The field molecular pathway analysis evolves rapidly, and many progressive methods have recently been discovered. Molecular Pathway Analysis Using High-Throughput OMICS Data contains the largest collections of molecular pathways. For the first time, guidelines on how to do genomic, epigenetic, transcriptomic, proteomic, and metabolomic data analysis in real-world research practice are given. Molecular Pathway Analysis Using High-Throughput OMICS Molecular Data also focuses on the pathway analysis applications for solving tasks in biotechnology, pharmaceutics, and molecular diagnostics ??It demonstrates how pathway analysis can be applied for the research and treatment of chronic and acute diseases, for next-generation molecular diagnostics, for drug design and preclinical testing; relevant real-world examples, molecular tests, and web resources will be reviewed in-depth.? ??The book shows a tendency of erasing the borders between chemistry, physics, informatics, mathematics, biology, and medicine by means of novel research approaches and instruments, providing a truly multidisciplinary approach.

Table of Contents:
Contributors Preface PART I: Foundational information Chapter 1: Past, current, and future of molecular pathway analysis Anton Buzdin, Alexander Modestov, Daniil Luppov and Ira-Ida Skvortsova 1.1. Molecular pathways 1.2. Quantitative omics data 1.3. Different levels of omics data analysis 1.4. Quantization of IMP activities 1.4.1. Annotation of functional roles for pathway participants 1.5. Applications of IMP analysis 1.5.1. Applications in medicine 1.6. Software for quantitative assessment of IMP activation 1.7. Concluding remarks References Chapter 2: Molecular data for the pathway analysis Xinmin Li and Anton Buzdin 2.1. Omics data available for the molecular pathway analysis 2.2. Data needed to reconstruct IMPs 2.3. Data needed to estimate activation levels of IMPs References Chapter 3: Benefits and challenges of OMICS data integration at the pathway level Nicolas Borisov and Maksim Sorokin 3.1. Background 3.2. The comparison 3.2.1. Functional annotation of gene expression data 3.2.2. Statistical tests 3.2.3. Mathematical modeling 3.2.4. Analysis of gene expression datasets 3.2.5. Biological relevance of cross-platform harmonized expression data 3.2.6. Marker gene and pathway analysis 3.3. Results 3.3.1. Cross-platform processing of transcriptomic and proteomic data 3.3.2. Building pathway activation profiles and assessment of batch effects 3.3.3. Mathematical modeling of data aggregation effects 3.3.4. Experimental model of cross-platform comparisons 3.3.5. Data aggregation effects assessed for RNA and protein expression levels 3.3.6. Comparison of data aggregation capacities of different PAL scoring methods 3.3.7. Retention of biological features 3.3.8. Gene and pathway analysis of PTSD datasets 3.4. Discussion Abbreviations References Chapter 4: Controls for the molecular data: Normalization, harmonization, and quality thresholds Nicolas Borisov 4.1. Background 4.2. Principles of harmonization algorithms 4.3. Differential clustering of human normal and cancer expression profiles 4.4. Correlation, regression, and sign-change analysis of cancer drug balanced efficiency score (BES) after application of different methods of harmonization 4.5. Discussion Abbreviations References Chapter 5: Reconstruction of molecular pathways Anton Buzdin and Maksim Sorokin 5.1. Molecular pathways 5.2. An approach to reconstruct the pathway 5.2.1. The interactome model 5.2.2. Building gene-centric pathways 5.2.3. Overall functional annotation of reconstructed pathwaysdgene ontology classification 5.2.4. Visual annotation of reconstructed pathways 5.2.5. Algorithmic annotation of functional roles for pathway components 5.2.6. Examples of building and annotation of molecular pathways References Chapter 6: Qualitative and quantitative molecular pathway analysis: Mathematical methods and algorithms Nicolas Borisov, Stella Liberman-Aronov, Igor Kovalchuk and Anton Buzdin 6.1. Background 6.2. Topology-based methods for pathway activation assessment 6.2.1. Oncobox 6.2.2. Topology analysis of pathway phenotype association 6.2.3. Topology-based score 6.2.4. Pathway-express 6.2.5. Signal pathway impact analysis 6.2.6. iPANDA (in silico pathway activation network decomposition analysis) 6.3. Methods for database preparation for pathway activation assessment 6.3.1. Curation of pathway databases 6.3.2. Algorithmic annotation of pathway graph nodes 6.3.3. Finding gene importance factors for iPANDA 6.4. Personalized ranking of cancer drugs based on PALs 6.4.1. Oncobox balance efficiency score (BES) 6.4.2. Drug efficiency index (DEI) 6.5. Multi-omics data pathway analysis 6.5.1. Pathway activation assessment for methylome, microRNAs, and long noncoding (LNC) antisense (AS) RNAs 6.6. Concluding remarks Abbreviations References Further reading PART II: Methods and guidelines Chapter 7: Getting started with the molecular pathway analysis Anton Buzdin and Xinmin Li 7.1. Strategies of pathway analysis 7.2. Reconstruction of pathways and networks 7.3. The devil is in the things 7.4. Applications of molecular pathway analysis 7.5. Preprocessing of data for pathway analysis 7.6. Visualization of pathways References Chapter 8: Molecular pathway analysis using comparative genomic and epigenomic data Ye Wang, Marianna Zolotovskaia and Anton Buzdin 8.1. Types of pathway analysis requiring (epi)genomic data 8.2. Profiling of genomic pathway instability by using DNA mutation data 8.2.1. Initial mutation data 8.2.2. Algorithm validation dataset 8.2.3. Molecular target interrogation dataset 8.2.4. Clinical trial data 8.2.5. Molecular pathway data 8.2.6. Pathway instability scoring 8.2.7. PI analysis of cancer mutation signatures 8.2.8. PI-based drug scoring 8.2.9. Assessment of MDS family methods performance using clinical trial data 8.2.10. Application of MDS to identify putative drug target genes 8.3. Epigenetic marks as the measure of IMP molecular evolution 8.3.1. Study design 8.3.2. Source IMPs 8.3.3. Aggregated dN/dS data 8.3.4. RE regulation enrichment data 8.3.5. Functional classification of histone modifications 8.3.6. Aggregated NGRE score 8.3.7. Correlation between structural and regulatory evolutionary rate metrics 8.3.8. Functional groups of genes and pathways with different evolutionary rates 8.4. Concluding remarks References Chapter 9: Quantitative molecular pathway analysis using transcriptomic and proteomic data Anton Buzdin, Sergey Moshkovskii and Maksim Sorokin 9.1. Types of molecular pathway analysis 9.2. Quantitative analysis of gene expression 9.3. Quantitative assessment of the pathway activities 9.3.1. Calculation of PAL 9.3.2. Annotation of functional roles of IMP members 9.4. Software 9.4.1. Visualization of the pathways 9.4.2. Manual on the installation of oncoboxlib library References Chapter 10: MicroRNA data for quantitative analysis of molecular pathways Anton Buzdin and Alina Artcibasova 10.1. Relevance of microRNA profiles to molecular pathway activation analysis 10.2. Algorithmic analysis of pathway activation 10.3. Applications of pathway analysis for microRNAs 10.3.1. MiRImpact application to profile regulation of IMPs in bladder cancer 10.3.2. MiRImpact application to profile regulation of IMPs during cytomegaloviral infection 10.4. Concluding remarks References Chapter 11: Methods and tools for OMICS data integration Ilya Belalov and Xinmin Li 11.1. A snapshot of the current state of OMICS integration landscape 11.2. The most important part of this chapter 11.3. Best practices in preprocessing multiomics datasets 11.4. OMICS data integration in the eyes of a life scientist 11.4.1. From genotype to phenotype: Step I-Transcription 11.4.2. From genotype to phenotype: Step II-Translation 11.4.3. From genotype to phenotype: Step III-Proteins 11.4.4. From genotype to phenotype: Step IV-Metabolites 11.5. Data scientist summary 11.6. Life scientist summary References Further reading PART III: Practical applications Chapter 12: Molecular pathway approach in clinical oncology Anton Buzdin, Alexander Seryakov, Marianna Zolotovskaia, Maksim Sorokin, Victor Tkachev and Alf Giese 12.1. Gene expression data in clinical oncology 12.2. Conversion of pathway activation data into personalized prediction of cancer drug efficacy 12.2.1. Molecular pathway databank 12.2.2. Clinical trial database 12.2.3. Drug target database 12.2.4. Algorithmic scoring of cancer drug efficiencies 12.3. Examples of IMP-based clinical ranking of drugs in oncology 12.3.1. Example 1. Ranking of cancer drugs based on mRNA expression data 12.3.2. Example 2. Comparison of alternative drug scoring methods 12.4. Conclusion References Chapter 13: Molecular pathway approach in pharmaceutics Anton Buzdin, Teresa Steinbichler and Maksim Sorokin 13.1. Molecular pathway analysis in general 13.1.1. What is intracellular molecular pathway 13.1.2. Molecular pathway analysis 13.1.3. Pathway analysis instruments 13.2. Pathway analysis to facilitate tasks in molecular pharmacology 13.2.1. Task 1. To establish mechanism of action of drug candidate X 13.2.2. Task 2. To identify robust response biomarkers for drug (candidate) X 13.2.3. Task 3. To identify drugs that act similarly to drug (candidate) X or to identify molecular targets of X 13.3. Practical examples how IMP analysis may help 13.4. Useful online resources 13.5. Conclusion References Chapter 14: Molecular pathway approach in biotechnology Anton Buzdin, Denis Kuzmin and Ivana Jovcevska 14.1. Pathways of biotechnology 14.1.1. Biotechnology 14.1.2. The pathways 14.1.3. Molecular pathways in biotech 14.2. Examples of pathway analysis in biotechnology 14.2.1. Golden rice 14.2.2. A humanized N-glycosylation system for expression of human proteins in yeast 14.2.3. Optimization of the photosynthesis system 14.3. Conclusion and perspective References Chapter 15: Molecular pathway approach in biology and fundamental medicine Anton Buzdin, Ye Wang, Ivana Jovcevska and Betul Karademir-Yilmaz 15.1. Molecular pathway analysis in biomedicine 15.2. IMP analysis in oncology 15.2.1. IMPs in cancer 15.2.2. Quantitative analysis of IMPs in oncology 15.2.3. IMPs as cancer biomarkers 15.2.4. Pathway-based scoring of cancer drug efficiencies 15.3. Other applications of IMP analysis in biomedicine 15.3.1. Ranking and repurposing of drugs 15.3.2. Understanding molecular mechanisms 15.4. Conclusion References