tmVar: a text mining approach for extracting sequence variants in biomedical literature
By Chih-Hsuan Wei, Bethany R. Harris, Hung-Yu kao, Zhiyong Lu. 2013
185 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Genetics, 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
- Zhiyong Lu
- Robert Leaman
- Chih-Hsuan Wei
- Hung-Yu kao
- Alexis Allot
- Rezarta Islamaj Dogan
- Yifan Peng
- Fei Wang
In Genetics
In Molecular Biology
- Zhiyong Lu
- Robert Leaman
- Chih-Hsuan Wei
- Alexis Allot
- Hung-Yu kao
- Rezarta Islamaj Dogan
- Yifan Peng
- Anita M. Preininger
Citations in the corpus
- tmChem: a high performance approach for chemical named entity recognition and normalization (2015, methodology): “The tokenization is the same as tmVar; specifically, tokens are separated at whitespace and punctuation, digits, lowercase letters and uppercase letters are divided into separate tokens [24].”
- PubTator central: automated concept annotation for biomedical full text articles (2019, background): “While the concept taggers used in PubTator (10) demonstrated high performance in their respective benchmarks (31–34), the content of full text articles is more complex than abstracts, making them more difficult to annotate accurately.”
- PubTator: a web-based text mining tool for assisting biocuration (2013, background): “gov/CBBresearch/Lu/Demo/DNorm/) for diseases, tmVar (21) for mutations and a dictionary-based lookup approach (8) for chemicals.”