Evaluation of Linear Classifiers on Articles Containing Pharmacokinetic Evidence of Drug-Drug Interactions

@article{Kolchinsky2012EvaluationOL,
  title={Evaluation of Linear Classifiers on Articles Containing Pharmacokinetic Evidence of Drug-Drug Interactions},
  author={Artemy Kolchinsky and An{\'a}lia Lourenço and Lang Li and Luis Mateus Rocha},
  journal={Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing},
  year={2012},
  pages={
          409-20
        }
}
BACKGROUND Drug-drug interaction (DDI) is a major cause of morbidity and mortality. DDI research includes the study of different aspects of drug interactions, from in vitro pharmacology, which deals with drug interaction mechanisms, to pharmaco-epidemiology, which investigates the effects of DDI on drug efficacy and adverse drug reactions. Biomedical literature mining can aid both kinds of approaches by extracting relevant DDI signals from either the published literature or large clinical… 

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References

SHOWING 1-10 OF 22 REFERENCES

Discovery and Explanation of Drug-Drug Interactions via Text Mining

This work trains a random forest classifier to score potential DDIs based on the features of the normalized assertions extracted from the literature that relate two drugs to a gene product, and shows how the classifier can be used to explain known DDIs and to uncover new DDIs that have not yet been reported.

Mining drug targets based on microarray experiments

A system, drug target integrative miner (DruTiMine), which aims at providing a service to enhance and shorten the process of drug target discovery for pharmaceutical companies and research labs concerned with drug discovery.

Use of Text Mining for Protein Structure Prediction and Functional Annotation in Lack of Sequence Homology

A novel method that uses text mining to improve and screen genome-scale structure and shows that the literature keywords and similarity measure used here are of great value for the increasingly important field of functional annotation of new sequences with no or little sequence homology.

BMC Bioinformatics

  • Medicine
  • 2005
BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We do not make

A Comparative Study on Feature Selection in Text Categorization

This paper finds strong correlations between the DF IG and CHI values of a term and suggests that DF thresholding the simplest method with the lowest cost in computation can be reliably used instead of IG or CHI when the computation of these measures are too expensive.

The relationship between Precision-Recall and ROC curves

It is shown that a deep connection exists between ROC space and PR space, such that a curve dominates in R OC space if and only if it dominates in PR space.

Machine learning

Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.

The nature of biotechnology.

  • E. Hall
  • Biology, Engineering
    Journal of biomedical engineering
  • 1988

Nucleic Acids Research

The results suggest that overall polymerase activity fluctuates with a peak occurring at approximately early S phase, which indicates that DNA polymerase I is subject to a complex control and imply that it has a role in both DNA synthesis and DNA repair.