"Bioinformatics and Biomedical Engineering" - Information and Links:

Bioinformatics and Biomedical Engineering

10th International Work-Conference, IWBBIO 2023, Meloneras, Gran Canaria, Spain, July 12-14, 2023 Proceedings, Part II

"Bioinformatics and Biomedical Engineering" was published by Springer in 2023 - Cham, it has 1 pages and the language of the book is English.


“Bioinformatics and Biomedical Engineering” Metadata:

  • Title: ➤  Bioinformatics and Biomedical Engineering
  • Authors:
  • Language: English
  • Number of Pages: 1
  • Publisher: Springer
  • Publish Date:
  • Publish Location: Cham

“Bioinformatics and Biomedical Engineering” Subjects and Themes:

Edition Specifications:

  • Pagination: xxi, 508

Edition Identifiers:

AI-generated Review of “Bioinformatics and Biomedical Engineering”:


"Bioinformatics and Biomedical Engineering" Description:

Open Data:

Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Feature Selection, Extraction, and Data Mining in Bioinformatics -- Agent Based Modeling of Fish Shoal Behavior -- 1 Introduction -- 2 Modeling -- 3 Methods -- 3.1 Multi agents Based Modeling and Simulation -- 4 Results and Discussion -- References -- Entropy Approach of Processing for Fish Acoustic Telemetry Data to Detect Atypical Behavior During Welfare Evaluation -- 1 Introduction -- 1.1 Biotelemetry -- 1.2 Fish Welfare -- 2 Dataset -- 3 Methods -- 4 Results -- 5 Conclusion -- References -- Determining HPV Status in Patients with Oropharyngeal Cancer from 3D CT Images Using Radiomics: Effect of Sampling Methods -- 1 Introduction -- 2 Material and Methods -- 2.1 Data Set -- 2.2 Image Pre-processing -- 2.3 Feature Extraction -- 2.4 Data Pre-processing and Resampling -- 2.5 Feature Selection -- 2.6 Model Training and Evaluation -- 3 Results -- 3.1 Data Pre-processing and Resampling -- 3.2 Feature Extraction -- 3.3 Feature Selection -- 3.4 Performance Evaluation -- 4 Discussion -- 5 Conclusion -- References -- MetaLLM: Residue-Wise Metal Ion Prediction Using Deep Transformer Model -- 1 Introduction -- 2 Methodology -- 2.1 MetaLLM: Residue-Wise Metal Ion Prediction -- 3 Experiments and Results -- 3.1 Experimental Details -- 3.2 Result Analysis -- 4 Conclusion -- References -- Genome-Phenome Analysis -- Prediction of Functional Effects of Protein Amino Acid Mutations -- 1 Introduction -- 2 Methods -- 2.1 nsSNV Datasets -- 2.2 Protein Mutation Prediction Methodology: The Holdout- nsSNV Algorithm -- 2.3 Consensus Holdout Training and Selection -- 2.4 Extreme Learning Machine -- 2.5 Random Forests -- 3 Results -- 4 Conclusions and Future Directions -- References -- Optimizing Variant Calling for Human Genome Analysis: A Comprehensive Pipeline Approach -- 1 Introduction

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