Intro
Preface
Organization
Contents
Main Track
The Impact of Schizophrenia Misdiagnosis Rates on Machine Learning Models Performance
1 Introduction
2 Methods
2.1 Data Description and Quality Control
2.2 Genotype-Phenotype Association
2.3 Machine Learning Models
2.4 Over-Representation Analysis
3 Results
3.1 Association Test
3.2 Test on Train Data
3.3 Filtering of Discordant Samples
3.4 Classification Model
4 Discussion
5 Conclusion
References

Deep Learning and Transformers in MHC-Peptide Binding and Presentation Towards Personalized Vaccines in Cancer Immunology: A Brief Review
1 Introduction
2 Methodology
3 Input Encoding
4 Deep Learning and Transformers Methods
4.1 Deep Learning
4.2 Transformers
5 Discussion
References
Auto-phylo: A Pipeline Maker for Phylogenetic Studies
1 Introduction
2 Material and Methods
3 Results
3.1 Auto-phylo Modules
3.2 Setting up an Auto-phylo Pipeline
3.3 Bacterial AOs May Have a Function Similar to Animal GULOs

3.4 Identification of Bacterial Species Groups that Have AOs Closely Related to Animal GULOs
4 Conclusion
References
Feature Selection Methods Comparison: Logistic Regression-Based Algorithm and Neural Network Tools
1 Introduction
1.1 Classification Problem
1.2 Feature Selection Methods
2 Methods and Materials
2.1 Logistic Regression-Based Algorithm
2.2 Neural Networks Approach
2.3 Materials
3 Results
3.1 Logistic Regression-Based Algorithm
3.2 Neural Networks Approach
3.3 Results Comparison
4 Conclusions
References

A New GIMME-Based Heuristic for Compartmentalised Transcriptomics Data Integration
1 Introduction
2 Methods
2.1 Flux Balance Analysis
2.2 Gene Inactivity Moderated by Metabolism and Expression
2.3 Implementation of the Proposed Method
2.4 The Model
2.5 The Dataset
3 Results
3.1 Case Studies
4 Discussion and Conclusions
References
Identifying Heat-Resilient Corals Using Machine Learning and Microbiome
1 Introduction
2 Related Work
3 Methods
3.1 Pipeline
3.2 Experimental Setup
4 Results
5 Analysis and Discussion
6 Conclusion
References

Machine Learning Based Screening Tool for Alzheimer's Disease via Gut Microbiome
1 Introduction
2 Related Work
3 Methodology
4 Experimental Analysis
4.1 Experimental Settings
4.2 Experimental Results
4.3 Discussion
5 Conclusion and Future Work
References
Progressive Multiple Sequence Alignment for COVID-19 Mutation Identification via Deep Reinforcement Learning
1 Introduction
2 Methodology
2.1 Progressive Deep Reinforcement Learning
2.2 Sequence Alignment
3 Result and Discussion
3.1 Analysis of Alignment Results
4 Conclusion
References