MEDICAL APPLICATIONS OF LASER MOLECULAR

ISBN-13: 9781510645349 / Miękka / 2021

KISTENEV BORISOV

Spis treści

• Preface
• 1 Fundamental Concepts Related to Laser Molecular Imaging
• Introduction
• 1.1 Molecular Biomarkers
• 1.1.1 Biomarker conception
• 1.1.2 Groups of molecular ""omics"" biomarkers
• 1.1.3 Pattern-recognition approach for metabolic profiling
• 1.1.4 Biological specimens for noninvasive diagnostics
• 1.2 Basics of Laser Molecular Spectroscopy and Imaging
• 1.2.1 Molecule absorption spectra
• 1.2.2 Raman scattering spectra
• 1.2.3 Fluorescence spectra
• 1.2.4 Molecular imaging
• 1.3 Basics of Machine Learning
• Conclusion
• References
• 2 Laser-based Molecular Data-Acquisition Technologies
• Introduction
• 2.1 Data-Acquisition Technologies Suitable for Breath Biopsy
• 2.1.1 The aim of data acquisition by breathomics
• 2.1.2 Nonoptical experimental methods for breathomics
• 2.1.3 Breath air sampling
• 2.1.4 Laser absorption spectroscopy
• 2.1.5 Fluorescence spectroscopy
• 2.2 Data Acquisition Technologies Suitable for Optical Liquid Biopsy
• 2.2.1 Possible optical modes for liquid sample analysis
• 2.2.2 Data acquisition using unprocessed or drying liquid samples
• 2.3 Data Acquisition Technologies Suitable for Optical Tissue Biopsy
• 2.3.1 Experimental methods for nonoptical tissue biopsy
• 2.3.2 Interaction of laser radiation with a tissue
• 2.3.3 Possible experimental laser spectroscopy methods for in vivo tissue optical biopsy
• 2.3.4 Possible experimental laser molecular imaging methods for in vivo tissue optical biopsy
• Conclusion
• References
• 3 Informative Feature Extraction
• Introduction
• 3.1 Feature Selection
• 3.1.1 Univariate methods of feature selection
• 3.1.2 Multivariate methods of feature selection
• 3.2 Feature Extraction
• 3.3 Outliers and Noise Reduction
• 3.3.1 Outlier removal
• 3.3.2 Noise reduction by signal filtration
• Conclusion
• References
• 4 Clusterization and Predictive Model Construction
• Introduction
• 4.1 Unsupervised Learning Methods: Clusterization
• 4.1.1 K-means algorithm
• 4.1.2 Density-based spatial clustering of applications with noise (DBSCAN)
• 4.1.3 Markov clusterization algorithm (MCL)
• 4.2 Predictive Model Construction
• 4.2.1 Linear discriminant analysis (LDA)
• 4.2.2 K-nearest neighbors (KNN)
• 4.2.3 Partial least squared discriminant analysis (PLS-DA)
• 4.2.4 Soft independent modeling of class analogy (SIMCA)
• 4.2.5 Naive Bayes
• 4.2.6 Support vector machine (SVM)
• 4.2.7 Multi-class decision rules based on binary classifiers
• 4.2.8 A random forest
• 4.2.9 Artificial neural networks
• 4.2.10 Extreme learning machine (ELM)
• 4.2.11 Deep learning neural networks
• 4.2.12 Improving prediction models; ensemble learning
• 4.2.13 Predictive model validation
• Conclusion
• References
• 5 Medical Applications
• Introduction
• 5.1 Breath Optical Biopsy by Laser Absorption Spectroscopy and Machine Learning
• 5.1.1 Machine learning pipeline for chemical-based breathomics
• 5.1.2 Machine learning pipeline for ""profiling""-based breathomics
• 5.2 Liquid Optical Biopsy by IR and THz Laser Spectroscopy and Machine Learning
• 5.2.1 Calibration and pre-processing
• 5.2.2 Chemical-based liquid optical biopsy data modeling by machine learning
• 5.2.3 ""Profiling""-based liquid optical biopsy data modeling by machine learning
• 5.3 Tissue Optical Biopsy Using Laser Molecular Imaging and Machine Learning
• 5.3.1 Calibration and pre-processing
• 5.3.2 Tissue optical biopsy data modeling using machine learning
• Conclusion
• References
• Supplemental Materials
• Index