Simple and efficient machine learning frameworks for identifying protein-protein interaction relevant articles and experimental methods used to study the interactions. Agarwal, S., Liu, F., & Yu, H. BMC Bioinformatics, 12(Suppl 8):S10, 2011. ![Simple and efficient machine learning frameworks for identifying protein-protein interaction relevant articles and experimental methods used to study the interactions [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex BACKGROUND: Protein-protein interaction (PPI) is an important biomedical phenomenon. Automatically detecting PPI-relevant articles and identifying methods that are used to study PPI are important text mining tasks. In this study, we have explored domain independent features to develop two open source machine learning frameworks. One performs binary classification to determine whether the given article is PPI relevant or not, named "Simple Classifier", and the other one maps the PPI relevant articles with corresponding interaction method nodes in a standardized PSI-MI (Proteomics Standards Initiative-Molecular Interactions) ontology, named "OntoNorm". RESULTS: We evaluated our system in the context of BioCreative challenge competition using the standardized data set. Our systems are amongst the top systems reported by the organizers, attaining 60.8% F1-score for identifying relevant documents, and 52.3% F1-score for mapping articles to interaction method ontology. CONCLUSION: Our results show that domain-independent machine learning frameworks can perform competitively well at the tasks of detecting PPI relevant articles and identifying the methods that were used to study the interaction in such articles.
@article{agarwal_simple_2011,
title = {Simple and efficient machine learning frameworks for identifying protein-protein interaction relevant articles and experimental methods used to study the interactions},
volume = {12},
issn = {1471-2105},
url = {http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-S8-S10},
doi = {10.1186/1471-2105-12-S8-S10},
abstract = {BACKGROUND:
Protein-protein interaction (PPI) is an important biomedical phenomenon. Automatically detecting PPI-relevant articles and identifying methods that are used to study PPI are important text mining tasks. In this study, we have explored domain independent features to develop two open source machine learning frameworks. One performs binary classification to determine whether the given article is PPI relevant or not, named "Simple Classifier", and the other one maps the PPI relevant articles with corresponding interaction method nodes in a standardized PSI-MI (Proteomics Standards Initiative-Molecular Interactions) ontology, named "OntoNorm".
RESULTS:
We evaluated our system in the context of BioCreative challenge competition using the standardized data set. Our systems are amongst the top systems reported by the organizers, attaining 60.8\% F1-score for identifying relevant documents, and 52.3\% F1-score for mapping articles to interaction method ontology.
CONCLUSION:
Our results show that domain-independent machine learning frameworks can perform competitively well at the tasks of detecting PPI relevant articles and identifying the methods that were used to study the interaction in such articles.},
language = {en},
number = {Suppl 8},
urldate = {2016-11-30},
journal = {BMC Bioinformatics},
author = {Agarwal, Shashank and Liu, Feifan and Yu, Hong},
year = {2011},
pmid = {22151701 PMCID: PMC3269933},
pages = {S10},
}
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One performs binary classification to determine whether the given article is PPI relevant or not, named \"Simple Classifier\", and the other one maps the PPI relevant articles with corresponding interaction method nodes in a standardized PSI-MI (Proteomics Standards Initiative-Molecular Interactions) ontology, named \"OntoNorm\". RESULTS: We evaluated our system in the context of BioCreative challenge competition using the standardized data set. Our systems are amongst the top systems reported by the organizers, attaining 60.8% F1-score for identifying relevant documents, and 52.3% F1-score for mapping articles to interaction method ontology. CONCLUSION: Our results show that domain-independent machine learning frameworks can perform competitively well at the tasks of detecting PPI relevant articles and identifying the methods that were used to study the interaction in such articles.","language":"en","number":"Suppl 8","urldate":"2016-11-30","journal":"BMC Bioinformatics","author":[{"propositions":[],"lastnames":["Agarwal"],"firstnames":["Shashank"],"suffixes":[]},{"propositions":[],"lastnames":["Liu"],"firstnames":["Feifan"],"suffixes":[]},{"propositions":[],"lastnames":["Yu"],"firstnames":["Hong"],"suffixes":[]}],"year":"2011","pmid":"22151701 PMCID: PMC3269933","pages":"S10","bibtex":"@article{agarwal_simple_2011,\n\ttitle = {Simple and efficient machine learning frameworks for identifying protein-protein interaction relevant articles and experimental methods used to study the interactions},\n\tvolume = {12},\n\tissn = {1471-2105},\n\turl = {http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-S8-S10},\n\tdoi = {10.1186/1471-2105-12-S8-S10},\n\tabstract = {BACKGROUND:\nProtein-protein interaction (PPI) is an important biomedical phenomenon. 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