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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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Balancing exploration and exploitation phases in whale optimization algorithm: an insightful and empirical analysis

arXiv

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arXiv 2023年

作者： Ahmed, Aram M. Rashid, Tarik A. Hassan, Bryar A. Majidpour, Jaffer Noori, Kaniaw A. Rahman, Chnoor Maheadeen Abdalla, Mohmad Hussein Qader, Shko M. Tayfor, Noor Mohammed, Naufel B. Department of Information Technology College of Science and Technology University of Human Development Sulaimani Iraq Department of Information Technology Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani46001 Iraq Department of Computer Science and Engineering School of Science and Engineering University of Kurdistan Hewler Erbil Iraq Department of Computer Science College of Science Charmo University Chamchamal Sulaimani46023 Iraq Department of Computer Science University of Raparin Sulaimani Iraq Database Technology Department Technical College of Informatics Sulaimani Polytechnic University Sulaimani Iraq Department of Computer Science Kurdistan Technical Institute Sulaimani Iraq

Agents of any metaheuristic algorithms are moving in two modes, namely exploration and exploitation. Obtaining robust results in any algorithm is strongly dependent on how to balance between these two modes. Whale optimization algorithm as a robust and well recognized metaheuristic algorithm in the literature, has proposed a novel scheme to achieve this balance. It has also shown superior results on a wide range of applications. Moreover, in the previous chapter, an equitable and fair performance evaluation of the algorithm was provided. However, to this point, only comparison of the final results is considered, which does not explain how these results are obtained. Therefore, this chapter attempts to empirically analyze the WOA algorithm in terms of the local and global search capabilities i.e. the ratio of exploration and exploitation phases. To achieve this objective, the dimension-wise diversity measurement is employed, which, at various stages of the optimization process, statistically evaluates the population's convergence and diversity. © 2023, CC BY.

关键词： Optimization

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Correction to: Concept Design Evaluation of Sustainable Product–Service Systems: A QFD–TOPSIS Integrated Framework with Basic Uncertain Linguistic Information

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Group Decision and Negotiation 2024年第3期33卷 513-514页

作者： Yang, Qiang Chen, Zhen-Song Zhu, Jiang-Hong Martínez, Luis Pedrycz, Witold Skibniewski, Mirosław J. School of Economics and Management Chongqing Jiaotong University Chongqing China Research Center of Integrated Customs-Port Logistics & Shipping Development Chongqing China School of Civil Engineering Wuhan University Wuhan China College of Traffic and Transportation Chongqing Jiaotong University Chongqing China Department of Computer Science University of Jaén Jaén Spain Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Civil and Environmental Engineering University of Maryland College Park USA Chaoyang University of Technology Taichung Taiwan Institute for Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland

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RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering

arXiv

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arXiv 2025年

作者： Liang, Sichu Zhang, Linhai Zhu, Hongyu Wang, Wenwen He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China Department of Informatics King’s College London United Kingdom School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China School of Electrical and Computer Engineering Carnegie Mellon University United States The Alan Turing Insitute United Kingdom

Medical question answering requires extensive access to specialized conceptual knowledge. The current paradigm, Retrieval-Augmented Generation (RAG), acquires expertise medical knowledge through large-scale corpus retrieval and uses this knowledge to guide a general-purpose large language model (LLM) for generating answers. However, existing retrieval approaches often overlook the importance of factual knowledge, which limits the relevance of retrieved conceptual knowledge and restricts its applicability in real-world scenarios, such as clinical decision-making based on Electronic Health Records (EHRs). This paper introduces RGAR, a recurrence generation-augmented retrieval framework that retrieves both relevant factual and conceptual knowledge from dual sources (i.e., EHRs and the corpus), allowing them to interact and refine each another. Through extensive evaluation across three factual-aware medical question answering benchmarks, RGAR establishes a new state-of-the-art performance among medical RAG systems. Notably, the Llama-3.1-8BInstruct model with RGAR surpasses the considerably larger, RAG-enhanced GPT-3.5. Our findings demonstrate the benefit of extracting factual knowledge for retrieval, which consistently yields improved generation quality. Copyright © 2025, The Authors. All rights reserved.

关键词： Question answering

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Embedding-aided network dismantling

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Physical Review Research 2023年第1期5卷 013076-013076页

作者： Saeed Osat Fragkiskos Papadopoulos Andreia Sofia Teixeira Filippo Radicchi Max Planck Institute for Dynamics and Self-Organization (MPIDS) 37077 Göttingen Germany Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 33 Saripolou Street 3036 Limassol Cyprus LASIGE Departamento de Informática Faculdade de Ciências Universidade de Lisboa 1749-016 Lisboa Portugal Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana 47408 USA

Optimal percolation concerns the identification of the minimum-cost strategy for the destruction of any extensive connected components in a network. Solutions of such a dismantling problem are important for the design of optimal strategies of disease containment based either on immunization or social distancing. Depending on the specific variant of the problem considered, network dismantling is performed via the removal of nodes or edges, and different cost functions are associated to the removal of these microscopic elements. In this paper, we show that network representations in geometric space can be used to solve several variants of the network dismantling problem in a coherent fashion. Once a network is embedded, dismantling is implemented using intuitive geometric strategies. We demonstrate that the approach well suits both Euclidean and hyperbolic network embeddings. Our systematic analysis on synthetic and real networks demonstrates that the performance of embedding-aided techniques is comparable to, if not better than, the one of the best dismantling algorithms currently available on the market.

关键词： Network phase transitions Robustness

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Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 83-109页

作者： Anbazhagu, U.V. Koti, Manjula Sanjay Muthukumaran, V. Geetha, V. Munrathnam, Meram Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of MCA Dayananda Sagar Academy of Technology and Management Bangalore560082 India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur Campus 603 203 India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.k.Valley Idupulapaya Andhra Pradesh Kadapa516330 India

The necessity for smart energy oversight solutions has arisen in response to the rising popularity of energy-efficient home automation and other energy-saving technologies. Optimizing smart home energy use using multi-criteria decision-making (MCDM) is a proven methodology. However, the procedure for making decisions and MCDM’s capacity to handle various criteria are typically limiting factors. Hybrid methods, which integrate multiple decision-making approaches like Fuzzy Logic (FL) and Modular Neural Networks (MNN), could potentially be able to circumvent these restrictions and boost energy management systems’ efficacy and precision. This investigation presents a hybrid Neuro-Fuzzy (H-NF) method for MCDM in regulating energy for smart homes by combining FL with an MNN. The suggested approach would optimize energy use in smart homes by considering several parameters, notably cost, ease of use, and environmental effects. In addition, this study aims to examine how the H-NF model fares in comparison to other methods of making important decisions in terms of several performance metrics. The suggested hybridized approach has the potential to deliver more precise and effective decision-making processes for energy management in smart homes, allowing users to optimize their energy consumption while preserving comfort and lowering environmental impact. © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Decision making

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Enhanced Electrochemical Response and Promising Anti-Cancer Potential of a Novel Azo Dye: From Sensor Technology to Bcl-Xl Inhibition

SSRN

引用

SSRN 2024年

作者： Harisha, S. Nagaraj, Kalyan Reddy, Mohan R. Sridhar, Amulyashree Kumar, S.R. Kiran Basavegowda, Nagaraj Centre for Nanosciences Department of Chemistry K.S. Institute of Technology Karnataka Bengaluru560 109 India B.M.S. College of Engineering Karnataka Bengaluru560019 India Department of Chemistry B.M.S. College of Engineering Karnataka Bengaluru560019 India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Karnataka Bengaluru560035 India Department of Biotechnology Yeungnam University Gyeongbuk Gyeongsan38451 Korea Republic of

We report the synthesis of heterocyclic azo dye 1,3-dimethyl-5-[(E)-(5-sulfanyl-1,3,4-thiadiazol-2-yl)diazenyl]pyrimidine-2,4,6(1H,3H,5H)-trione (i.e. DSTDPT). The structure of DSTDPT was characterized using UV-Visible, FTIR, 1HNMR, and LCMS analyses. Furthermore, electrochemical properties were examined in a 0.2 M phosphate buffer (pH=7.2) with a carbon paste electrode (CPE), which was in turn altered using surfactants CTAB, SDS, and Triton X-100 to boost sensitivity and selectivity metrics towards paracetamol detection. The nonlinear optical metrics and electronic structure of DSTDPT was investigated by performing density functional theory (DFT) computations using 6-31G (d, p)/B3LYP basis set. The compound revealed promising optical characteristics, with significant first-order hyperpolarizability, which was 19 times that of urea. In addition, molecular docking studies revealed favourable binding energies of DSTDPT towards B-cell lymphoma-extra-large (Bcl-xL) protein. To further validate the solidity and instabilities within Bcl-xL protein and Bcl-xL-DSTDPT complex, molecular dynamics (MD) simulations was performed. The outcomes established DSTDPT as a promising lead molecule with potent anti-cancer property. © 2024, The Authors. All rights reserved.

关键词： Molecular docking

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Validation and Refinement of Two Interpretable Models for Coronavirus Disease 2019 Prognosis Prediction

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World Journal of Traditional Chinese Medicine 2023年第2期9卷 191-200页

作者： Kai Chang Ting Jia Ya-Na Zhou Zi-Xin Shu Ji-Fen Liu Jing Sun Qi-Guang Zheng Hao-Yu Tian Jia-Nan Xia Kuo Yang Ning Wang Hai-Long Sun Xin-Yan Wang Deng-Ying Yan Taane G.Clark Bao-Yan Liu Xiao-Dong Li Yong-Hong Peng Xue-Zhong Zhou Institute of Medical Intelligence School of Computer and Information TechnologyBeijing Jiaotong UniversityBejingChina College of Information Engineering Hubei University of Chinese MedicineWuhanHubeiChina Department of Data Center Hubei Provincial Hospital of Traditional Chinese MedicineWuhanHubeiChina Department of Infection Biology London School of Hygiene and Tropical MedicineLondonEngland Department of Infectious Disease Epidemiology Faculty of Epidemiology and Population HealthLondon School of Hygiene and Tropical MedicineLondonEngland Data Center of Traditional Chinese Medicine China Academy of Chinese Medical SciencesBeijingChina Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterEngland

Objective:To validate two proposed coronavirus disease 2019(COVID-19)prognosis models,analyze the characteristics of different models,consider the performance of models in predicting different outcomes,and provide new insights into the development and use of artificial intelligence(AI)predictive models in clinical decision-making for COVID-19 and other *** and Methods:We compared two proposed prediction models for COVID-19 prognosis that use a decision tree and logistic regression *** evaluated the effectiveness of different model-building strategies using laboratory tests and/or clinical record data,their sensitivity and robustness to the timings of records used and the presence of missing data,and their predictive performance and capabilities in single-site and multicenter ***:The predictive accuracies of the two models after retraining were improved to 93.2% and 93.9%,compared with that of the models directly used,with accuracies of 84.3% and 87.9%,indicating that the prediction models could not be used directly and require retraining based on actual *** addition,based on the prediction model,new features obtained by model comparison and literature evidence were transferred to integrate the new models with better ***:Comparing the characteristics and differences of datasets used in model training,effective model verification,and a fusion of models is necessary in improving the performance of AI models.

关键词： Coronavirus disease 2019 decision tree interpretable models logistic regression prognosis prediction

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Chinese E-Romance: Analyzing and Visualizing 7.92 Million Alibaba Valentine's Day Purchases

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Data and Information Management 2021年第4期5卷 363-371页

作者： Wang, Yongzhen Liu, Xiaozhong Ju, Yingnan Börner, Katy Lin, Jun Sun, Changlong Si, Luo WISE Lab Institute of Science of Science and S&T Management Faculty of Humanities and Social Sciences Dalian University of Technology Dalian China Computer Science Department Worcester Polytechnic Institute WorcesterMA United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Alibaba DAMO Academy Hangzhou China

The days that precede Valentine's Day are characterized by extensive gift shopping activities all across the globe. In China, where much shopping takes place online, there has been an explosive growth in e-commerce sales during Valentine's Day over the recent years. This exploratory study investigates the extent to which each product category and each shopper group can exhibit romantic love within China's e-market throughout the 2 weeks leading up to 2019 Valentine's Day. Massive data from Alibaba, the biggest e-commerce retailer worldwide, are utilized to formulate an innovative romance index (RI) to quantitatively measure e-romantic values for products and shoppers. On this basis, millions of shoppers, along with their millions of products purchased around Valentine's Day, are analyzed as a case study to demonstrate their love consumption and romantic gift-giving. The results of the analysis are then illustrated to help understand Chinese e-romance based on the perspectives of different product categories and shopper groups. This empirical information visualization also contributes to improving the segmentation, targeting, and positioning of China's e-market for Valentine's Day. © 2021 Yongzhen Wang et al., published by Sciendo

关键词： Sales

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AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT

arXiv

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arXiv 2025年

作者： Lang, Zifan Liu, Guixia Sun, Geng Li, Jiahui Sun, Zemin Wang, Jiacheng Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

This paper proposes a UAV-assisted forwarding system based on distributed beamforming to enhance age of information (AoI) in Internet of Things (IoT). Specifically, UAVs collect and relay data between sensor nodes (SNs) and the remote base station (BS). However, flight delays increase the AoI and degrade the network performance. To mitigate this, we adopt distributed beamforming to extend the communication range, reduce the flight frequency and ensure the continuous data relay and efficient energy utilization. Then, we formulate an optimization problem to minimize AoI and UAV energy consumption, by jointly optimizing the UAV trajectories and communication schedules. The problem is non-convex and with high dynamic, and thus we propose a deep reinforcement learning (DRL)-based algorithm to solve the problem, thereby enhancing the stability and accelerate convergence speed. Simulation results show that the proposed algorithm effectively addresses the problem and outperforms other benchmark algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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