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Prediction of Essential Proteins Using Topological Properties in GO-Pruned PPI Network Based on Machine Learning Methods

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Tsinghua Science and Technology 2012年第6期17卷 645-658页

作者： Wooyoung Kim Department of Computing and Software Systems University of Washington Bothell

The prediction of essential proteins, the minimal set required for a living cell to support cellular life, is an important task to understand the cellular processes of an organism. Fast progress in high-throughput technologies and the production of large amounts of data enable the discovery of essential proteins at the system level by analyzing Protein-Protein Interaction （PPI） networks, and replacing biological or chemical experiments. Furthermore, additional gene-level annotation information, such as Gene Ontology （GO） terms, helps to detect essential proteins with higher accuracy. Various centrality algorithms have been used to determine essential proteins in a PPI network, and, recently motif centrality GO, which is based on network motifs and GO terms, works best in detecting essential proteins in a Baker＇s yeast Saccharomyces cerevisiae PPI network, compared to other centrality algorithms. However, each centrality algorithm contributes to the detection of essential proteins with different properties, which makes the integration of them a logical next step. In this paper, we construct a new feature space, named CENT-ING-GO consisting of various centrality measures and GO terms, and provide a computational approach to predict essential proteins with various machine learning techniques. The experimental results show that CENT-ING-GO feature space improves performance over the INT-GO feature space in previous work by Acencio and Lemke in 2009. We also demonstrate that pruning a PPI with informative GO terms can improve the prediction performance further.

关键词： essential protein network motif gene ontology motif centrality GO CENT-ING-GO centrality algorithm Protein-Protein Interaction （PPI） machine learning

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An intelligent framework for sarcasm detection in Arabic tweets using deep learning with Al-Biruni earth radius optimization algorithm

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Alexandria Engineering Journal 2025年 127卷 562-572页

作者： AL-anazi, Reema G. A. Alzaidi, Muhammad Swaileh Eltahir, Majdy M. Almukadi, Wafa Sulaiman Alajmani, Samah Hazzaa Darem, Abdulbasit A. Alshahrani, Mohammed Alhebaishi, Nawaf Department of Arabic Language and Literature College of Humanities and Social Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of English Language College of Language Sciences King Saud University P. O. Box 145111 Riyadh Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Software Engineering College of Engineering and Computer Science University of Jeddah Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Computer Science College of Science Northern Border University Arar Saudi Arabia Computer Department Applied College Najran University Najran66462 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Social media networks play a major role in expressing people's feelings, reviews, and thoughts. One of the popular linguistic patterns to express or criticize one's ideas with ridicule is sarcasm, where they have unintended and intended meanings in written text. The sarcastic text is used to reverse the polarity of the sentiment. As a result, sarcasm detection in the text positively affects the sentimental analysis (SA) tasks and improves accuracy. The sarcasm recognition of Arabic content is constrained even though Arabic is the most popular language for web content sharing, and yet still naive owing to various challenges, such as the insufficient data sources, the multiple dialects, and the morphological structure of the Arabic language. This study presents an Artificial Intelligence Driven Sarcasm Detection using Optimal Deep Learning (AIDSD-ODL) technique on Arabic tweets. In the AIDSD-ODL technique, a hyperparameter-tuned DL model is utilized to identify sarcastic or non-sarcastic Arabic tweets. At the initial stage, the AIDSD-ODL method undergoes data pre-processing to convert the input tweets into a compatible format. In the next phase, the Glove word embedding process is used in the AIDSD-ODL technique. The self-attention bidirectional long short-term memory (SA-BiLSTM) model is utilized for sarcasm detection. The Al-Biruni Earth Radius (BER) model is implemented for the hyperparameter selection process to enhance the performance of the SA-BiLSTM network. The experimental results of the AIDSD-ODL method are examined under the Arabic tweets dataset. The comparison study of the AIDSD-ODL method portrayed a superior accuracy value of 94.20 % over existing models. © 2025 The Authors

关键词： Deep learning

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Vulnerability Repair via Concolic Execution and Code Mutations

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ACM Transactions on software Engineering and Methodology 2025年第4期34卷

作者： Shariffdeen, Ridwan Timperley, Christopher S. Noller, Yannic Le Goues, Claire Roychoudhury, Abhik Department of Computer Science National University of Singapore School of Computing Singapore Singapore Software and Societal Systems Department Carnegie Mellon University PittsburghPA United States Ruhr University Bochum Bochum Germany Carnegie Mellon University PittsburghPA United States National University of Singapore Singapore Singapore

Security vulnerabilities detected via techniques like greybox fuzzing are often fixed with a significant time lag. This increases the exposure of the software to vulnerabilities. Automated fixing of vulnerabilities where a tool can generate fix suggestions is thus of value. In this work, we present such a tool, called CrashRepair, to automatically generate fix suggestions using concolic execution, specification inference, and search techniques. Our approach avoids generating fix suggestions merely at the crash location because such fixes often disable the manifestation of the error instead of fixing the error. Instead, based on sanitizer-guided concolic execution, we infer desired constraints at specific program locations and then opportunistically search for code mutations that help respect those constraints. Our technique only requires a single detected vulnerability or exploit as input;it does not require any user-provided properties. Evaluation results on a wide variety of CVEs in the VulnLoc benchmark, show CrashRepair achieves greater efficacy than state-of-the-art vulnerability repair tools like Senx. The repairs suggested come in the form of a ranked set of patches at different locations, and we show that on most occasions, the desired fix is among the top-3 fixes reported by CrashRepair. © 2025 Copyright held by the owner/author(s).

关键词： Search engines

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Correction to: A comprehensive performance evaluation of container migration strategies

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computing 2025年第4期107卷 1-2页

作者： Feitosa, Leonel Barbosa, Vandirleya Sabino, Arthur Lima, Luiz Nelson Fé, Iure Silva, Luis Guilherme Callou, Gustavo Carvalho, Juliana Leão, Erico Nguyen, Tuan Anh Rego, Paulo Silva, Francisco Airton Laboratory of Applied Research to Distributed Systems (PASID) Federal University of Piauí (UFPI) Picos Brazil Department of Computing Federal Rural University of Pernambuco (UFRPE) Recife Brazil Department of Software Engineering Faculty of Information Technology University of Industry and Trade (HUIT) Ho Chi Minh City Vietnam Department of Computer Science Federal University of Ceará (UFC) Fortaleza Brazil

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Pronounced advance on typhoon track forecast with global convection-permitting model

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Science Bulletin 2025年第9期70卷 1424-1428页

作者： Gu, Jun Zhao, Chun Li, Gudongze Feng, Jiawang Xu, Mingyue Du, Qiuyan Xia, Zihan Li, Yubin Chen, Guanghua Hao, Xiaoyu Chen, Junshi An, Hong Deep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software University of Science and Technology of China Hefei230026 China Laoshan Laboratory Qingdao266237 China CAS Center for Excellence in Comparative Planetology University of Science and Technology of China Hefei230026 China School of Atmospheric Physics Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of Cloud-Precipitation Physics and Severe Storms Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China

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Transparent AI for Adaptive Fraud Detection

Transparent AI for Adaptive Fraud Detection

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Okechukwu Clement Agomuo Agomuo Kingsley Uzoma Zohaib Khan Agomuo Ijeoma Otuomasirichi Junaid Hussain Muzamal Department of Operations and Information Systems University of Utah Salt Lake City USA Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria dept. of Software Engineering FAST - NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Fast School of computing Fast - NUCES Lahore Pakistan

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

The rapid evolution of digital financial services has significantly increased the vulnerability of financial systems to fraudulent activities. Traditional machine learning models have offered substantial progress in detecting fraudulent transactions; however, they often lack adaptability and transparency, critical for dynamic financial environments and regulatory compliance. This paper introduces a novel approach that integrates Deep Reinforcement Learning (DRL) with Explainable Artificial Intelligence (XAI) to enhance fraud detection capabilities in cloud-based financial platforms. Our model, tested on the synthetic PaySim mobile money transaction dataset, leverages the adaptive nature of DRL to continuously learn and update its fraud detection strategies based on evolving transaction patterns. Concurrently, the integration of XAI provides clear, interpretable insights into the decision-making process, thus ensuring transparency and building trust. The proposed model demonstrates superior performance with an achieved accuracy of 99.5% and an Fl-score of 99.0%, significantly outperforming traditional fraud detection systems. These results not only underscore the efficacy of our model in improving the accuracy of fraud detection but also highlight its capability in offering significant improvements in understanding the underlying reasons for each decision, thereby aiding compliance with financial regulations. The practical implications of this research highlight its potential to provide a robust, scalable, and transparent solution for realtime fraud detection, which is essential for safeguarding the integrity of modern financial systems.

关键词： Adaptation models Accuracy Explainable AI Scalability Decision making Predictive models Regulation Fraud Security Standards

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Data-Driven Decision Management: Multi-Modal Analytics in a Health Case Study

Data-Driven Decision Management: Multi-Modal Analytics in a ...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Okechukwu Clement Agomuo Zohaib Khan Agomuo Ijeoma Otuomasirichi Agomuo Kingsley Uzoma Junaid Hussain Muzamal Department of Operations and Information Systems University of Utah Salt Lake City USA dept. of Software Engineering FAST - NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria Fast - NUCES Fast School of computing Lahore Pakistan

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

In an era where data is abundant yet underutilized, organizations across various sectors face challenges in extracting actionable insights from complex, multi-modal datasets. To address these challenges, this study proposes a versatile data analytics framework designed to enhance predictive accuracy and support informed decision-making. The framework integrates structured and unstructured data, leveraging advanced Transformer architectures to capture temporal dynamics and complex interactions within the data. In a healthcare use case, the framework demonstrated its effectiveness by achieving an Accuracy of 0.854, an F1-Score of 0.895, and an AUC-ROC of 0.930, significantly outperforming traditional models. By incorporating SHAP for interpretability, the framework ensures that its predictions are both accurate and transparent, making it a valuable tool for decision-makers. While healthcare is one application, the framework's flexibility allows it to be adapted to a wide range of industries. Future work will focus on validating the framework across different sectors and integrating it into operational workflows to optimize decision-making and outcomes.

关键词： Accuracy Decision making Mortality Medical services Organizations Predictive models Transformers Data models Information management X-ray imaging

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Loss Functions in Deep Learning: A Comprehensive Review

arXiv

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arXiv 2025年

作者： Elharrouss, Omar Mahmood, Yasir Bechqito, Yassine Serhani, Mohamed Adel Badidi, Elarbi Riffi, Jamal Tairi, Hamid Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Informatics Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University Fez Morocco

Loss functions are at the heart of deep learning, shaping how models learn and perform across diverse tasks. They are used to quantify the difference between predicted outputs and ground truth labels, guiding the optimization process to minimize errors. Selecting the right loss function is critical, as it directly impacts model convergence, generalization, and overall performance across various applications, from computer vision to time series forecasting. This paper presents a comprehensive review of loss functions, covering fundamental metrics like Mean Squared Error and Cross-Entropy to advanced functions such as Adversarial and Diffusion losses. We explore their mathematical foundations, impact on model training, and strategic selection for various applications, including computer vision (Discriminative and generative), tabular data prediction, and time series forecasting. For each of these categories, we discuss the most used loss functions in the recent advancements of deep learning techniques. Also, this review explore the historical evolution, computational efficiency, and ongoing challenges in loss function design, underlining the need for more adaptive and robust solutions. Emphasis is placed on complex scenarios involving multi-modal data, class imbalances, and real-world constraints. Finally, we identify key future directions, advocating for loss functions that enhance interpretability, scalability, and generalization, leading to more effective and resilient deep learning models. © 2025, CC BY.

关键词： Deep learning

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YOLO-RAW: Advancing UAV Detection With Robustness to Adverse Weather Conditions

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IEEE Transactions on Intelligent Transportation systems 2025年第6期26卷 7857-7873页

作者： Munir, Adnan Siddiqui, Abdul Jabbar Hossain, M. Shamim El-Maleh, Aiman Computer Engineering Department Dhahran31261 Saudi Arabia KFUPM SDAIA-KFUPM Joint Research Center for Artificial Intelligence IRC for Intelligent Secure Systems and Computer Engineering Department Dhahran31261 Saudi Arabia King Saud University College of Computer and Information Sciences Research Chair of Pervasive and Mobile Computing Department of Software Engineering Riyadh12372 Saudi Arabia KFUPM Computer Engineering Department Information and Computer Science Department IRC for Intelligent Secure Systems Dhahran31261 Saudi Arabia

With the widespread adoption of unmanned aerial vehicles (UAVs) in various applications (e.g., aerial transportation, traffic monitoring), there have been apprehensions regarding the associated risks of employing UAVs in both civilian and military contexts, including concerns about privacy infringement, safety issues, and security threats. Although several methods have been proposed to detect UAVs, a pressing open challenge is posed by varying adverse weather conditions that could degrade the performance of many existing methods. To address these limitations, this work proposes a YOLO-based (You Only Look Once) novel model, YOLO-RAW that exhibits improved performance in adverse weather conditions and considers different scales of UAVs. Furthermore, to facilitate a more comprehensive evaluation of the proposed model’s effectiveness in UAV detection, we have curated a complex background dataset and introduced three distinct test sets affected by adverse weather conditions. These three test sets comprise the Rainy Test Set (RTS), the AWGN (Additive White Gaussian Noise) Test Set (ATS), and the Motion Blurred Test Set (MBTS). The comprehensive experiments demonstrate the effectiveness of the proposed YOLO-RAW model over its counterparts in detecting UAVs under adverse conditions. © 2000-2011 IEEE.

关键词： Drones

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Beyond current boundaries: Integrating deep learning and AlphaFold for enhanced protein structure prediction from low-resolution cryo-EM maps

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Computational Biology and Chemistry 2025年 119卷

作者： Xin Ma Dong Si Division of Computing and Software Systems University of Washington Bothell 18115 Campus Way NE Bothell 98011 WA USA

Constructing atomic models from cryo-electron microscopy (cryo-EM) maps is a crucial yet intricate task in structural biology. While advancements in deep learning, such as convolutional neural networks (CNNs) and graph neural networks (GNNs), have spurred the development of sophisticated map-to-model tools like DeepTracer and ModelAngelo, their efficacy notably diminishes with low-resolution maps beyond 4 Å. To address this critical gap, this study introduces DeepTracer-LowResEnhance, an innovative computational framework that uniquely integrates structural predictions from AlphaFold with a deep-learning-based map refinement strategy specifically tailored to enhance low-resolution maps. Unlike existing techniques, our approach leverages the strengths of AlphaFold’s sequence-based predictions combined with advanced neural network-driven refinement processes to significantly improve map interpretability and modeling accuracy. DeepTracer-LowResEnhance demonstrates substantial and consistent improvements on an extensive dataset comprising 37 diverse protein cryo-EM maps, covering resolutions from 2.5 to 8.4 Å and including 22 challenging cases below 4 Å resolution. DeepTracer-LowResEnhance achieves an average TM-score improvement of 3.53x compared to baseline DeepTracer predictions. Notably, our enhanced methodology showed performance gains across 95.5% of the tested low-resolution datasets. A comparative analysis alongside traditional sharpening methods such as Phenix’s auto-sharpening illustrates DeepTracer-LowResEnhance’s superior capability in rendering more detailed and precise atomic models, thereby pushing the boundaries of current computational structural biology methodologies.

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