Context-specific protein network miner - an online system for exploring context-specific protein interaction networks from the literature
Article - Full Text
Supplemental File 1
Supplemental File 2
Supplemental File 3
Supplemental File 4
Supplemental File 5
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Applied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/325303
MetadataShow full item record
AbstractBackground: Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis. Results: We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality. Conclusions: CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/. © 2012 Chowdhary et al.
CitationChowdhary R, Tan SL, Zhang J, Karnik S, Bajic VB, et al. (2012) Context-Specific Protein Network Miner - An Online System for Exploring Context-Specific Protein Interaction Networks from the Literature. PLoS ONE 7: e34480. doi:10.1371/journal.pone.0034480.
PublisherPublic Library of Science (PLoS)
PubMed Central IDPMC3321019
- Visualization of protein interaction networks: problems and solutions.
- Authors: Agapito G, Guzzi PH, Cannataro M
- Issue date: 2013
- PPLook: an automated data mining tool for protein-protein interaction.
- Authors: Zhang SW, Li YJ, Xia L, Pan Q
- Issue date: 2010 Jun 16
- IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model.
- Authors: Xia K, Dong D, Han JD
- Issue date: 2006 Nov 18
- The Gene Interaction Miner: a new tool for data mining contextual information for protein-protein interaction analysis.
- Authors: Ikin A, Riveros C, Moscato P, Mendes A
- Issue date: 2010 Jan 15
- QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks.
- Authors: Thibodeau A, Márquez EJ, Luo O, Ruan Y, Menghi F, Shin DG, Stitzel ML, Vera-Licona P, Ucar D
- Issue date: 2016 Jun
Showing items related by title, author, creator and subject.
The human interactome knowledge base (hint-kb): An integrative human protein interaction database enriched with predicted protein–protein interaction scores using a novel hybrid techniqueTheofilatos, Konstantinos A.; Dimitrakopoulos, Christos M.; Likothanassis, Spiridon D.; Kleftogiannis, Dimitrios A.; Moschopoulos, Charalampos N.; Alexakos, Christos; Papadimitriou, Stergios; Mavroudi, Seferina P. (Artificial Intelligence Review, Springer Nature, 2013-07-12) [Article]Proteins are the functional components of many cellular processes and the identification of their physical protein–protein interactions (PPIs) is an area of mature academic research. Various databases have been developed containing information about experimentally and computationally detected human PPIs as well as their corresponding annotation data. However, these databases contain many false positive interactions, are partial and only a few of them incorporate data from various sources. To overcome these limitations, we have developed HINT-KB (http://biotools.ceid.upatras.gr/hint-kb/), a knowledge base that integrates data from various sources, provides a user-friendly interface for their retrieval, cal-culatesasetoffeaturesofinterest and computesaconfidence score for every candidate protein interaction. This confidence score is essential for filtering the false positive interactions which are present in existing databases, predicting new protein interactions and measuring the frequency of each true protein interaction. For this reason, a novel machine learning hybrid methodology, called (Evolutionary Kalman Mathematical Modelling—EvoKalMaModel), was used to achieve an accurate and interpretable scoring methodology. The experimental results indicated that the proposed scoring scheme outperforms existing computational methods for the prediction of PPIs.
PPI-IRO: A two-stage method for protein-protein interaction extraction based on interaction relation ontologyLi, Chuanxi; Chen, Peng; Wang, Rujing; Wang, Xiujie; Su, Yaru; Li, Jinyan (International Journal of Data Mining and Bioinformatics, Inderscience Publishers, 2014) [Article]Mining Protein-Protein Interactions (PPIs) from the fast-growing biomedical literature resources has been proven as an effective approach for the identifi cation of biological regulatory networks. This paper presents a novel method based on the idea of Interaction Relation Ontology (IRO), which specifi es and organises words of various proteins interaction relationships. Our method is a two-stage PPI extraction method. At fi rst, IRO is applied in a binary classifi er to determine whether sentences contain a relation or not. Then, IRO is taken to guide PPI extraction by building sentence dependency parse tree. Comprehensive and quantitative evaluations and detailed analyses are used to demonstrate the signifi cant performance of IRO on relation sentences classifi cation and PPI extraction. Our PPI extraction method yielded a recall of around 80% and 90% and an F1 of around 54% and 66% on corpora of AIMed and Bioinfer, respectively, which are superior to most existing extraction methods. Copyright © 2014 Inderscience Enterprises Ltd.
Detecting mutually exclusive interactions in protein-protein interaction maps.Sánchez Claros, Carmen; Tramontano, Anna (PLoS ONE, Public Library of Science (PLoS), 2012-06-08) [Article]Comprehensive protein interaction maps can complement genetic and biochemical experiments and allow the formulation of new hypotheses to be tested in the system of interest. The computational analysis of the maps may help to focus on interesting cases and thereby to appropriately prioritize the validation experiments. We show here that, by automatically comparing and analyzing structurally similar regions of proteins of known structure interacting with a common partner, it is possible to identify mutually exclusive interactions present in the maps with a sensitivity of 70% and a specificity higher than 85% and that, in about three fourth of the correctly identified complexes, we also correctly recognize at least one residue (five on average) belonging to the interaction interface. Given the present and continuously increasing number of proteins of known structure, the requirement of the knowledge of the structure of the interacting proteins does not substantially impact on the coverage of our strategy that can be estimated to be around 25%. We also introduce here the Estrella server that embodies this strategy, is designed for users interested in validating specific hypotheses about the functional role of a protein-protein interaction and it also allows access to pre-computed data for seven organisms.