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StackTHP: A stacking ensemble model for accurate prediction of tumor-homing peptides in cancer therapy

computers in Biology and Medicine 2025年 189卷 109958-109958页

作者： Raihan, Fazla Rabby Abdulrazak, Lway Faisal Rahman, Md. Ashikur Ali, Md Mamun Ibrahim, Sobhy M. Ahmed, Kawsar Bui, Francis M. Mahmud, Imran Department of Software Engineering Daffodil International University Daffodil Smart City Birulia Dhaka1216 Bangladesh Electrical Engineering Technical College Middle Technical University Baghdad Iraq Department of Computer Science Cihan University Sulaimaniya Kurdistan Region Sulaimaniya46001 Iraq Division of Biomedical Engineering University of Saskatchewan 57 Campus Drive SaskatoonSKS7N 5A9 Canada Department of Biochemistry College of Science King Saud University P.O. Box: 2455 Riyadh11451 Saudi Arabia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive SaskatoonSKS7N 5A9 Canada Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City Birulia Dhaka1216 Bangladesh

The tumor-homing peptides (THPs) have emerged as one of the attractive resources for targeted cancer therapy, being able to bind and penetrate tumor cells selectively while ignoring adjacent healthy tissues. Therefore, the computational models to predict THPs became popular very rapidly, since laboratory methods are slow and resourceful. Herein, we are proposing StackTHP, a newly developed stacking-ensemble model aimed at further improving THP prediction accuracy. StackTHP implements multiple feature extraction methods, including amino acid composition (AAC), and pseudo amino acid composition (PAAC) together with classical machine learning classifiers like Extra Trees, Random Forest, and AdaBoost, while the logistic regression-based meta-classifier is used for the stacking framework. StackTHP outperformed all other models, producing an accuracy of 91.92 %, Matthew's correlation coefficient (MCC) of 0.8415, AUC of 0.977 on benchmark datasets, indicates that it is better than approaches attempted earlier and provides a robust solution for proceeding towards the discovery and development of peptide-based cancer therapies. Future research will focus on the application of StackTHP over more diverse sets of data along with some hybrid methods to enhance the prediction capability. The dataset and the code are available at the following link: https://***/Ashikur562/StackTHP. © 2025 The Authors

关键词： Prediction models

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The impact of vaccination on the infection rate and the severity of Covid-19

arXiv

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arXiv 2022年

作者： Rayhan, Md. Abdullah-Al-Wadud, M. Ahmed, M. Helal Uddin Department Of Computer Science And Engineering Brac University Dhaka Bangladesh Department Of Software Engineering College Of Computer And Information Sciences King Saud University Riyadh Saudi Arabia Department Of Management Information Systems University Of Dhaka Dhaka1000 Bangladesh

This study aims to statistically assess the effectiveness of vaccination against SARS-CoV-2. It is indispensable to investigate the relationship between Covid-19 deadliness and vaccination in order to study the impact of vaccine in real-world. We studied rates of infection and death due to Covid-19 in different countries with respect to their levels of vaccination. People who received the required dose of vaccination were considered as fully vaccinated in this study. Based on the percentage of fully vaccinated population, countries were categorized into several groups. Though a high-level study on the vaccine effectiveness may not provide much insight for individual level differences, a global analysis is imperative to infer the influence of vaccination as a controlling measure of the pandemic. © 2022, CC BY-NC-ND.

关键词： Vaccines

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Unsupervised Learning Method for the Wave Equation Based on Finite Difference Residual Constraints Loss

arXiv

引用

arXiv 2024年

作者： Feng, Xin Jiang, Yi Qin, Jia-Xian Zhang, Lai-Ping Deng, Xiao-Gang College of Computer Science Sichuan University Chengdu610065 China Tianfu Engineering-oriented Numerical Simulation & Software Innovation Center Sichuan University Chengdu610000 China PLA Beijing100082 China Institute of Defense Science and Technology Innovation Academy of Military Sciences Beijing100071 China

The wave equation is an important physical partial differential equation, and in recent years, deep learning has shown promise in accelerating or replacing traditional numerical methods for solving it. However, existing deep learning methods suffer from high data acquisition costs, low training efficiency, and insufficient generalization capability for boundary conditions. To address these issues, this paper proposes an unsupervised learning method for the wave equation based on finite difference residual constraints. We construct a novel finite difference residual constraint based on structured grids and finite difference methods, as well as an unsupervised training strategy, enabling convolutional neural networks to train without data and predict the forward propagation process of waves. Experimental results show that finite difference residual constraints have advantages over physics-informed neural networks (PINNs) type physical information constraints, such as easier fitting, lower computational costs, and stronger source term generalization capability, making our method more efficient in training and potent in application. Copyright © 2024, The Authors. All rights reserved.

关键词： Finite difference method

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Risk-Sensitive Reinforcement Learning with Exponential Criteria

arXiv

引用

arXiv 2022年

作者： Noorani, Erfaun Mavridis, Christos N. Baras, John S. Department of Electrical and Computer Engineering the Institute for Systems Research University of Maryland College Park United States Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

While reinforcement learning has shown experimental success in a number of applications, it is known to be sensitive to noise and perturbations in the parameters of the system, leading to high variability in the total reward amongst different episodes on slightly different environments. To introduce robustness, as well as sample efficiency, risk-sensitive reinforcement learning methods are being thoroughly studied. In this work, we provide a definition of robust reinforcement learning policies and formulate a risk-sensitive reinforcement learning problem to approximate them, by solving an optimization problem with respect to a modified objective based on exponential criteria. In particular, we study a model-free risk-sensitive variation of the widely-used Monte Carlo Policy Gradient algorithm, and introduce a novel risk-sensitive online Actor-Critic algorithm based on solving a multiplicative Bellman equation using stochastic approximation updates. Analytical results suggest that the use of exponential criteria generalizes commonly used ad-hoc regularization approaches, improves sample efficiency, and introduces robustness with respect to perturbations in the model parameters and the environment. The implementation, performance, and robustness properties of the proposed methods are evaluated in simulated experiments. © 2022, CC BY.

关键词： Contrastive Learning

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TPMARL-MSS Thought-Perception Multi-Agent Reinforcement Learning Meeting Scheduling System

TPMARL-MSS Thought-Perception Multi-Agent Reinforcement Lear...

引用

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Jinsong Zhang Xiaoxuan Mao Lu Li Hanqian Wu Joint graduate school Southeast University Nanjing China College of Software Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration of Ministry of Education School of Cyber Science and Engineering Southeast University Nanjing China

In today's evolving society, the increasing complexity and frequency of meetings necessitate advanced scheduling systems. Traditional methods are constrained by rigid prede-fined strategies, lack intelligent negotiation mechanisms, and often compromise user privacy. Addressing these challenges, we introduce the Thought-Perception Multi-Agent Reinforcement Learning Meeting Scheduling System (TPMARL-MSS). Unlike conventional systems, TPMARL-MSS autonomously learns and refines its strategies through continuous feedback. It features automated negotiation and adaptive decision-making, offering a more nuanced scheduling approach. Importantly, our Thought-Perception module protect privacy, allowing the agent to deduce preferences from participant behavior without revealing personal data. Evaluations on the real-world dataset shows that TPMARL-MSS surpasses traditional methods in efficiency and schedule quality, highlighting its practical applicability.

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Automatic removal algorithm of electrooculographic artifacts in non-invasive braincomputer interface based on independent component analysis

引用

Shengwu Yixue Gongchengxue Zazhi/Journal of Biomedical engineering 2022年第6期39卷 1074-1081页

作者： Song, Hao Xu, Song Liu, Guoming Liu, Jing Xiong, Peng Key Laboratory of Digital Medical Engineering of Hebei Province Hebei Baoding071002 China School of Quality and Technical Supervision Hebei University Hebei Baoding071002 China Beijing Aerospace Automatic Control Institute Beijing100854 China National Key Laboratory of Aerospace Intelligent Control Technology Beijing100854 China College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China College of Electronic and Information Engineering Hebei University Hebei Baoding071002 China

The non-invasive brain-computer interface (BCI) has gradually become a hot spot of current research, and it has been applied in many fields such as mental disorder detection and physiological monitoring. However, the electroencephalography (EEG) signals required by the non-invasive BCI can be easily contaminated by electrooculographic (EOG) artifacts, which seriously affects the analysis of EEG signals. Therefore, this paper proposed an improved independent component analysis method combined with a frequency filter, which automatically recognizes artifact components based on the correlation coefficient and kurtosis dual threshold. In this method, the frequency difference between EOG and EEG was used to remove the EOG information in the artifact component through frequency filter, so as to retain more EEG information. The experimental results on the public datasets and our laboratory data showed that the method in this paper could effectively improve the effect of EOG artifact removal and improve the loss of EEG information, which is helpful for the promotion of non-invasive BCI. © 2022 West China Hospital, Sichuan Institute of Biomedical engineering. All rights reserved.

关键词： Electroencephalography

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RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction

arXiv

引用

arXiv 2024年

作者： Li, Yushen Wang, Jinpeng Dai, Tao Zhu, Jieming Yuan, Jun Zhang, Rui Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Huawei Noah's Ark Lab Shenzhen China Shenzhen China Research Center of Artificial Intelligence Peng Cheng Laboratory Shenzhen China

Predicting click-through rates (CTR) is a fundamental task for Web applications, where a key issue is to devise effective models for feature interactions. Current methodologies predominantly concentrate on modeling feature interactions within an individual sample, while overlooking the potential cross-sample relationships that can serve as a reference context to enhance the prediction. To make up for such deficiency, this paper develops a RetrievalAugmented Transformer (RAT), aiming to acquire fine-grained feature interactions within and across samples. By retrieving similar samples, we construct augmented input for each target sample. We then build Transformer layers with cascaded attention to capture both intra- and cross-sample feature interactions, facilitating comprehensive reasoning for improved CTR prediction while retaining efficiency. Extensive experiments on real-world datasets substantiate the effectiveness of RAT and suggest its advantage in long-tail scenarios. The code has been open-sourced at https://***/YushenLi807/WWW24-RAT. © 2024, CC BY.

关键词： Forecasting

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Hierarchical Multi-Class Group Correlation Learning Network for Medical Image Segmentation

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4254-4267页

作者： Zixuan Wang Yuanzhi Cheng Xinghu Zhou Pengyong Yu Guohua Wang Shinichi Tamura School of Information Science and Technology Qingdao University of Science and Technology Qingdao China Qingdao Hiser Hospital Affiliated of Qingdao University Qingdao China College of Computer and Control Engineering Northeast Forestry University Harbin China Department of Radiology Graduate School of Medicine Osaka University Osaka Japan Rm I-271 ISIR Osaka University Osaka Japan

Hierarchical approaches have been tremendously successful at multi-label segmentation. However, it has been shown they may seriously suffer from the problem of only imposing constraints on shallow layers while ignoring deep relationships in the label space. In this paper we overcome this limitation through a hierarchical multi-class group correlation learning (HMGC). Thus, we first transform regional constraints into voxel vector correlations in a high-dimensional space. After performing transformation, we compute a voxel vector correlation matrix to group voxel vectors to reduce disparities between erroneous and valid vectors. We then introduce two loss functions: intra-class group loss, which minimizes differences within the same class, and inter-class group loss, which adjusts distances between class group centers and voxel vectors. This, in turn, can be used to mitigate bias propagation and improve segmentation accuracy. The effectiveness of our method is demonstrated on three Brain Tumor Segmentation Challenge datasets: BraTS2018, BraTS2019, and BraTS2020. Moreover, generalization of our method is evaluated on the ACDC MICCAI'17 Challenge Dataset. Our HMGC model ranks first in overall score on Brats2020 and achieves one of the most competitive results in cardiac segmentation.

关键词： Vectors Correlation Image segmentation Tumors Brain tumors Biomedical imaging Accuracy Refining Information science computer architecture

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ADAPTIVE LOCAL IMPLICIT IMAGE FUNCTION FOR ARBITRARY-SCALE SUPER-RESOLUTION

arXiv

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arXiv 2022年

作者： Li, Hongwei Dai, Tao Li, Yiming Zou, Xueyi Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University China College of Computer Science and Software Engineering Shenzhen University China Huawei Noah’s Ark Lab China

Image representation is critical for many visual tasks. Instead of representing images discretely with 2D arrays of pixels, a recent study, namely local implicit image function (LIIF), denotes images as a continuous function where pixel values are expansion by using the corresponding coordinates as inputs. Due to its continuous nature, LIIF can be adopted for arbitrary-scale image super-resolution tasks, resulting in a single effective and efficient model for various up-scaling factors. However, LIIF often suffers from structural distortions and ringing artifacts around edges, mostly because all pixels share the same model, thus ignoring the local properties of the image. In this paper, we propose a novel adaptive local image function (A-LIIF) to alleviate this problem. Specifically, our A-LIIF consists of two main components: an encoder and a expansion network. The former captures cross-scale image features, while the latter models the continuous up-scaling function by a weighted combination of multiple local implicit image functions. Accordingly, our A-LIIF can reconstruct the high-frequency textures and structures more accurately. Experiments on multiple benchmark datasets verify the effectiveness of our method. Our codes are available at https://***/LeeHW-THU/A-LIIF. © 2022, CC BY.

关键词： Pixels

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Securing Blockchain Using Propagation Chain Learning 5th

Securing Blockchain Using Propagation Chain Learning

引用

5th International Conference on Blockchain and Trustworthy Systems, BlockSys 2023

作者： Jin, Shifeng Zhai, Yijun Zhong, Yuxuan Cui, Jifu Xu, Lei Sun, Hongxia Lei, Yan School of Big Data and Software Engineering Chongqing University Chongqing400044 China College of Computer Science Chongqing University Chongqing400044 China School of Pre-school Education Chongqing College of International Business and Economics Chongqing401520 China Qingdao Penghai Software Co. Ltd. Qingdao266000 China Haier Smart Home Co. Ltd. Qingdao266000 China Qingdao Haidacheng Purchasing Service Co. Ltd. Qingdao266000 China

ISBN: (纸本)9789819981007

Smart contract vulnerabilities are the most common and severe type of blockchain vulnerability, which may result in very serious economic and property losses. Vulnerability detection and repair are necessary to ensure the security of the blockchain. Currently, the-state-of-art smart contract vulnerability detection methods (e.g. Oyente and Securify) use heuristics based on human-designed algorithms, which have certain shortcomings in different application scenarios. Therefore, this paper proposes a smart contract vulnerability detection method, i.e. CuVuD, which uses Propagation Chain Learning to solve the current vulnerability detection problem. This method first parses the source code, then obtains and trims the propagation chain of smart contracts, and finally detects vulnerabilities in smart contracts. To verify the effectiveness of CuVuD, this paper compares the CuVuD method with seven the-state-of-art smart contract vulnerability detection methods on a large-scale smart contract dataset based on the Solidity language. The experimental results show that CuVuD’s effectiveness in detecting smart contract vulnerabilities is significantly higher than seven the-state-of-art smart contract vulnerability detection methods, significantly improving the ability to detect vulnerabilities. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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