Geschäftskunden Kundenprogramme
Vor Ort
Merkzettel Warenkorb
Produktbild: Molecular Pathway Analysis Using High-Throughput Omics Molecular Data

Molecular Pathway Analysis Using High-Throughput Omics Molecular Data Analysis of Molecular Pathway Composition, Architecture, and Activation Using High-Throughput Genomic, Epigenetic, Transcriptomic, Proteomic, and Metabolomic Data

Fr. 209.00

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

29.10.2024

Herausgeber

Anton Buzdin

Verlag

Elsevier

Seitenzahl

408

Maße (L/B/H)

23.4/19.1/2.3 cm

Gewicht

776 g

Sprache

Englisch

ISBN

978-0-443-15568-0

Beschreibung

Portrait

Anton Buzdin is professor and head of laboratories at Sechenov First Moscow Medical University, Moscow Institute of Physics and Technology, and Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry. He was elected as the chair of Biostatistics and Bioinformatics subgroup, Pathobiology Group of European Organization for Research and Treatment of Cancer (EORTC). Professor Buzdin is cofounder and chief scientific officer of Oncobox, which is a translational oncology company. He has authored more than 170 scientific papers and patent applications, and has worked as chief executive of several biotechnological and biomedical companies in Russia, Hong Kong and in the United States. He has also chaired two clinical trials of transcriptomics-based diagnostic tools in oncology.
Professor Buzdin’s team pioneered bioinformatic quantitative analysis of molecular pathways using gene expression data and developed specialized algorithms and software packages to personalize prescription of targeted immunotherapy and chemotherapy drugs to cancer patients that were clinically validated and commercialized.

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

29.10.2024

Herausgeber

Anton Buzdin

Verlag

Elsevier

Seitenzahl

408

Maße (L/B/H)

23.4/19.1/2.3 cm

Gewicht

776 g

Sprache

Englisch

ISBN

978-0-443-15568-0

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

Kundinnen und Kunden meinen

0 Bewertungen

Informationen zu Bewertungen

Zur Abgabe einer Bewertung ist eine Anmeldung im Konto notwendig. Die Authentizität der Bewertungen wird von uns nicht überprüft. Wir behalten uns vor, Bewertungstexte, die unseren Richtlinien widersprechen, entsprechend zu kürzen oder zu löschen.

Die Bewertungen sind nach Format, Anzahl Sterne und Datum sortiert.

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kund*innen durch Ihre Meinung

Kundinnen und Kunden meinen

0 Bewertungen filtern
Die Leseprobe wird geladen.
  • Produktbild: Molecular Pathway Analysis Using High-Throughput Omics Molecular Data
  • 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