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检索条件"任意字段=International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2013"

共 1098 条记录，以下是31-40 订阅

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Multi-target Feature Selection Method for Predicting User-Level Psychological Status from Text 26th

Multi-target Feature Selection Method for Predicting User-L...

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26th international conference on Information Integration and Web Intelligence, iiWAS 2024

作者： Cai, Danmeng Wakabayashi, Kei Ye, Shaoyu University of Tsukuba Tsukuba Japan

ISBN: (纸本)9783031780899

Estimating psychological status from linguistic information is essential for understanding human actions across various domains. Conventional machine learning methods often face overfitting issues due to the tendency of linguistic features outnumbering data entries, which need to be collected by questionnaire survey. Our study addresses this issue by employing a multi-target feature selection (MTFS) method, which selects linguistic features relevant to multiple psychological variables to enhance the generalization ability of the prediction model. We tested MTFS against single-target feature selection (STFS) methods using several machine learning algorithms on two datasets. The results show that the MTFS method improves prediction performance, which suggests that implications from the field of social psychology can enhance the selection of relevant linguistic features. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Integrating Mixed Reality with Neural Networks for Advanced Molecular Visualization in bioinformatics: A Mathematical Framework for Drug Discovery 15

Integrating Mixed Reality with Neural Networks for Advanced ...

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15th international Scientific and Practical conference on Environment. Technology. Resources, ETR 2024

作者： Trenchev, Ivan Trencheva, Tereza Angelov, Vladimir Spirov, Yordan Karshiyska, Yana Shumanova, Kamelia Faculty of Information Sciences University of Library Studies and Information Technologies South-West University "Neofit Rilski" Sofia Bulgaria Faculty of Library Studies and Cultural Heritage University of Library Studies and Information Technologies Sofia Bulgaria Faculty of Information Sciences University of Library Studies and Information Technologies Sofia Bulgaria

ISBN: (纸本)9781713899570

In this study, we develop and present an innovative approach that integrates Mixed Reality (MR) technologies with neural network algorithms, aiming to revolutionize molecular structure visualization in bioinformatics through the application of mathematical methods. The development includes the creation of a mathematical framework aimed at optimizing drug discovery processes, utilizing the potential of MR to facilitate detailed and interactive exploration of molecules in three-dimensional space. Our approach is based on the use of Unreal Engine for the realization of a simulation environment and the application of Python and PyTorch for the development of complex neural network models. These models are capable of efficiently processing and analyzing molecular data, enabling scientifically grounded manipulation of molecular structures. This approach facilitates the identification of potential active sites for interaction with pharmaceutical agents, improving the efficiency and speed of the drug discovery process. A key aspect of our work is the development of a comprehensive mathematical framework that effectively simplifies and optimizes molecular design and analysis, while simultaneously increasing the accuracy of predictions for interactions between potential drug molecules and their targets. This approach not only enriches our understanding of the molecular basis of diseases but also offers a more rational and economical path to pharmacological development. In conclusion, we propose a new approach that we hope will be considered and applied by the scientific community. This method presents a promising opportunity for advancement in research and development in bioinformatics and pharmacology, providing a solid foundation for further exploration of molecular dynamics and drug discovery through the application of mathematical and computer sciences. © 2024 Ivan Trenchev, Tereza Trencheva, Vladimir Angelov, Yordan Spirov, Yana Karshiyska, Kamelia Shumanova.

关键词： Molecular structure

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A Mini-model can Make Machine Unlearning Better 24th

A Mini-model can Make Machine Unlearning Better

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24th international conference on algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Zhao, Mingkang Yu, Weng Zhu, Congcong China University of Geosciences Wuhan China Ministry of Education Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance School of Information Engineering Beijing China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan572499 China City University of Macau 999078 China

ISBN: (纸本)9789819615247

In recent years, with the development of machine learning, plenty of personal data have been utilized in the training process of the models which incurs severe privacy leakage in the field. Current regulations mandate the removal of private user information from both databases and machine learning models upon specific deletion requests. While wiping data records from memory storage is a straightforward task, eliminating the influence of specific data samples from an already-trained model is challenging. Numerous studies in machine unlearning focus on mitigating the impact of target data by adjusting model parameters in deep learning. However, existing methods for data removal in deep learning often fall short of meeting real-world practicality. The intricacies of deep neural networks make unlearning in deep learning a less expedient process than retraining from scratch. For example, some methods require a ton of matrix calculations or even more complicated computations, and some algorithms need a lot of space, which ends up hindering their effectiveness in real-world applications. Accordingly, we’ve come up with a more practical solution. Our method just needs a small part of the validation dataset to pre-train a mini-model to update the parameters of the target model quickly. With this assistant model, we can quickly and accurately unlearn target classes and items. We prove that our proposed method is highly effective in unlearning tasks with not very large amounts of data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information leakage

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Machine Learning-Assisted Protein Structure Prediction: An AI Approach for Biochemical Insights 9

Machine Learning-Assisted Protein Structure Prediction: An A...

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9th international conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Mahendran, S. Radha Dogra, Akriti Mendhe, Dinesh Babu, S. B. G. Tilak Dixit, Shriniket Singh, Surya Pratap Vels Institute of Science Technology And Advanced Studies Department of Bioinformatics Chennai Pallavaram India Manav Rachna Dental College Department of Oral & Maxillofacial Surgery Haryana Faridabad India Rutgers State University of New Jersey Office of Research Computing New BrunswickNJ United States Aditya Engineering College Department of Ece Surampalem India School of Computer Science Engineering Vellore Institute of Technology Tamil Nadu Vellore India Upums Department of Multi-Disciplinary Research Unit UP Etawah India

ISBN: (纸本)9798350365092

Recently, machine learning has changed the field of bioinformatics by making it possible to identify and understand protein structures in ways that were not possible before. This paper gives a thorough examination of a unique way for guessing protein structure. It uses complex machine learning algorithms to give you deep biological information. We use cutting-edge deep learning models along with traditional computer methods to improve the accuracy and usefulness of predicting protein structures. The suggested AI-assisted method uses a variety of protein structures and attention processes to find complex spatial connections and long-range interactions within protein sequences. It does this by using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and CNNs. Using machine learning to predict protein structures can be very useful, and this work shows how it can effectively connect computational biology and experimental research. Our method not only makes predictive modeling better, but it also gives researchers a useful way to learn more about the complex links between protein structure and function as the field grows. The study's results could speed up the process of finding new drugs and help us learn more about how different diseases work at the molecular level. © 2024 IEEE.

关键词： Proteins

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Towards a Parallel Code for Cellular Behavior in Vitro Prediction 4th

Towards a Parallel Code for Cellular Behavior in Vitro Pr...

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4th international conference on Numerical Computations: Theory and algorithms, NUMTA 2023

作者： De Luca, Pasquale Galletti, Ardelio Marcellino, Livia Department of Mathematics University Carlos III of Madrid Leganes Spain International PhD Programme / UNESCO Chair "Environment Resources and Sustainable Development" Department of Science and Technology Parthenope University of Naples Centro Direzionale Isola C4 Naples Italy Science and Technologies Department Parthenope University of Naples Naples Italy

ISBN: (纸本)9783031812460

In recent years, there has been an increasing interest in developing in vitro models that predict the behavior of cells in living organisms. Mathematical models based on differential equations, and related numerical algorithms, have been provided to this aim. In this work, we present first experiences in designing parallel strategies for accelerating an algorithm for behavior prediction based on the Cellular Potts Model (CPM). In particular, we exploit the computational power of Graphic Process Units in CUDA environment to address main low-level kernels involved. Tests and experiments complete the paper. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Potts model

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Graph Sampling Quality Prediction for Algorithm Recommendation 31

Graph Sampling Quality Prediction for Algorithm Recommendati...

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31st Annual IEEE international conference on High Performance Computing, Data, and Analytics, HiPC 2024

作者： Dizaji, S. Haleh S. Farahani, Reza Rožanec, Jože M. Kimovski, Dragi Soylu, Ahmet Prodan, Radu Institute of Information Technology University of Klagenfurt Austria Jožef Stefan Institute Ljubljana Slovenia Kristiania University College Oslo Norway

ISBN: (纸本)9798331509095

The increasing size of graph structures in real-world applications, such as distributed computing networks, social media, or bioinformatics, requires appropriate sampling algorithms that simplify them while preserving key properties. Unfortunately, predicting the outcome of graph sampling algorithms is challenging due to their irregular complexity and randomized properties. Therefore, it is essential to identify appropriate graph features and apply suitable models capable of estimating their sampling outcomes. In this paper, we compare three machine learning (ML) models for predicting the divergence of five metrics produced by twelve node, edge, and traversal-based graph sampling algorithms: degree distribution (D3), clustering coefficient distribution (C2D2), hop-plots distribution (HPD2) (including the largest connected component (HPD2C)), and execution time. We use these prediction models to recommend suitable sampling algorithms for each metric and conduct mutual information analysis to extract relevant graph features. Experiments on six large real-world graphs demonstrate a prediction error under 20 % in C2D2 and HPD2 prediction for most algorithms despite their relatively high dissimilarity with the training data. Sampling algorithm recommendations on ten real-world graphs show higher hits@3 for D3 and C2D2 and comparable results for HPD2 and HPD2C compared to the K-best baseline method. Finally, ML models show superior runtime recommendations compared to baseline methods, with hits@3 over 86 % for synthetic and real graphs and hits@1 over 60 % for small graphs. These findings are promising for algorithm recommendation systems, particularly when balancing quality and runtime preferences. © 2024 IEEE.

关键词： Prediction models

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Enhancing Quantum Diffusion models with Pairwise Bell State Entanglement 27th

Enhancing Quantum Diffusion Models with Pairwise Bell State ...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Shah, Shivalee R. K. Vatsa, Mayank Indian Institute of Technology Jodhpur Rajasthan Jodhpur India

ISBN: (纸本)9783031783944

This paper introduces a novel quantum diffusion model designed for Noisy Intermediate-Scale Quantum (NISQ) devices. Unlike previous methods, this model efficiently processes higher-dimensional images with complex pixel structures, even on qubit-limited platforms. This is accomplished through a pairwise Bell-state entangling technique, which reduces space complexity. Additionally, parameterized quantum circuits enable the generation of quantum states with minimal parameters, while still delivering high performance. We conduct comprehensive experiments, comparing the proposed model with both classical and quantum techniques using datasets such as MNIST and CIFAR-10. The results show significant improvements in computational efficiency and performance metrics such as FID, SSIM and PSNR. By leveraging quantum entanglement and superposition, this approach advances quantum generative learning. This advancement paves the way for more sophisticated and resource-efficient quantum diffusion algorithms capable of handling complex data on the NISQ devices. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Quantum entanglement

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A review: Data pre-processing techniques used for diabetes prediction 9th

A review: Data pre-processing techniques used for diabetes p...

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9th international conference on Computer Science and Computational Intelligence, ICCSCI 2024

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Cmputer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

When processing datasets in diabetes classification, common problems included a large number of missing values, outliers, and dataset imbalance. To deal with those issues, this study analyzed 18 studies on diabetes classification with machine learning algorithms over the past 5 years. This revealed the important role of data pre-processing in creating effective classification models, as it was found that by using different data pre-processing techniques, the same model can provide different performance. The study identified K-Nearest Neighbor (KNN) and support vector machine (SVM) as superior methods for filling in missing values, achieving an accuracy of 98.49% and 94.89%, respectively. These approaches outperformed traditional methods such as median or mean replacement. However, the challenge of imbalanced data sets remains in all studies reviewed. The common evaluation metrics used to evaluate the created models in previous studies included accuracy, precision, specificity, sensitivity/recall, and F1 Score. Overall, this review showed that the role of data pre-processing is no less important than algorithm selection to improve the performance of machine learning models in diabetes classification. © 2024 The Authors.

关键词： Adversarial machine learning

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Black-Box Adversarial Attack Against Transformer-Based Object Detection models in Vehicular Networks 24th

Black-Box Adversarial Attack Against Transformer-Based Objec...

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24th international conference on algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Tang, Yinghao Lu, Jinbiao Kang, Xingkai Li, Yuhang Guo, Hongchen School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819615476

Vehicular networks based on distributed machine learning have gained extensive research attention due to their outstanding performance compared to traditional methods. Specifically, machine learning systems can output vehicle control signals based on inputs from the object detection models. However, it has been demonstrated that machine learning is susceptible to security issues, where adversarial samples may mislead models into producing incorrect outputs. In this paper, we propose a novel adversarial patch attack against transformer-based object detection models. Our attack can make the object of a specific class invisible to object detection models. To enhance the attack performance and transferability of the adversarial patches, we designed the Gradient Self-Ensemble module and Transformer Masking Matrix module. These modules obscure parts of the inputs to each encoder and decoder and aggregate the outputs of all decoders within the model to generate adversarial samples, significantly improving the success rates of black-box attacks. We successfully suppressed the model’s inference capabilities and deceived multiple transformer-based object detection models. Our work highlights the vulnerability of object detection models in vehicular networks to adversarial patch attacks in both digital and physical domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Vehicular ad hoc networks

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On the Application of Saddle-Point methods for Combined Equilibrium Transportation models 23rd

On the Application of Saddle-Point Methods for Combined E...

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23rd international conference on Mathematical Optimization Theory and Operations Research, MOTOR 2024

作者： Yarmoshik, Demyan Persiianov, Michael Moscow Institute of Physics and Technology Dolgoprudny Russia Institute for Information Transmission Problems Moscow Russia The Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (纸本)9783031627910

Travel demand modeling is an essential tool in urban planning and transportation system management. Existing practically efficient algorithms for solving multistage travel demand problems are variations of the heuristic sequential procedure. We propose a novel approach that applies saddle-point methods to a combined convex optimization formulation of the problem. Unlike all previous methods, our algorithm does not require costly shortest-paths calculations, and can be seamlessly scaled on GPUs. We show that in some cases it drastically outperforms the sequential procedures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convex optimization

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