Сулимов Павел Андреевич Обучение генеративных вероятностных моделей для распознавания данных масс-спектрометрии

brief description

Сулимов Павел Андреевич Обучение генеративных вероятностных моделей для распознавания данных масс-спектрометрии
ОГЛАВЛЕНИЕ ДИССЕРТАЦИИ
кандидат наук Сулимов Павел Андреевич
Contents

1 Introduction

1.1 The relevance of research

1.2 Aims and objectives of research

1.3 Importance of work

1.4 Publications

1.5 The organization of the thesis

2 Mass spectrometry

2.1 Biological sample preparation

2.2 Mass spectrometer and data generation

2.3 Database searching-based spectrum identification

2.3.1 Peptide database

2.3.2 Candidate peptides

2.3.3 Score functions

2.4 Search result validation

2.4.1 Decoy peptides

2.4.2 FDR estimation

2.4.3 Q-value calculation

2.5 De Novo and hybrid spectrum identification methods

3 Scoring functions properties

3.1 Discriminative property

3.2 Calibration property

3.3 Unbiasedness property

3.4 Universality property

3.5 Learning new score functions

4 Summary of the thesis articles

4.1 Training of BoltzMatch

4.2 Diversifying regularization

4.3 Evaluation of BoltzMatch in spectrum identification

4.4 PSM score calibration with Tailor methods

4.4.1 The main results of BoltzMatch in spectrum annotation

4.5 Interpretation of BoltzMatch

4.6 Biased score functions

4.6.1 Bias test of BoltzMatch

5 Conclusions

Acknowledgments

List of abbreviations and conventions

Bibliography

List of figures

List of tables

List of algorithms

Appendices

A Article "Annotation of tandem mass spectrometry data using stochastic neural networks in shotgun proteomics"

B Article "Guided Layer-wise Learning for Deep Models using Side Information"

C Article "Tailor: A Nonparametric and Rapid Score Calibration Method for Database Search-Based Peptide Identification in Shotgun Proteomics"

D Article "Bias in False Discovery Rate Estimation in Mass-Spectrometry-Based Peptide Identification"