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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21-30 订阅

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An Analytical Paradigm for Exploration of Diabetes Using Machine Learning 19

An Analytical Paradigm for Exploration of Diabetes Using Mac...

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19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Chauhan, Ritu Mishra, Aarushi Mani, Ruchi Jakhmola Yafi, Eiad Zuhairi, Megat F. Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP India Proteomics and Translational Research Lab Center for Medical Biotechnology Amity University UP India Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Malaysian Institute of Information Technology University of Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

Machine Learning is an approach in AI which has swiftly emerged as a transformative and powerful tool in the healthcare industry, changing the methods of diagnosing and predicting diseases. In the present situation, the application of different machine learning methods, including deep learning, ensemble techniques, and natural language processing, across various healthcare activities, such as disease prediction, medical imaging analysis, drug discovery, and tailored treatment suggestions, has offered encouraging prospects for improved diagnostics, treatment, and patient results. By utilizing a diverse range of data sources, including electronic health records, medical imaging, and wearable devices, machine learning algorithms have facilitated early detection, precise prognostication, and customized interventions. The application of machine learning algorithms into health databases is the ongoing area of research concerning future modelling predictions. The current study revolves around the utilization of descriptive analytic methods and machine learning algorithms to predict if an individual is diabetic. The effectiveness of four distinct models of machine learning, specifically Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM) and K Nearest Neighbor (KNN), is assessed and contrasted. The respective accuracies of these classifiers are noted as 89.83%, 77.16%, 84.84%, 81.16%. © 2025 IEEE.

关键词： Electronic health record

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Comparative Efficiency of Machine Learning models for Enhancing algorithms in Solving Multiextremal Multicriteria Problems 15th

Comparative Efficiency of Machine Learning Models for Enha...

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15th international conference on Optimization and Applications, OPTIMA 2024

作者： Konnov, Sergey Kozinov, Evgeny Barkalov, Konstantin Grishagin, Vladimir Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia

ISBN: (纸本)9783031791185

Solving a multicriteria optimization problem requires finding a whole set of independent variables corresponding to non-dominated criteria values (Pareto set). The complexity of these problems increases significantly in the case when the criteria are multiextremal. The paper presents several variations of the global optimization algorithm intended for approximate solving "black-box" multiobjective problems with multiextremal criteria. The proposed methods are based on a combination of the information-statistical approach and machine learning procedures aimed at increasing the efficiency of constructing the Pareto set approximation. As a novelty, the paper describes a new approach to using machine learning models within the algorithm of multicriteria optimization. Based on a representative computational experiment, the effectiveness of different versions of the proposed algorithm is estimated. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Examining Effects of Schizophrenia on EEG with Explainable Deep Learning models 22

Examining Effects of Schizophrenia on EEG with Explainable D...

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IEEE 22nd international conference on bioinformatics and Bioengineering (BIBE)

作者： Ellis, Charles A. Sattiraju, Abhinav Miller, Robyn Calhoun, Vince Georgia State Univ Georgia Inst Technol Triinst Ctr Translat Res Neuroimaging & Data Sci Atlanta GA 30303 USA Emory Univ Atlanta GA 30322 USA

ISBN: (数字)9781665484879

ISBN: (纸本)9781665484879

Schizophrenia (SZ) is a mental disorder that affects millions of people globally. At this time, diagnosis of SZ is based upon symptoms, which can vary from patient to patient and create difficulty with diagnosis. To address this issue, researchers have begun to look for neurological biomarkers of SZ and develop methods for automated diagnosis. In recent years, several studies have applied deep learning to raw EEG for automated SZ diagnosis. However, the use of raw time-series data makes explainability more difficult than it is for traditional machine learning algorithms trained on manually engineered features. As such, none of these studies have sought to explain their models, which is problematic within a healthcare context where explainability is a critical component. In this study, we apply perturbation-based explainability approaches to gain insight into the spectral and spatial features learned by two distinct deep learning models trained on raw EEG for SZ diagnosis for the first time. We develop convolutional neural network (CNN) and CNN long short-term memory network (CNN-LSTM) architectures. Results show that both models prioritize the T8 and C3 electrodes delta- and gamma-bands, which agrees with previous literature and supports the overall utility of our models. This study represents a step forward in the implementation of deep learning models for clinical SZ diagnosis, and it is our hope that it will inspire the more widespread application of explainability methods for insight into deep learning models trained for SZ diagnosis in the future.

关键词： schizophrenia deep learning diagnosis explainable AI

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Cracking the Genetic Codes: Exploring DNA Sequence Classification with Machine Learning algorithms and Voting Ensemble Strategies

Cracking the Genetic Codes: Exploring DNA Sequence Classific...

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2024 international conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Rahman, Arifur Zaman, Sakib Das, Dola Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350350289

In the domain of bioinformatics, DNA sequence classification is an indispensable tool that spans various scientific disciplines, contributing to scientists' understanding of biology, aiding in the identification of genes, regulatory elements, and the functional significance of distinct genomic regions. Moreover, it plays a vital role in disease diagnosis, treatment strategies, drug discovery, evolution, agriculture, forensic identification, environmental monitoring and more. The classification process involves the intricate mapping of DNA sequences to distinct classes based on the arrangement of nucleotides. A fractional mutation in the sequence corresponds to a nuanced shift in the assigned class. Every numerical instance, serving as a depiction of a particular class, is closely associated with a specific gene lineage. In this study, for the DNA sequence preprocessing, both K-mer counting and count vectorization were used respectively. Afterwards, we utilized a variety of classifier models, encompassing Multinomial naive bayes (MNB), Logistic regression (LR), Random forest (RF), LightGBM (LGMB), XGBoost (XGB), K-nearest neighbors (KNN) and Decision tree (DT) algorithm on three types of DNA sequence datasets (Human, Chimpanzee & Dog) to identify each of sequence's corresponding gene class (0, 1, 2, 3, 4, 5, & 6). Then, the highest three and highest five classifier models were picked based on their accuracy scores. Afterwards, both soft voting and hard voting ensemble methods were implemented on this cluster of fundamental models to effectively leverage their collective predictive strength. The soft voting ensemble on the best three models consistently reached the highest accuracy across all three datasets. Employing this ensemble method, the human, chimpanzee, and dog datasets exhibited highest performance metrics i.e. accuracy, precision, recall, and fl-scores of (98.42 %, 98.41 %, 98.40%, 98.40%), (92.28%, 92.40%, 92.30%, 92.10%), and (70.12%, 73.10%, 70.10%, 69.2

关键词： bioinformatics

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A Multi-dimensional Survey on Learning from Imbalanced Data 14th

A Multi-dimensional Survey on Learning from Imbalanced Dat...

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14th international conference on Information, Intelligence, Systems, and Applications, IISA 2023

作者： Akritidis, Leonidas Bozanis, Panayiotis Department of Science and Technology International Hellenic University Thermi 14th km Thessaloniki - N. Moudania Thessaloniki57001 Greece

ISBN: (纸本)9783031674259

The problem of data imbalance in machine learning is related to the uneven distribution of the training examples to the involved classes. Nowadays, a large number of research fields and applications suffer from class imbalance, including Cybersecurity, bioinformatics, Natural Language Processing, management of multimedia content, and so on. Directly training machine/deep learning classifiers with such data has been proved quite problematic, because the generated models become strongly biased towards the majority class. Unable to learn the minority classes effectively, the accuracy of these "imbalanced" models degrades rapidly. Given the importance of the problem, numerous researchers have introduced innovative, state-of-the-art approaches with the aim of addressing it. In this chapter, we present a survey on the most significant advances in the area, by adopting a multi-dimensional categorization approach. Specifically, we classify the relevant works according to: i) the application field they focus on, ii) the methods they introduce to mitigate class imbalance, and iii) the classification models they utilize to evaluate the introduced algorithms. Additionally, we cover the state-of-the-art overviews in a systematic manner and we describe the proposed methods and their achieved results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Contrastive Learning

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10th international Work-conference on bioinformatics and Biomedical Engineering, IWBBIO 2023

10th International Work-Conference on Bioinformatics and Bio...

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10th international Work-conference on bioinformatics and Biomedical Engineering, IWBBIO 2023

ISBN: (纸本)9783031349522

The proceedings contain 78 papers. The special focus in this conference is on bioinformatics and Biomedical Engineering. The topics include: Three-Dimensional Representation and Visualization of High-Grade and Low-Grade Glioma by Nakagami Imaging;speeding up Simulations for Radiotherapy Research by Means of Machine Learning;A Meta-Graph for the Construction of an RNA-Centered Knowledge Graph;Structural Analysis of RNA-Binding Protein EWSR1 Involved in Ewing’s Sarcoma Through Domain Assembly and Conformational Molecular Dynamics Studies;Pharmacoinformatic Analysis of Drug Leads for Alzheimer’s Disease from FDA-Approved Dataset Through Drug Repositioning Studies;motion Control of a Robotic Lumbar Spine Model;annotation-Free Identification of Potential Synteny Anchors;analysing Dose Parameters of Radiation Therapy Treatment Planning and Estimation of Their Influence on Secondary Cancer Risks;A Platform for the Radiomic Analysis of Brain FDG PET Images: Detecting Alzheimer’s Disease;identification of InhA-Inhibitors Interaction Fingerprints that Affect Residence Time;deep Learning for Automatic Electroencephalographic Signals Classification;evaluation of Homogeneity of Effervescent Tablets Containing Quercetin and Calcium Using X-ray Microtomography and Hyperspectral Analysis;the Effect of Biofeedback on Learning the Wheelie Position on Manual Wheelchair;preliminary Study on the Identification of Diseases by Electrocardiography Sensors’ Data;exploring Machine Learning algorithms and Protein Language models Strategies to Develop Enzyme Classification Systems;a System Biology and bioinformatics Approach to Determine the Molecular Signature, Core Ontologies, Functional Pathways, Drug Compounds in Between Stress and Type 2 Diabetes;recent Advances in Discovery of New Tyrosine Kinase Inhibitors Using Computational methods;the Coherent Multi-representation Problem with Applications in Structural Biology;computational Study of Conformational Changes in Intrinsically Disordered

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10th international Work-conference on bioinformatics and Biomedical Engineering, IWBBIO 2023

10th International Work-Conference on Bioinformatics and Bio...

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10th international Work-conference on bioinformatics and Biomedical Engineering, IWBBIO 2023

ISBN: (纸本)9783031349591

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Federated Learning and Parallel Prompt Scheduling Strategies for Large Language models 24th

Federated Learning and Parallel Prompt Scheduling Strategie...

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

作者： Lv, Guangtong Gu, Bruce Jia, Xiaocong Gao, Longxiang Qu, Youyang Cui, Lei Jinan China Shandong Provincial Key Laboratory of Computer Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9789819615278

Large language models have garnered significant attention and are widely utilized across different fields due to their impressive performance. However, centralized training of these models can pose privacy risks like data leakage, hindering their application and advancement. As a decentralized learning paradigm, Federated Learning(FL) can effectively address data security in large language models and has specific application scenarios. In addition, the fine-tuning process of large language models also incurs significant communication expenses and leads to the loss of computational resources. The utilization of prompt tuning in federated learning is anticipated to decrease training costs, accelerate the training process, and enhance model performance. In this paper, we propose ‘FedLLM-PPS’ to explore a novel approach for large language models within federated learning. This method address data security concerns associated with large language models while enhancing performance and efficiency. Experiments based on several datasets for various Natural Language Processing(NLP) tasks demonstrate superior performance and efficiency compared to baseline methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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LRNN: A Formal Logic Rules-Based Neural Network for Software Defect Prediction 1

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25th international conference on Formal Engineering methods, ICFEM 2024

作者： Shang, Yuxiang Liu, Shaoying Hiroshima University Hiroshima Japan

ISBN: (数字)9789819606177

ISBN: (纸本)9789819606160

Class imbalance in deep learning systems can lead to biased models due to the unbalanced distribution of data classes in the training dataset. When such biased models are adopted for software defect prediction (SDP), the prediction precision will be significantly affected. To improve the mechanism of existing related deep learning models for SDP, we propose a new neural network, called Logic Rules Neural Network (LRNN), to address the class imbalance problem. LRNN is characterized by utilizing formal logic rules on the association between code metrics to improve the efficiency of deep learning. We describe how relevant formal logic can be identified and selected and how it can be utilized in the learning process. We evaluate the performance of our approach by conducting some experiments using the NASA, PROMISE, AEEEM repositories. The results demonstrate a significant improvement over several existing algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Prediction models

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Systems for Scalable Graph Analytics and Machine Learning: Trends and methods 24

Systems for Scalable Graph Analytics and Machine Learning: T...

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30th ACM SIGKDD conference on Knowledge Discovery and Data Mining

作者： Da Yan Yuan, Lyuheng Ahmad, Akhlaque Zheng, Chenguang Chen, Hongzhi Cheng, James Indiana Univ Bloomington Dept Comp Sci Bloomington IN 47401 USA Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Kasma Pte Ltd Singapore Singapore

ISBN: (纸本)9798400704901

Graph-theoretic algorithms and graph machine learning models are essential tools for addressing many real-life problems, such as social network analysis and bioinformatics. To support large-scale graph analytics, graph-parallel systems have been actively developed for over one decade, such as Google's Pregel and Spark's GraphX, which (i) promote a think-like-a-vertex computing model and target (ii) iterative algorithms and (iii) those problems that output a value for each vertex. However, this model is too restricted for supporting the rich set of heterogeneous operations for graph analytics and machine learning that many real applications demand. In recent years, two new trends emerge in graph-parallel systems research: (1) a novel think-like-a-task computing model that can efficiently support the various computationally expensive problems of subgraph search;and (2) scalable systems for learning graph neural networks. These systems effectively complement the diversity needs of graph-parallel tools that can flexibly work together in a comprehensive graph processing pipeline for real applications, with the capability of capturing structural features. This tutorial will provide an effective categorization of the recent systems in these two directions based on their computing models and adopted techniques, and will review the key design ideas of these systems.

关键词： Graph system vertex structure subgraph graph neural network GNN

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