing and deep learning technologies to develop a model capable of accurately identifying and segmenting white matter hyperintensities (WMHs) to assist doctors in the early diagnosis of LA. We first performed efficient preprocessing on MR image slices from two different series and used the ResNet18 model to identify key slices containing WMHs, ultimately achieving precise segmentation of WMH areas with the U-Net model. This research focused on developing innovative preprocessing techniques, improving the accuracy by 12.3% with a limited dataset through preprocessing. Ultimately, the ResNet18 model achieved an F1 score of 0.829, while the U-Net model’s intersection over union (IoU) and Dice coefficient reached 0.750 and 0.857, respectively, showing the potential of this architecture. We propose improvements in the final chapter to construct a more comprehensive model. © 2024 Copyright ACM.

关键词： Deep learning

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Constructing an Automated Method for Interpreting Alzheimer’s Disease Based on Deep Learning Models and Image Processing 24

Constructing an Automated Method for Interpreting Alzheimer...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Yi, Chia-Pin Ting, Hsien-Wei Yang, Tzu-Fang Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Alzheimer’s disease (AD) often presents only mild symptoms in its early stages, and as there is no direct diagnostic method currently available, many patients are diagnosed only after the condition has worsened. Considering that magnetic resonance imaging (MRI) can provide key information in the diagnosis of AD, but the condition may go undiagnosed under manual interpretation due to the subtleness of symptoms and the reliance on radiologists to interpret MRI images, this study aimed to develop an automated method for interpreting brain MR images, hoping to screen for potential AD patients. By combining image processing, the U-Net model, YOLOv8 model, various CNN model architectures, and ensemble data statistics, our method was able to identify features related to AD and predict whether a patient has the disease. Our method can process a complete set of MR images and automatically extract important slices for prediction, making it practical for medical applications. Furthermore, we tested the accuracy and reliability of the method with additional datasets. © 2024 Copyright ACM.

关键词： Magnetic resonance imaging

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Recent advances in electronic devices for monitoring and modulation of brain

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Nano Research 2021年第9期14卷 3070-3095页

作者： Yong Won Kwon Yoon Sun Jun Young-Geun Park Jiuk Jang Jang-Ung Park Nano Science Technology Institute Department of Materials Science and EngineeringYonsei UniversitySeoul 03722Republic of Korea Center for Nanomedicine Institute for Basic Science(IBS)Seoul 03722Republic of Korea Graduate Program of Nano Biomedical Engineering(NanoBME) Advanced Science InstituteYonsei UniversitySeoul 03722Republic of Korea

The brain is actuated by billions of neurons with trillions of interconnections that regulate human *** the mechanisms of these systems that induce sensory reactions and respond to disease remains one of the greatest challenges in science,engineering,and *** advances in nanomaterials and nanotechnologies have led to the extensive research of electronic devices for brain interfaces to better understand the neural activities of the brains complex nervous *** development of sensor devices for monitoring the physiological signals of the brain related to traumatic injury status has accompanied by the progress of electronic neural probes in *** addition,these neurological and stereotactic surgical revolutions hold immense potential for clinical analysis of pharmacological systems within cerebral ***,we review the progress of electronic devices interfacing with brain in terms of the materials,fabrication technologies,and device *** activity can be measured and modulated by brain probes based on newly developed nanofabrication ***,in vivo pathological monitoring of the brain and pharmacological assessment has been developed in miniaturized and wireless *** also consider the key challenges and prospects for further development,and explore the future directions emerging in the latest research.

关键词： neural interfaces neural recording neuromodulation intracranial pressure monitoring microfluidic drug delivery

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Assessment of Simple Adhesive Substrates for Quantifying Microplastics

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SSRN 2022年

作者： Rieland, Julie M. Hu, Zeyuan Deese, Julian S. Love, Brian J. The University of Michigan United States Department of Materials Science and Engineering Department of Biomedical Engineering Macromolecular Science and Engineering Graduate Program

Microplastics are a growing environmental concern, with a large body of evidence documenting substantial distribution of plastic material in virtually all environmental compartments. Countermeasures that help to bind, aggregate, or coalesce these distributed particles might result in lower human and animal exposures. Pressure sensitive adhesives (PSAs) have been identified as a viable microplastic (MP) capture mechanism with a range of potential use conditions. As with any countermeasure, there is a need to evaluate potential solutions in terms of collection efficiency, cost, ease of installation, etc. Expanding on our previous work, spray-coated PSAs were investigated as submerged surfaces as tools to assess MP binding in aqueous dispersions. Resins containing two differing molecular weights of poly(2-ethylhexyl acrylate) PSA (92k and 950k), and a 50:50 by weight mixture of the two resins were applied as spray-coated substrates to compare the effect of resin composition on MP-adhesive binding. Thin films of PSA (92k 6 ± 1 μm, 950k 4 ± 2 μm, 50:50 BD 6.5 ± 1 μm) were sprayed on borosilicate glass slides using a commercial air brush. Polydisperse nylon-12 particles varying in size from 15-30 µm in diameter were dispersed in deionized water at concentrations between 0.01 and 5 mg mL -1 and agitated under ambient conditions to assess adhesive binding as quantitative comparisons of microparticle capture. Mixed assays were also performed comparing binding of common MP species including polyethylene (50, 200 µm), polystyrene (10 µm), and polyester fibers (1000 µm) to understand how varying composition, size, and form factor affect adsorption. The glass slide method showed increasing linear trends of particle binding with increased adhesive exposure time and MP concentration. The lower molecular weight 92K adhesive had lower viscosity, higher wettability for microplastics but also led to a less stable adhesive film complicating analysis. As a field-deployable scheme for mi

关键词： Aggregates

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First Results on Graph Similarity Search in Resting-State Functional Connectivity Networks Using Spectral and Graph Edit Distances

First Results on Graph Similarity Search in Resting-State Fu...

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18th International Joint Conference on biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Carvalho, M.A.G. Frayne, R. School of Technology University of Campinas Brazil Radiology and Clinical Neurosciences Hotchkiss Brain Institute Univ. of Calgary Canada Seaman Family MR Research Centre Foothills Medical Centre Calgary Canada Biomedical Engineering Graduate Program University of Calgary Canada Calgary Image Processing and Analysis Centre Foothills Medical Centre Calgary Canada

The application of graph theory in the modeling and analysis of brain networks has generated both new op portunities as well as new challenges in neuroscience. Resting state functional connectivity (RSFC) networks studied with graphs is an important field of investigation because of the potential benefits in understanding function in healthy individuals and identifying evidence of brain diseases and injury in patients. This work is unique because it applies information retrieval techniques to create ranked lists from RSFC graph theory derived networks. In our analysis, we used a sample of whole-brain resting-state functional magnetic reso nance imaging (rs-fMRI) data obtained from Young (n = 10, age: 20.1±2.1) and Old (n = 10, 65.6±0.4) sex-balanced groups drawn from a healthy, i.e., neurotypical, cohort. We estimated two well-known distance metrics (graph edit distance and graph spectral distance) and by using information-retrieval graph ranking methods achieved precision measures at the top-5 positions of ranked lists of up to 80%. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Graph neural networks

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