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.
- Provides theoretical insights, links to available resources and their descriptions, and protocols related to multiple possibilities and options of the molecular pathway analysis
- Elucidates unique instruments (i) for the quantitative pathway analysis using metabolomic data, and (ii) for algorithmic hypothesis-free reconstruction and functional annotation of the molecular pathways that have a strong potential to revolutionize the field
- Includes intuitive practical guidelines on the analysis of genomic, epigenetic, transcriptomic, proteomic, and metabolomic data at the molecular pathway level for non-bioinformaticians
- Provides state-of-the art in the field of Big molecular data analysis for research, medicine, biotechnology, pharmaceutics, and next-generation molecular diagnostics
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978-0-443-15569-7 (9780443155697)
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ContributorsPrefacePART I: Foundational informationChapter 1: Past, current, and future of molecular pathway analysisAnton Buzdin, Alexander Modestov, Daniil Luppov and Ira-Ida Skvortsova1.1. Molecular pathways1.2. Quantitative omics data1.3. Different levels of omics data analysis1.4. Quantization of IMP activities 1.4.1. Annotation of functional roles for pathway participants1.5. Applications of IMP analysis 1.5.1. Applications in medicine1.6. Software for quantitative assessment of IMP activation1.7. Concluding remarksReferencesChapter 2: Molecular data for the pathway analysisXinmin Li and Anton Buzdin2.1. Omics data available for the molecular pathway analysis2.2. Data needed to reconstruct IMPs2.3. Data needed to estimate activation levels of IMPsReferencesChapter 3: Benefits and challenges of OMICS data integration at the pathway levelNicolas Borisov and Maksim Sorokin3.1. Background3.2. 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 analysis3.3. 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 datasets3.4. DiscussionAbbreviationsReferencesChapter 4: Controls for the molecular data: Normalization, harmonization, and quality thresholdsNicolas Borisov4.1. Background4.2. Principles of harmonization algorithms4.3. Differential clustering of human normal and cancer expression profiles4.4. Correlation, regression, and sign-change analysis of cancer drug balanced efficiency score (BES) after application of different methods of harmonization4.5. DiscussionAbbreviationsReferencesChapter 5: Reconstruction of molecular pathwaysAnton Buzdin and Maksim Sorokin5.1. Molecular pathways5.2. 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 pathwaysReferencesChapter 6: Qualitative and quantitative molecular pathway analysis: Mathematical methods and algorithmsNicolas Borisov, Stella Liberman-Aronov, Igor Kovalchuk and Anton Buzdin6.1. Background6.2. 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 iPANDA6.4. 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) RNAs6.6. Concluding remarksAbbreviationsReferencesFurther readingPART II: Methods and guidelinesChapter 7: Getting started with the molecular pathway analysisAnton Buzdin and Xinmin Li7.1. Strategies of pathway analysis7.2.
