9 edition of Biological data mining in protein interaction networks found in the catalog.
Biological data mining in protein interaction networks
Includes bibliographical references and index.
|Statement||Xiao-Li Li and See-Kiong Ng, editors.|
|Contributions||Li, Xiao-Li, 1969-, Ng, See-Kiong.|
|LC Classifications||QP551.5 .B56 2009|
|The Physical Object|
|LC Control Number||2008041607|
Protein-protein interactions (PPIs) control all functions and physiological states of the cell. Identification and understanding of novel PPIs would facilitate the discovery of new biological models and therapeutic targets for clinical intervention. Numerous resources and PPI databases have been developed to define a global interactome through the PPI data mining, curation, and integration . Heterogeneous biological data such as sequence matches, gene expression correlations, protein-protein interactions, and biochemical pathways can be merged and analyzed via graphs, or networks. Existing software for network analysis has limited scalability to large data sets or is only accessible to software developers as libraries. In addition, the polymorphic nature of the data .
3 Protein-protein interactions Introduction to PPIs Protein-protein interactions (PPIs), which refer to two or more proteins, when binding together, often to carry out their biological functions, play important roles in biological processes. Many of the most important biological . Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical : $
and Data Mining) consists of two phases: Phase 1: we develop a Scalable and Portable IE method (SPIE) to extract the protein-protein interaction from the biomedical literature. These extracted protein-protein interactions form a scale-free network graph. In Phase 2, . Biological Data Mining then describes the relationships between data mining and related areas of computing, including knowledge representation, information retrieval, and data integration for structured and unstructured biological data. The last part explores emerging data mining opportunities for biomedical applications.
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Biological Data Mining in Protein Interaction Networks explains bioinformatic methods for predicting PPIs, as well as data mining methods to mine or analyze various protein interaction networks.
Enter your mobile number or email address below Biological data mining in protein interaction networks book we'll send you a link to download the free Kindle by: Biological Data Mining in Protein Interaction Networks explains bioinformatic methods for predicting PPIs, as well as data mining methods to mine or analyze various protein interaction networks.
A defining body of research within the field, this book discovers underlying interaction mechanisms by studying intra-molecular features that form the common denominator of.
Explains bioinformatic methods for predicting protein-protein interactions (PPIs), as well as data mining methods to mine or analyze various protein interaction networks.
This book discovers underlying interaction mechanisms by studying intra-molecular features that form the common denominator of various PPIs. The standard paradigm is to visualize the very large networks implicit in high-throughput interaction data, then study sub-network interactions in detail.
We invert this, going from individual interactions with target genes to construct a larger network centred Cited by: Annotation pipeline. All biological data were combined into a relational database.
Human disease gene information was extracted from the OMIM database and lists of genes flanking the disease genes were obtained from EntrezGene (build 35) ().Protein sequence data were taken from GenBank and complete protein domain annotation was performed on all protein sequences using Pfam Hidden Markov Cited by: Methods for detecting protein-protein interactions (PPIs) have given researchers a global picture of protein interactions on a genomic scale.
Biological Data Mining in Protein Interaction Networks. Methods for detecting protein-protein interactions (PPIs) have given researchers a global picture of protein interactions on a genomic scale. "Biological Data Mining in Protein Interaction Networks" explains bioinformatic methods for predicting PPIs, as well as data mining methods to mine or analyze various protein interaction networks.
Biological data mining is the activity of finding significant information in biomolecular data. The significant information may refer to motifs, clusters, genes, and protein signatures. Part of the Intelligent Systems Reference Library book series (ISRL, protein-protein interaction networks, signaling features and sharing components.
To this end, we introduce a soft-clustering method for doing the task by exploiting integrated multiple data, especially signaling features, i.e., protein-protein interactions, signaling. This book biological data mining is a one stop resource for getting a firsthand account of data mining applications in bioinformatics.
The book covers most of the aspects of data mining for example classification, clustering and text mining applied to interesting biological problems touching the various aspects of s: 2. This book focuses on the data mining, systems biology, and bioinformatics computational methods that can be used to summarize biological networks.
Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research.
Each chapter is written by a distinguished team of interdisciplin. Biological Network Analysis: Trends, Approaches, Graph Theory, and Algorithms considers three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN), and Human Brain Connectomes.
The book's authors discuss various graph theoretic and data analytics approaches used to analyze these networks with respect to available tools, technologies, standards, algorithms and databases for generating, representing and analyzing graphical data. The latent geometry of the human protein interaction network.
Bioinformatics. 34,  Mier, P., G. Alanis-Lobato and M.A. Andrade-Navarro. Protein-protein interactions can be predicted using coiled coil co-evolution patterns. Journal of Theoretical Biology. Predicting Protein Functions from Protein Interaction Networks: /ch Functional characterization of genes and their protein products is essential to biological and clinical research.
Yet, there is still no reliable way of. Though the protein interaction networks constructed to date do not provide a truly realistic picture of biological network mechanisms, they are functional in the sense that they have enabled researchers to test the reliability of high-throughput data, predict protein function, and localize proteins within the cell.
Book Description. Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research.
Each chapter is written by a distinguished team of interdisciplinary data mining researchers. Protein–protein interactions (PPIs) are physical contacts of high specificity established between two or more protein molecules as a result of biochemical events steered by interactions that include electrostatic forces, hydrogen bonding and the hydrophobic are physical contacts with molecular associations between chains that occur in a cell or in a living organism in a specific.
Science, Engineering, and Biology Informatics Biological Data Mining and Its Applications in Healthcare, pp.
() No Access Chapter Automated Mining of Disease-Specific Protein Interaction Networks Based on Biomedical Literature. The book begins with a brief overview of biological networks and graph theory/graph algorithms and goes on to explore: global network properties, network centralities, network motifs, network clustering, Petri nets, signal transduction and gene regulation networks, protein interaction networks, metabolic networks, phylogenetic networks.
The UT Biological Data Mining Research group was supported by the National Science Foundation through an Information Technology Research Grant ``Feedback from Multi-Source Data Mining to Experimentation for Gene Network Discovery,'' (IIS).
Any opinions, findings, and conclusions or recommendations expressed in this material are those of.the direct experimental data, a number of large biological datasets also provide indirect evidence about protein-interaction relationships.
Thus computational approaches could be utilized to combine multiple information sources in order to predict the sets of interacting protein pairs and identify important biological substructures in this network.Many protein–protein interactions (PPIs) in a cell form protein interaction networks (PINs) where proteins are nodes and their interactions are edges.
PINs are the most intensely analyzed networks in biology. There are dozens of PPI detection methods to identify such interactions. The yeast two-hybrid system is a commonly used experimental technique for the study of binary interactions.