http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Protein Interaction Networks, Volume 131 in the Advances in Protein Chemistry and Structural Biology series, highlights new advances in the field, with this new volume presenting interesting chapters written by an international board of authors.- Provides the authority and expertise of leading contributors from an international board of authors-.
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 SANAś 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
Advances in protein chemistry and structural biology ; v. 131