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AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains (vol 6, pg 147, 2023)

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ANTIBODY THERAPEUTICS 2023年第3期6卷 180-180页

作者： Zhou, Yuwei Huang, Ziru Gou, Yushu Liu, Siqi Yang, Wei Zhang, Hongyu Dzisoo, Anthony Mackitz Huang, Jian School of Life Science and Technology University of Electronic Science and Technology of China Sichuan Chengdu China School of Life Science and Technology University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Research and Development Zhanyuan Therapeutics Ltd. Zhejiang Hangzhou China Bioinformatics Data and Medical Reporting Arcencsus GmbH Mecklenburg-Vorpommern Rostock Germany University of Electronic Science and Technology of China No.2006 Xiyuan Ave West Hi-Tech Zone Chengdu China

Over 120 FDA-approved antibody-based therapeutics are used to treat a variety of ***, many candidates could fail because of unfavorable physicochemical properties. Light-chain amyloidosis is one form of aggregation that can lead to severe safety risks in clinical development. Therefore, screening candidates with a less amyloidosis risk at the early stage can not only save the time and cost of antibody development but also improve the safety of antibody drugs. In this study, based on the dipeptide composition of 742 amyloidogenic and 712 non-amyloidogenic antibody light chains, a support vector machine-based model, AB-Amy, was trained to predict the light-chain amyloidogenic risk. The AUC of AB-Amy reaches 0.9651. The excellent performance of AB-Amy indicates that it can be a useful tool for the in silico evaluation of the light-chain amyloidogenic risk to ensure the safety of antibody therapeutics under clinical development. A web server is freely available at http://***/AB-Amy/.

关键词： amyloidosis developability prediction support vector machine (SVM) therapeutic antibody

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ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus

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computers in Biology and Medicine 2025年 186卷 109708-109708页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Ejiyi, Makuachukwu Bennedith Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Onyekwere, Favour Ezinne Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan 610059 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Pharmacy Department University of Nigeria Nsukka Enugu State Nsukka Nigeria School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

In image segmentation for medical image analysis, effective upsampling is crucial for recovering spatial information lost during downsampling. This challenge becomes more pronounced when dealing with diverse medical image modalities, which can significantly impact model performance. Plain and standard skip connections, widely used in most models, often fall short of maintaining high segmentation accuracy across different modalities, because essential spatial information transferred from the encoder to the decoder is lost. Inspired by these limitations, the Attention-Embedded Deep UNet (ATEDU-Net) is presented here, an innovative framework designed and tested on diverse medical image modalities, including X-ray, breast ultrasound, and retinal fundus images. ATEDU-Net features a unique architecture that combines progressive context refinement modules (PCRM) and global context modules (GCM) within a U-shaped network structure with Convgroup blocks which facilitate the integration of convolutional operations. This allows ATEDU-Net to autonomously capture rich spatial details and crucial contextual information from input images. The GCM captures essential contextual information for informed decision-making, while the PCRM enhances feature attention, resulting in precise and robust segmentation. The experimentation and analysis demonstrate that ATEDU-Net promises to be a powerful tool for medical professionals, aiding in the early detection of chest-related diseases, accurate localization of breast tumors, and early identification of eye diseases. This, in turn, contributes significantly to the formulation of optimal therapeutic strategies, enhancing patient care and outcomes. The versatility of ATEDU-Net is further highlighted by its ability to analyze medical images across various modalities, making it well-suited for the complex task of medical image analysis in diverse clinical scenarios. © 2025 Elsevier Ltd

关键词： Image segmentation

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Environment-aware dynamic graph learning for out-of-distribution generalization 23

Environment-aware dynamic graph learning for out-of-distribu...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Haonan Yuan Qingyun Sun Xingcheng Fu Ziwei Zhang Cheng Ji Hao Peng Jianxin Li Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University and School of Computer Science and Engineering Beihang University Key Lab of Education Blockchain and Intelligent Technology Guangxi Normal University Department of Computer Science and Technology Tsinghua University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two major challenges: (1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? (2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatiotemporal invariant patterns. Specifically, we first design the environment-aware EADGNN to model environments by multi-channel environments disentangling. Then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.

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A Semiconductor Manufacturing Final Test Yield Classification Using Random Forest

A Semiconductor Manufacturing Final Test Yield Classificatio...

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data Science and Information System (ICDSIS), IEEE International Conference on

作者： R. Dineshkumar Srinivas Aluvala Suma Srinath Zaid Alsalami V T Krishnaprasath Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India School of Computer Science and Engineering SR University Warangal Telangana India Department of AIML Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Semi-conductor manufacturing companies are growing rapidly by forming collaborative design and wafer fabrication. The yield prediction techniques use production data and provide improved operational efficiency and decrease production costs. Random forest classification method is proposed for semiconductor manufacturing Final Test (FT) yield prediction. data pre-processing methods include Gaussian mixture Model, one hot encoder and Label encoder whereas improved grid search algorithm is used for feature selection. The selected features are then given to the implemented classification Random forest (RF) model. The RF method was evaluated by conducting experiments for three different products which are provided by Silicon Laboratories. The RF method outperformed in handling both numerical and categorical production data and is evaluated by the metrics such as accuracy, precision, recall and F-measure. The developed method achieved 0.985 of accuracy when compared with the existing methods such as LSTM-AM and SH-DNN.

关键词： Measurement Accuracy Laboratories Production Semiconductor device manufacture Feature extraction Prediction algorithms

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Emotional Dialogue Generation Based on Transformer and Conditional Variational Autoencoder

Emotional Dialogue Generation Based on Transformer and Condi...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Hongquan Lin Zhenrong Deng School of Data Science and Software Engineering Wuzhou University Wuzhou China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

Human-machine dialogue is one of the most challenging tasks in the field of natural language processing, and it is also the basis for the realization of a human-machine inclusive society in the future. At present, the generative dialogue model based on deep learning is prone to generate general responses with single content and no meaningful information, and the existing research considers emotional factors relatively little. Aiming at the shortcomings of existing methods, an emotional dialogue generation model based on Transformer and conditional variational autoencoder(CVAE) is proposed, which is intended to improve the diversity of response information and embed emotional factors in the generated response. This model uses Transformer to extract the semantic features of the text sequence to improve the utilization of the semantic information of the text sequence. To increase the diversity of the response information, the latent variable of the conditional variational self-encoder is introduced into the decoder. In addition, in order to enhance the empathy ability of the model, an emotion perception encoder is used to encode user emotion information, and a pre-trained emotion classification model based on BERT is proposed to detect the emotion information implicit in the utterance. Experiments have shown that the proposed model has a stronger generation ability, more diverse information to generate responses, and a stronger empathy ability.

关键词： Analytical models Computational modeling Semantics Bit error rate Transformers Feature extraction Ubiquitous computing

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CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

CroPrompt: Cross-task Interactive Prompting for Zero-shot Sp...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Qin, Libo Wei, Fuxuan Chen, Qiguang Zhou, Jingxuan Huang, Shijue Si, Jiasheng Lu, Wenpeng Che, Wanxiang School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

ISBN: (纸本)9798350368741

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU. © 2025 IEEE.

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Deep Learning Classification and Segmentation of Brain MRI Images

Deep Learning Classification and Segmentation of Brain MRI I...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： S Pooja Ahuja S R Likhith A Shiva Balan K Navya Department of CSE (Data Science) Nagarjuna College of Engineering and Technology Bengaluru India Department of School of Computer Science and Engineering and Information Science Bio-intelligence Lab Presidency University Bengaluru India Department of ISE Nagarjuna College of Engineering and Technology Bengaluru India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

Unique Clinical pictures have a basic impact on the specialist’s capability to the right conclusion and in the treatment of the patient. Diagnostic imaging can quickly identify lesions thanks to sophisticated algorithms, and it is essential to glean characteristics from photographs. In a number of studies, a number of algorithms have been incorporated into imaging in medicine. Using convolutional neural network, CNN’s fundamental architecture is constructed using picture component extraction. To be able to circumvent the limitations imposed by both machine vision and human vision, the study is extended to include CNN with multiple input channels for visual feature extraction. This study uses approximately 3300 MRI samples from Kaggle to investigate four classifications: tumors of the pituitary gland, meningioma, glioma, and none. The implemented BrainNet has a accuracy of 98.31 percent and a validation accuracy of 87.80 percent. In order to determine which strategy performed better, deep architectures like InceptionNet and ResNet were also put through tests with or without learning through transfer.

关键词： Visualization Image segmentation Magnetic resonance imaging Object segmentation Feature extraction Pituitary gland Convolutional neural networks

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A Comprehensive Survey on Magnetic Resonance Image Reconstruction

arXiv

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arXiv 2025年

作者： Kui, Xiaoyan Fan, Zijie Ji, Zexin Li, Qinsong Liu, Chengtao Si, Weixin Zou, Beiji School of Computer Science and Engineering Central South University Changsha410083 China Big Data Institute Central South University Hunan Changsha410083 China Shenzhen Institute of Advanced Technology Chinese Academy of Science Guangdong Shenzheng518055 China

Magnetic resonance imaging (MRI) reconstruction is a fundamental task aimed at recovering high-quality images from undersampled or low-quality MRI data. This process enhances diagnostic accuracy and optimizes clinical applications. In recent years, deep learning-based MRI reconstruction has made significant progress. Advancements include single-modality feature extraction using different network architectures, the integration of multimodal information, and the adoption of unsupervised or semi-supervised learning strategies. However, despite extensive research, MRI reconstruction remains a challenging problem that has yet to be fully resolved. This survey provides a systematic review of MRI reconstruction methods, covering key aspects such as data acquisition and preprocessing, publicly available datasets, single and multi-modal reconstruction models, training strategies, and evaluation metrics based on image reconstruction and downstream tasks. Additionally, we analyze the major challenges in this field and explore potential future directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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GANTEE: Generative Adversatial Network for Taxonomy Entering Evaluation

arXiv

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arXiv 2023年

作者： Gu, Zhouhong Jiang, Sihang Liu, Jingping Xiao, Yanghua Feng, Hongwei Li, Zhixu Liang, Jiaqing Zhong, Jian Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Information Science and Engineering East China University of Science and Technology China Fudan-Aishu Cognitive Intelligence Joint Research Center China School of Data Science Fudan University China HUAWEI CBG Edu AI Lab. China

Taxonomy is formulated as directed acyclic concepts graphs or trees that support many downstream tasks. Many new coming concepts need to be added to an existing taxonomy. The traditional taxonomy expansion task aims only at finding the best position for new coming concepts in the existing taxonomy. However, they have two drawbacks when being applied to the real-scenarios. The previous methods suffer from low-efficiency since they waste much time when most of the new coming concepts are indeed noisy concepts. They also suffer from low-effectiveness since they collect training samples only from the existing taxonomy, which limits the ability of the model to mine more hypernym-hyponym relationships among real concepts. This paper proposes a pluggable framework called Generative Adversarial Network for Taxonomy Entering Evaluation (GANTEE) to alleviate these drawbacks. A generative adversarial network is designed in this framework by discriminative models to alleviate the first drawback and the generative model to alleviate the second drawback. Two discriminators are used in GANTEE to provide long-term and short-term rewards, respectively. Moreover, to further improve the efficiency, pre-trained language models are used to retrieve the representation of the concepts quickly. The experiments on three real-world large-scale datasets with two different languages show that GANTEE improves the performance of the existing taxonomy expansion methods in both effectiveness and efficiency. © 2023, CC BY.

关键词： Taxonomies

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data-Driven Power Flow Estimation for MVDC Distribution Systems Based on Physics-Embedded FCN

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IEEE Transactions on Smart Grid 2025年

作者： Sun, Pingyang Wu, Rongcheng Shen, Zhiwei Wang, Hongyi Li, Gen Khalid, Muhammad Konstantinou, Georgios School of Electrical Engineering and Telecommunications UNSW Sydney Australia University of Technology Sydney Data Science Institute Australia Aalborg University Department of AAU energy Denmark Energy Technology and Computer Science Section Department of Engineering Technology Ballerup2750 Denmark King Fahd University of Petroleum and Minerals Electrical Engineering Department Interdisciplinary Research Center for Sustainable Energy Systems Dhahran31261 Saudi Arabia

Maintaining high prediction accuracy with varying grid topologies poses a significant challenge to adopting neural network (NN)-based approaches for power flow (PF) estimation in medium-voltage direct current (MVDC) distribution systems. This paper proposes a physics-embedded fully convolutional network (PEFCN) to improve the accuracy. Physics-embedded techniques are incorporated in the proposed PEFCN through 1) architecture reconstruction, and 2) loss function reformulation. Architecture is reconstructed by input channel conversion calculation in a new physics operation layer. This process offers physical connections among the three input matrix channels (voltage, current, and line conductance). Three new physics loss terms are included in the loss function to restrict the outliers violating the limits of converter power ratings and terminal MVDC voltages. The two operations enable the FCN to achieve improved prediction accuracy and strong generalization capabilities. Five MVDC distribution systems, characterized by different dc voltage levels and topology configurations, serve to validate the superiority of the proposed PEFCN over other NNs in the PF estimation for scenarios involving both fixed and varying system structures. Moreover, a modified IEEE 69-bus distribution system is further used to demonstrate the applicability of the proposed PEFCN for larger systems. © 2010-2012 IEEE.

关键词： DC distribution systems

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