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Table of contents
Acknowledgment
Abstract
1 Introduction
1.1 Background and rationale
1.1.1 Context
1.1.2 Exploring microbial environments with metagenomics
1.1.3 Metagenomics in precision medicine
1.1.4 Overview of different sequencing technologies
1.1.5 Bioinformatics workflows to analyze metagenomic data
1.1.6 Classification models in metagenomics
1.2 Research problem and contributions
1.2.1 Objectives
1.2.2 Deep learning based approach and point of care
1.2.3 Building interepretable signatures based on Subgroup Discovery
1.2.4 Scientific mediation
2 Experimental methods and design
2.1 Survey of existing metagenomics datasets
2.2 Simulating metagenomic datasets
2.2.1 Datasets used to train embeddings and taxa classifier
2.2.2 Datasets to learn the disease prediction tasks
2.3 Introduction of the IDMPS database
2.4 Code implementation
2.4.1 State-of-the-art classifiers
2.4.2 End-to-end deep learning for disease classification from metagenomic data
2.4.3 Generate statistically credible subgroups for interpretable metagenomic signature
2.5 Conclusion
3 End-to-end deep learning for disease classification from metagenomic data
3.1 The representation of metagenomic data
3.2 State of the art
3.2.1 Machine learning models from nucleotide one hot encoding .
3.2.2 Machine learning models from DNA embeddings
3.2.3 Learning from multiple-instance representation of reads
3.3 Metagenome2Vec: a novel approach to learn metagenomes embeddings
3.3.1 kmer2vec: learning k-mers embeddings
3.3.2 read2vec: learning read embeddings
3.3.3 read2genome: reads classification
3.3.4 metagenome2vec: learning metagenome embeddings
3.4 Experiments and Results
3.4.1 Reference Methods compared to metagenome2Vec
3.4.2 Results of the Disease prediction tasks
3.5 Conclusion
4 Generate statistically credible subgroups for interpretable metagenomic signature
4.1 Introduction
4.1.1 Subgroup analysis in clinical research
4.1.2 Subgroup discovery: two cultures
4.1.3 Limits of current SD algorithms for clinical research
4.2 Q-Finder’s pipeline to increase credible findings generation
4.2.1 Basic definitions: patterns, predictive and prognostic rules .
4.2.2 Preprocessing and Candidate Subgroups generation in Q-Finder
4.2.3 Empirical credibility of subgroups
4.2.4 Q-Finder subgroups diversity and top-k selection
4.2.5 Possible addition of clinical expertise
4.2.6 Subgroups’ generalization credibility
4.2.7 Experiments and Results
4.3 Applications to metagenomics for phenotype status prediction .
4.3.1 Overview and concepts of the Q-Classifier
4.3.2 Statistical metrics and optimal union
4.3.3 Rejection and delegation concepts to adapt SD for prediction
4.3.4 Benchmark on real-world and simulated metagenomic data .
4.4 Conclusion
5 Conclusion and perspectives
5.1 Summary of contributions
5.2 Methodological assessment
5.3 Perspectives for future works
Bibliography
Figures list
A Appendix
A.1 Multiple instance learning
A.1.1 Beam search strategy using decision tree versus exhaustive algorithm
A.2 Comparision of Q-Classifier with or without cascaded combination of state-of-the-art classifiers
