Extracting interactions between proteins from the literature
98 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Philippe E. Thomas
- Ulf Leser
- Domonkos Tikk
- Jörg Hakenberg
- Igor Jurisica
- Peter Palaga
- Steffen Klamt
- Zhiyong Lu
In Molecular Biology
- Douglas A. Lauffenburger
- Peter K. Sorger
- J. Sáez-Rodríguez
- Ulf Leser
- Domonkos Tikk
- Igor Jurisica
- Steffen Klamt
- Zhiyong Lu
Citations in the corpus
- Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction (2009, methodology): “Literature curation can be performed by expert readers or automatically using ‘bibliome’ mining software”
- A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature (2010, background): “A number of different techniques have been proposed to solve the problem of extracting interactions between proteins in natural language text. These can be roughly sorted into one of three classes: co-occurrence, pattern matching, and machine learning. We briefly review these methods here for comple”
- Evaluation of linguistic features useful in extraction of interactions from PubMed; Application to annotating known, high-throughput and predicted interactions in I2D (2009, background): “Most of the systems have been evaluated on different datasets, specific to a given work (Zhou and He, 2008).”