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检索条件"任意字段=IEEE Computational Systems Bioinformatics Conference"

共 878 条记录，以下是1-10 订阅

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21st ieee conference on computational Intelligence in bioinformatics and computational Biology, CIBCB 2024

21st IEEE Conference on Computational Intelligence in Bioinf...

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21st ieee conference on computational Intelligence in bioinformatics and computational Biology, CIBCB 2024

ISBN: (纸本)9798350356632

The proceedings contain 38 papers. The topics discussed include: a multiobjective evolutionary algorithm for colon cancer biomarkers identification on gene expression data;machine learning identifies novel microRNA biomarkers predictive for gastric cancer;methods for analyzing swallowing sound in dysphagia care: a telemedicine approach;a fast feature selection for interpretable modeling based on fuzzy inference systems;improving machine learning based sepsis diagnosis using heart rate variability;machine learning and gut microbiome for breast cancer screening;comprehensive modeling and question answering of cancer clinical practice guidelines using LLMs;and predicting metabolic reactions with a molecular transformer for drug design optimization.

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Comprehensive Survey of Post-Quantum Secure Lattice-Based Authentication Schemes for VANETs

Comprehensive Survey of Post-Quantum Secure Lattice-Based Au...

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2025 ieee International conference on computational, Communication and Information Technology, ICCCIT 2025

作者： Mishra, Dheerendra Nagar, Manish Maulana Azad National Institute of Technology Department of Mathematics Bioinformatics and Computer Applications Bhopal462003 India

ISBN: (纸本)9798331512965

Intelligent Transportation systems (ITS) rely heavily on Vehicular Ad Hoc Networks (VANETs) to enable secure and reliable communication between vehicles and roadside infrastructure. However, Shor's algorithm poses a significant threat to conventional cryptographic systems. To counteract this threat, post-quantum cryptography offers quantum-resistant solutions capable of providing secure authentication in the face of quantum adversaries. This paper presents a comprehensive survey of recently proposed six post-quantum state-of-the-art authentication protocols designed to meet the security and performance demands of VANETs. The survey identifies key strengths and limitations in existing approaches, providing valuable insights to guide future research in designing robust, efficient, and quantum-resistant authentication mechanisms for next-generation vehicular networks. © 2025 ieee.

关键词： Vehicular ad hoc networks

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Big Data Analytics in bioinformatics: Transforming Genomics and Proteomics for Advanced Biological Insights

Big Data Analytics in Bioinformatics: Transforming Genomics ...

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2024 International conference on Big Data Analytics in bioinformatics, DABCon 2024

作者： Vohra, D.S. Roorkee Geomatics Division RoorkeePC-247667 India

ISBN: (纸本)9798350378047

The integration of big data analytics into bioinformatics has revolutionized biological research and healthcare. This paper explores the methodologies, tools, and big data analytics use cases in bioinformatics, focusing on genomics, proteomics, and systems biology. Key methodologies include data collection and storage, preprocessing, and advanced data driven approaches like machine learning, statistical analysis, and visual analytics. Applications in genomics involve association studies across the genome and identification of gene diseases, while proteomics benefits from mass spectrometry data analysis. systems biology leverages integrated data for comprehensive models of biological net/orks. Challenges such as data integration, computational complexity, and ethical concerns are discussed, alongside future directions involving advanced algorithms, improved data integration, and secure data-sharing technologies. This interdisciplinary domain holds immense potential for advancing science and medicine. © 2024 ieee.

关键词： Genome

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A Comparative Analysis of Machine Learning and Deep Learning Approaches in bioinformatics 4

A Comparative Analysis of Machine Learning and Deep Learning...

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4th International conference on Expert Clouds and Applications, ICOECA 2024

作者： Ramar, J. Porkodi, R. Bharathiar University Department of Computer Science Tamilnadu Coimbatore India

ISBN: (纸本)9798350385793

bioinformatics is an interdisciplinary area that combines biology, computer technological know-how, and records to investigate and interpret biological facts. It makes use of computational algorithms, equipment, and databases to understand biological techniques, genetic variation, and relationships among genes, proteins, and different organic molecules. bioinformatics performs a critical role in genomics, structural biology, and drug discovery. bioinformatics uses programming languages like Python, R, and Perl, statistical methods, and device gaining knowledge of techniques to investigate and interpret biological records. All things considered, bioinformatics advances our knowledge of biological systems, helps us comprehend diseases, and helps us create personalized medication and specialized treatment plans. © 2024 ieee.

关键词： Contrastive Learning

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A Closed-Loop Deep Brain Stimulation Biomedical Model of Parkinson's Disease 21

A Closed-Loop Deep Brain Stimulation Biomedical Model of Par...

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21st ieee conference on computational Intelligence in bioinformatics and computational Biology, CIBCB 2024

作者： Petitjean, Timothe Millet, Hugo Araújo, Mariana F. P. Moioli, Renan C. Vargas, Patricia A. Ranieri, Caetano M. Heriot-Watt University School of Mathematical And Computer Science Edinburgh United Kingdom Health Sciences Center Department of Physiological Sciences Vitoria Brazil Bioinformatics Multidisciplinary Environment Digital Metropolis Institute Natal Brazil Institute of Geosciences and Exact Sciences Rio Claro Brazil

ISBN: (纸本)9798350356632

In this paper, we introduce a new approach to the study of Parkinson's Disease (PD) through the implementation of a neurorobotics model via the integration of computational neuroscience with the latest robotic technology. Using the iCub as our robot platform, we adapted a computational model of the Basal Ganglia-Thalamo-Cortical (BG-T-C) circuit to investigate the efficiency of Deep Brain Stimulation (DBS) in mitigating PD's motor *** to our methodology is the use of closed-loop DBS, a technique that adjusts stimulation parameters in real time based on specific kinematic and neuronal biomarkers of PD severity, such as fluctuations in beta band activity and tremor movements. This dynamic approach allows for a more personalized and efficient treatment regimen compared to traditional, static open-loop systems, which cannot adapt to the patient's changing *** findings of our study corroborate the feasibility of using a neurorobotics model to simulate the motor symptoms of PD and provide evidence that closed-loop DBS can effectively modulate these symptoms. This was achieved by reducing the power spectral density at the beta-band frequency range (8-30 Hz) of the neural activity to below threshold levels and revealing a complex relationship between the severity of the disease and the effectiveness of the treatment. © 2024 ieee.

关键词： Neurodegenerative diseases

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AI in bioinformatics and Genomics

AI in Bioinformatics and Genomics

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2024 International conference on Intelligent and Innovative Practices in Engineering and Management, IIPEM 2024

作者： Mukherjee, Sumita Thapliyal, Kavita Maurya, Alka Chaudhary, Naina Sudeshna, Sushree Amity University AIBS Noida India SIIB Pune India Amity University Tashkent Uzbekistan

ISBN: (纸本)9798350390049

Artificial intelligence (AI) has revolutionized various fields, including bioinformatics and genomics, by offering powerful tools and techniques to analyse and interpret complex biological data. This research paper explores the significant role of AI in bioinformatics and genomics, highlighting its applications, advancements, challenges, and future directions. Through a comprehensive review of relevant literature, case studies, and examples, and aims the power of AI transformation to provide insights on the study of biological systems, accelerating advancements in personalized medicine and biotechnology and enhancing our understanding of genomics. These areas have risen from the needs of biologists to utilize and help interpret the vast amounts of data that are constantly being gathered in genomic research. Artificial intelligence (AI) has increasingly gained attention in bioinformatics research and computational molecular biology has become common for the researchers to apply the off-shelf systems with the availability of different types of AI algorithms. At present researchers are facing difficulties in choosing the best method that could be applied to a specific data set, with various intelligent methods available in the literature. The researchers need to apply machine learning and AI with different tools and techniques which present the data in an annotated with context, estimates of accuracy, proper predictions and explanation. comprehensible fashion. This article aims to review the use of AI in the areas of bioinformatics and computational molecular biology (DNA sequencing). The AI in functional genomics, and its application in this paper defines the importance of applicability and interpretability including ethical, legal and economic issues. © 2024 ieee.

关键词： accelerating advancements AI bioinformatics biotechnology complex biological data Genomics

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Single-Cell RNA-Seq Data Clustering: Highlighting computational Challenges and Considerations

Single-Cell RNA-Seq Data Clustering: Highlighting Computatio...

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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

作者： Nossier, Mohamad Moussa, Sherin M. Badr, Nagwa Ain Shams University Faculty of Computer and Information Sciences Program of Bioinformatics Department of Information Systems Cairo11566 Egypt Cairo11837 Egypt Ain Shams University Faculty of Computer and Information Sciences Department of Information Systems Cairo11566 Egypt

ISBN: (纸本)9798350337488

Recent advancements in single-cell transcriptomics have revolutionized data generation, resulting in the production of vast amounts of single-cell RNA sequencing (scRNA-seq) data. The intricate biology of tissues and organs may be better understood through the analysis of such data, which aims to not only identify known cell types but also uncover novel ones. In this paper, we outline the standard single-cell RNA-seq data analytic process with particular focus on the critical role of unsupervised clustering, since it has a significant influence over subsequent analyses and the resulting biological insights. However, navigating the computational landscape for clustering single-cell transcriptomic data poses significant challenges. Thus, in this article, we provide a simplified exploration of key computational challenges and considerations within the realm of scRNA-seq data clustering, emphasizing the application and relevance of selected unsupervised methods in current research, while acknowledging the potential for further investigation of the biological aspects in this field. Furthermore, we present recommendations on how such challenges may be addressed. This work not only sheds light on the complexities of scRNA-seq data cluster analysis, but also provides insights into how to effectively address and surmount these challenges. © 2023 ieee.

关键词： Clustering Data Analysis RNA-sequencing Single-cell Transcriptomics Unsupervised learning

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Aggregating Structural Features for computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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2023 International conference on Informatics, Multimedia, Cyber and Information systems, ICIMCIS 2023

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Isnan, Mahmud Trinugroho, Joko Pebrianto Pardamean, Bens Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Binus Graduate Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features. © 2023 ieee.

关键词： Proteins

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Assessing Tree-Based Phenotype Prediction on the UK Biobank

Assessing Tree-Based Phenotype Prediction on the UK Biobank

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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

作者： Melendez, Alex Lopez, Cayetana Bonet, David Sant, Gerard Montserrat, Daniel Mas Abante, Jordi Rivas, Manuel Marques, Ferran Ioannidis, Alexander G. Universitat Politècnica de Catalunya Barcelona Spain Stanford University StanfordCA United States Universitat de Barcelona Barcelona Spain

ISBN: (纸本)9798350337488

Precision medicine relies on the ability to identify associations between genomic data and its phenotypic expression in order to provide personalized predictions. Phenotype prediction using statistical models trained on large-scale genomic and phenotypic data is a critical research area at the intersection of machine learning and genomics. Current genotype-to-phenotype models, such as polygenic risk scores, only account for linear relationships, and the use of nonlinear methods is still partially unexplored. In this work, we evaluate the prediction accuracy and scalability of nine nonlinear decision tree-based algorithms, including ensembling and boosting mechanisms, and compare them to linear prediction models. We assess the prediction performance for 24 anthropometric and disease-related phenotypes present in the UK Biobank. By using random feature selection, we explore how accuracy and computational time vary for each method as a function of the number of genetic variants selected. Our results show that tree-based methods, especially gradientboosted trees, can offer superior predictions with computational times comparable to those of linear methods. Thus, models able to capture nonlinear relationships between genotypes and phenotypes merit consideration for integration in upcoming computational systems for personalized medicine. © 2023 ieee.

关键词： bioinformatics Machine Learning Phenotype Prediction Precision Medicine Tree-based Methods

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The M² Pipeline: A Novel Tool for Rapidly Analyzing Host Metabolic Profiles from RNA-seq Data 11

The M<SUP>2</SUP> Pipeline: A Novel Tool for Rapidly Analyzi...

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11th International conference on bioinformatics and computational Biology (ICBCB)

作者： Baker, Ryan Bassaganya-Riera, Josep Tubau-Juni, Nuria Leber, Andrew J. Hontecillas, Raquel NIMML Inst Blacksburg VA 24060 USA

ISBN: (纸本)9798350397871

A novel bioinformatics and data analytics pipeline that utilizes global gene expression datasets in combination with advanced computational modeling of metabolic pathways has been applied to predicting the metabolic profiles during viral infections. The Modeling Metabolism (M2) pipeline was used to analyze and compare complex metabolic profiles of four viral respiratory infections (rhinovirus infection, flu, COVID-19, and MERS-CoV infection) based on host RNA-seq datasets. During infection, multiple factors can affect the metabolic state of the host. Metabolic changes are biomarkers of host antiviral responses and viral pathogenesis. Therefore, analyzing the metabolic response to infectious diseases might provide valuable mechanistic insights into host-pathogen interactions and highlight metabolic changes that play important roles in disease pathogenesis. By using the M2 pipeline, we predicted that influenza virus, SARS-CoV-2, and MERS-CoV infections resulted in increased glycolytic activities and a reduced capacity for oxidative phosphorylation. Furthermore, SARS-CoV-2 and MERS-CoV infections both presented dysregulated pentose phosphate pathways (PPP), while influenza infection elicited an increase in PPP activity in correlation with disease severity and mortality. Notably, rhinovirus infection, the mildest respiratory infection studied, had little effect on the overall perturbation of host cellular metabolism. The M2 pipeline provides a rapid, comprehensive, and systems-wide analysis of metabolic profiles from host response RNA-seq datasets during emerging and re-emerging infections.

关键词： bioinformatics computational Biology Metabolism Infectious Disease Immunology RNA-seq

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