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
2 Background
3 Methods
3.1 Reference
3.2 Dataset
3.3 Quality and Control
3.4 Pipeline
3.5 Workflow Management and Reproducibility
3.6 Benchmarking
3.7 Computational Resources
4 Results
4.1 Different Methods Performance
4.2 Computational Time
5 Discussion
6 Conclusion
References
Healthcare and Diseases
Improving Fetal Health Monitoring: A Review of the Latest Developments and Future Directions
1 Introduction
2 Methods
2.1 Study Design

2.2 Search Strategy
2.3 Inclusion Criteria
2.4 Selection of Studies
2.5 Data Analysis
3 Result
4 Discussion
4.1 The Development of Monitoring Devices for Fetal Well-Being
4.2 Algorithm for More Accurate Maternal-Fetal FHR Filtering
4.3 Fetal Well-Being Indicators
4.4 Target Users
5 Conclusion
References
Deep Learning for Parkinson's Disease Severity Stage Prediction Using a New Dataset
1 Introduction
2 Related Work
3 Materials and Methods
3.1 Data Acquisition
3.2 Dataset Construction
3.3 Data Pre-processing
3.4 Proposed LSTM Model