Protein interaction networks

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Donev, Rossen
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge : Academic Press, 2022.
Colección:Advances in protein chemistry and structural biology ; v. 131.
Temas:
Protein-protein interactions.
Interactions prot�eine-prot�eine.
Protein-protein interactions
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Protein Interaction Networks
  • Copyright
  • Contents
  • Contributors
  • Chapter One: On the current failure-but bright future-of topology-driven biological network alignment
  • 1. Introduction
  • 1.1. Motivation
  • 1.2. The sequence-topology ``trade-off��
  • 1.3. Possible reasons for the failure
  • 1.4. Contribution
  • 2. Preliminaries
  • 2.1. Pairwise global network alignment (PGNA)
  • 2.2. Measuring topological similarity
  • 2.3. SANA: The simulated annealing network aligner
  • 3. Testing hypotheses R1 and R3
  • 3.1. Addressing R1 using information theory and edge density
  • 3.2. Addressing R3: Inadequate optimization of chosen topological objective functions
  • 3.2.1. SANA achieves near-optimal solutions when the optimal solution is known
  • 3.2.2. SANA outscores other aligners even at optimizing their own objectives
  • 3.2.3. Summary: SANA provides a near-optimal level playing field for objective function comparison
  • 4. Addressing R2: Measuring functional relevance of topological objective functions
  • 4.1. Functional relevance
  • 4.2. Objective function saturation
  • 4.3. Evaluating the functional relevance of topological measures
  • 4.4. Recovery of common Gene Ontology terms
  • 4.5. Topology-based recovery of thousands of orthologs between major BioGRID species
  • 5. Discussion
  • 5.1. Statistical significance
  • 6. Methods
  • 6.1. Information theory in the context of network alignment
  • 6.2. Exactly computing the logarithm of large integers
  • 6.3. Comparing SANA�s alignments to those of competing aligners
  • 6.4. Computing the p-value of recovered orthologs
  • 6.5. Computing the p-value of shared GO terms in an alignment
  • 6.6. On the importance of choosing the right measure of topological similarity
  • 7. Data availability
  • Acknowledgments
  • Author contributions
  • References
  • Chapter Two: From single-omics to interactomics: How can ligand-induced perturbations modulate single-cell phenotypes?
  • 1. Introduction
  • 1.1. Fundamentals of the drug discovery process
  • 1.2. Overwhelming data quantity and complexity in biology
  • 2. Single-cell omics: From unimodal to multimodal analysis
  • 2.1. Is multimodal single-cell analysis the way to go?
  • 2.2. Single-cell data integration challenges
  • 3. Bridging structure and cell data as drivers to understand ligand-induced perturbations
  • 3.1. Three-dimensional structure role in ligand-induced perturbations
  • 3.2. Single-cell role in ligand-induced perturbations
  • 3.3. How can we integrate multimodal biological data? Can structure lead to better network analysis while potentiating th ...
  • 3.4. Datasets and prevision models for drug-induced perturbations
  • 4. Conclusion
  • Funding
  • References
  • Chapter Three: A review of bioinformatics tools and web servers in different microarray platforms used in cancer research
  • 1. Microarray and bioinformatics
  • 2. Application of microarray data

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