Intro
Preface
Organization
Contents
Computational Advances in Bio and Medical Sciences
Single Model Quality Estimation of Protein Structures via Non-negative Tensor Factorization
1 Introduction
2 Related Works
3 Methodology
3.1 Stage I: From Structures to Groups
3.2 Stage II: Ranking Groups
3.3 Stage III: Partitioning Groups into Subgroups
3.4 Stage IV: Scoring Each Structure
3.5 Experimental Setup
3.6 Dataset
3.7 Evaluation Metrics
4 Results
4.1 Comparative Evaluation on Correlation with TM-Score
4.2 Loss-Based Comparison

4.3 Statistical Significance Analysis
5 Conclusion
References
Graph Representation Learning for Protein Conformation Sampling
1 Introduction
1.1 Related Work
2 Methods
3 Results
3.1 Experimental Setup
3.2 Evaluation of Models on Fixed-Length Chains
3.3 Evaluation of Models on Variable-Length Chains
4 Conclusion
References
Excerno: Filtering Mutations Caused by the Clinical Archival Process in Sequencing Data
1 Introduction
2 Excerno: A Bayes Classifier Using Mutational Signatures
3 Simulation and Evaluation Approach
4 Simulation Results

4.1 Performance Characteristics Across Different COSMIC Baseline Signatures
4.2 Performance Characteristics Across Different Percentages of FFPE
5 Conclusions
References
Relabeling Metabolic Pathway Data with Groups to Improve Prediction Outcomes
1 Introduction
2 Method
2.1 Feed-Forward Phase
2.2 Feed-Backward Phase
2.3 Closing the Loop
3 Experiments
3.1 Accumulated History Probability Analysis
3.2 Metabolic Pathway Prediction
4 Conclusion
References
MELEPS: Multiple Expert Linear Epitope Prediction System
1 Introduction
2 Materials and Methods

2.1 Data Collection
2.2 The System Flow of MELEPS
2.3 Integrated Multi-expert Recommendation Methodology
2.4 The Weighted Recommendation Score
2.5 Performance Measurement of Recommendation
3 Results and Discussion
3.1 The Weight Parameter Table
3.2 Performance of MELEPS
3.3 The MELEPS Platform
4 Conclusion
References
Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation
1 Introduction
2 Related Work
2.1 Major Vessel Segmentation
2.2 Full Coronary Tree Segmentation
2.3 Catheter and Full Coronary Tree Segmentation

2.4 Other Segmentation Criteria
3 Model Evaluation Metrics
4 Loss Function
5 Architecture
5.1 Encoder Comparison
5.2 Decoder Comparison
5.3 EfficientUNet++ Architecture
5.4 Performance vs. Computation Trade-Off
6 Experimental Results
7 Implementation Details
7.1 Training Methodology
7.2 Dataset
7.3 Data Augmentation
8 Discussion and Future Work
References
Unified SAT-Solving for Hard Problems of Phylogenetic Network Construction
1 Introduction: Evolutionary Trees and Phylogenetic Networks
2 Definitions and Problem Statements