Heterogeneous data integration by tree-augmented naïve Bayes for protein-protein interactions prediction

PROTEOMICS ◽  
2012 ◽  
Vol 13 (2) ◽  
pp. 261-268 ◽  
Author(s):  
Xiaotong Lin ◽  
Xue-wen Chen
Keyword(s):  
Data Integration ◽  
Protein Interactions ◽  
Naive Bayes ◽  
Heterogeneous Data ◽  
Naïve Bayes ◽  
Protein Protein Interactions ◽  
Heterogeneous Data Integration

scholarly journals A Comprehensive Approach Characterizing Fusion Proteins and Their Interactions Using Biomedical Literature

2018 ◽  
Author(s):  
Somnath Tagore ◽  
Alessandro Gorohovski ◽  
Lars Juhl Jensen ◽  
Milana Frenkel-Morgenstern
Keyword(s):  
Protein Interactions ◽  
Fusion Proteins ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Biomedical Literature ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Protein Protein Interactions ◽  
Naïve Bayes Classifier ◽  
Downstream Analysis

AbstractToday’s increase in scientific literature requires the efficient methods of data mining for improving the extraction of the useful information from texts. In this manuscript, we used a data and text mining method to identify fusions and their protein-protein interactions from published biomedical text. The extracted fusion proteins and their protein-protein interactions are used as a training set for a Naïve Bayes classifier that is further used for final identification of testing dataset, consisting of 1817 fusions. Our method has a literature corpus, text and annotation mappers; keywords, rule bases, negative tokens, and pattern extractor; synonym tagger, normalization, regular expression mapper; and Naïve Bayes classifier. We classified 1817 unique fusion proteins and their corresponding 2908 protein-protein interactions for 18 cancer types. Therefore, it can be used for screening literature for identifying mentions unique cases of fusions that can be further used for downstream analysis. It is available at http://protfus.md.biu.ac.il/.

VGEs-Oriented Multi-sourced Heterogeneous Data Integration

2010 ◽  
Vol 11 (3) ◽  
pp. 292-298
Author(s):  
Hongjun SU ◽  
Yehua SHENG ◽  
Yongning WEN ◽  
Min CHEN
Keyword(s):  
Data Integration ◽  
Heterogeneous Data ◽  
Heterogeneous Data Integration

Design and Implementation of Oilfield Heterogeneous Data Integration Model Based on Ontology

2014 ◽  
Vol 912-914 ◽  
pp. 1201-1204
Author(s):  
Gang Huang ◽  
Xiu Ying Wu ◽  
Man Yuan
Keyword(s):  
Data Integration ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Semantic Heterogeneity ◽  
Integration Model ◽  
Integration Framework ◽  
Heterogeneous Data Integration ◽  
Semantic Level ◽  
Heterogeneous Data Sources ◽  
Semantic Difference

This paper provides an ontology-based distributed heterogeneous data integration framework (ODHDIF). The framework resolves the problem of semantic interoperability between heterogeneous data sources in semantic level. By metadatas specifying the distributed, heterogeneous data and by describing semantic information of data source , having "ontology" as a common semantic model, semantic match is established through ontology mapping between heterogeneous data sources and semantic difference institutions are shielded, so that semantic heterogeneity problem of the heterogeneous data sources can be effectively solved. It provides an effective technology measure for the interior information of enterprises to be shared in time accurately.

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