• Corpus ID: 17786087

gene – drug relationships n information extraction n information retrieval n machine learning n natural language processing n NLP n pharmacogenetics n pharmacogenomics n text mining

@inproceedings{Garten2010geneD,
  title={gene – drug relationships n information extraction n information retrieval n machine learning n natural language processing n NLP n pharmacogenetics n pharmacogenomics n text mining},
  author={Yael Garten and Adrien Coulet and Russ B. Altman},
  year={2010}
}
s. Important advance in mining the scientific literature. 

Figures from this paper

References

SHOWING 1-10 OF 153 REFERENCES

Semantic Relations Asserting the Etiology of Genetic Diseases

TLDR
A natural language processing method for extracting causal relations between genetic phenomena and diseases and the use of a graphical display application for viewing the semantic predications produced by the system is suggested.

Anni 2.0: a multipurpose text-mining tool for the life sciences

TLDR
Anni 2.0's usability is illustrated by applying the tool to two use cases: interpretation of a set of differentially expressed genes, and literature-based knowledge discovery.

Natural Language Processing and Systems Biology

  • K. CohenL. Hunter
  • Biology
    Artificial Intelligence Methods And Tools For Systems Biology
  • 2004
This chapter outlines the basic families of applications of natural language processing techniques to questions of interest to systems biologists and describes publicly available resources for such

GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles

TLDR
A system is presented that extracts and structures information about cellular pathways from the biological literature in accordance with a knowledge model that was developed earlier and implemented by modifying an existing medical natural language processing system.

RelEx - Relation extraction using dependency parse trees

TLDR
RelEx, an approach for relation extraction from free text based on natural language preprocessing producing dependency parse trees and applying a small number of simple rules to these trees, is developed.

Unsupervised Discovery of Compound Entities for Relationship Extraction

TLDR
A method based on rules over grammatical dependency structures for unsupervised segmentation of sentences into compound entities and relationships is presented, complementing the rule-based approach with a statistical component that prunes structures with low information content, thereby reducing false positives in the prediction of compound entities, their constituents and relationships.

EDGAR: extraction of drugs, genes and relations from the biomedical literature.

TLDR
The mechanisms for automatically generating assertions about drugs and genes relevant to cancer and on a simple application, conceptual clustering of documents are reported on.

Extracting Semantic Predications from Medline Citations for Pharmacogenomics

TLDR
A natural language processing system (Enhanced SemRep) is described to identify core assertions on pharmacogenomics in Medline citations and discusses the potential of this system in assisting both clinical practice and scientific investigation.

Extraction of Genotype-Phenotype-Drug Relationships from Text: From Entity Recognition to Bioinformatics Application

TLDR
The Genotype-Phenotype-Drug Relationship Extraction from Text workshop (or GPD-Rx workshop) is to examine the current state of art and discuss the next steps for making the extraction of relationships between biomedical entities integral to the curation and knowledge management workflow in Pharmacogenomics.

Linking genes to literature: text mining, information extraction, and retrieval applications for biology

TLDR
This review presents a general introduction to the main characteristics and applications of currently available text-mining systems for life sciences in terms of the type of biological information demands being addressed; the level of information granularity of both user queries and results; and the features and methods commonly exploited by these applications.
...