Prediction of protein-protein interaction types using association rule based classification

Park, Sung Hee; Reyes, Jonathan; Gilbert, David; Kim, Ji Woong; Kim, Sang Soo

doi:10.1186/1471-2105-10-36

Cited by 53 publications

(50 citation statements)

References 25 publications

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“…In our approach we employed Association Rule Mining (ARM) to discover a set of multivariate traits expressed as association rules describing associations among a set of singlet traits. We have previously shown [9] that association rules detected by ARM are informative and quantitative and have benefits to interpret their meaning.…”

Section: Association Rule Miningmentioning

confidence: 99%

Discovery of multivariate phenotypes using association rule mining and their application to genome-wide association studies

Park

Kim

2010

2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

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Genome-wide association studies (GWAS) have served crucial roles in investigating disease susceptible loci for single traits. On the other hand, the GWAS have been limited in measuring genetic risk factors for multivariate phenotypes from pleiotropic genetic effects of genetic loci. This work reports a data mining approach to discover patterns of multivariate phenotypes expressed as association rules, and present an analytical scheme for GWAS of those multivariate phenotypes as defining new phenotypes. We identified 13 SNPs for four genes (CSMD1, NFE2L1, CBX1, and SKAP1) associated with low levels of low density lipoprotein cholesterol (LDL-C 100 mg/dl) and high levels of triglycerides (TG 180 mg/dl) as a multivariate phenotype. Compared with a traditional approach to GWAS, the use of discovered multivariate phenotypes can be advantageous in identifying genetic risk factors, accounting for pleiotropic genetic effects when the multivariate phenotypes have a common etiologic pathway.

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Section: Association Rule Miningmentioning

confidence: 99%

Discovery of multivariate phenotypes using association rule mining and their application to genome-wide association studies

Park

Kim

2010

2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

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“…A number of methods, including biochemical, molecular and cellular approaches, have been developed to analyse these interactions, for example, yeast two-hybrid systems, mass spectrometry, bimolecular fluorescence complementation (BiFC), fluorescence resonance energy transfer analysis of crystal structure and the use of protein arrays in vitro and in vivo (Hu and Kerppola, 2003;Bracha-Drori et al, 2004;Kuroda et al, 2006;Lalonde et al, 2008). Moreover, information derived from genome sequences allows us to make accurate predictions about protein-protein interactions (von Mering et al, 2002;Park et al, 2009;Wu et al, 2009). Each method can yield false positives, so results of each method should be validated independently (Lalonde et al, 2008;Miernyk and Thelen, 2008).…”

Section: Introductionmentioning

confidence: 99%

Detection of protein-protein interactions in plants using the transrepressive activity of the EAR motif repression domain

Matsui

Ohme‐Takagi²

2010

The Plant Journal

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SUMMARYThe activities of many regulatory factors involve interactions with other proteins. We demonstrate here that the ERF-associated amphiphilic repression (EAR) motif repression domain (SRDX) can convert a transcriptional complex into a repressor via transrepression that is mediated by protein-protein interactions and show that transrepressive activity of SRDX can be used to detect such protein-protein interactions. When we fused a protein that interacts with a transcription factor with SRDX and co-expressed the product with the transcription factor in plant cells, the expression of genes that are targets of the transcription factor was suppressed by transrepression. We demonstrated the transrepressive activity of SRDX using FOS and JUN as a model system and used two MADS box plant proteins, PISTILLATA and APETALA3, which are known to form heterodimers. Furthermore, the transgenic plants that expressed TTG1, which is a WD40 protein and interacts with bHLH transcription factors, fused to SRDX exhibited a phenotype similar to ttg1 mutants by transrepression and the regions of TTG1 required for interaction to the bHLH protein were detected using our system. We also used this system to analyse a protein factor that might be incorporated into a transcriptional complex and identified an Arabidopsis WD40 protein PWP2 (AtPWP2) interacting with AtTBP1 through comparison of phenotypes induced by 35S:AtPWP2-SRDX with that induced by the chimeric repressor. Our results indicate that the transrepression mediated by SRDX can be used to detect and confirm protein-protein interactions in plants and should be useful in identifying factors that form transcriptional protein complexes.

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“…In [52], interaction pattern discovery with characterization of different types of interactions is discussed along with their use in protein-protein interaction. Graph databases enable efficient storage and processing of the encoded biological relationships.…”

Section: Physical Data Storage In Graph Databasementioning

confidence: 99%

Designing Graphical Data Storage Model for Gene-Protein and Gene-Gene Interaction Networks

Farooq¹,

Ferzund²,

Mahmood³

et al. 2017

ijacsa

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Abstract-Graph is an expressive way to represent dynamic and complex relationships in highly connected data. In today's highly connected world, general purpose graph databases are providing opportunities to experience benefits of semantically significant networks without investing on the graph infrastructure. Examples of prominent graph databases are: Neo4j, Titan and OrientDB etc. In biological OMICS landscape, Interactomics is one of the new disciplines that focuses mainly on the data modeling, data storage and retrieval of biological interaction data. Biological experiments generate prodigious amount of data in various formats(semi-structured or unstructured). The large volume of such data posses challenges for data acquisition, data integration, multiple data modalities (either data model of storage model, storage, processing and visualization. This paper aims at designing a well suited graphical data storage model for biological information which is collected from major heterogeneous biological data repositories, by using graph database.

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Prediction of protein-protein interaction types using association rule based classification

Cited by 53 publications

References 25 publications

Discovery of multivariate phenotypes using association rule mining and their application to genome-wide association studies

Discovery of multivariate phenotypes using association rule mining and their application to genome-wide association studies

Detection of protein-protein interactions in plants using the transrepressive activity of the EAR motif repression domain

Designing Graphical Data Storage Model for Gene-Protein and Gene-Gene Interaction Networks

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