Disambiguating proteins, genes, and RNA in text: a machine learning approach.
about
Building a protein name dictionary from full text: a machine learning term extraction approachBiomedical language processing: what's beyond PubMed?Thesaurus-based disambiguation of gene symbols.Contextual weighting for Support Vector Machines in literature mining: an application to gene versus protein name disambiguationVarious criteria in the evaluation of biomedical named entity recognition.Mining the pharmacogenomics literature--a survey of the state of the artNatural language query in the biochemistry and molecular biology domains based on cognition search™Gene and protein nomenclature in public databasesMutationFinder: a high-performance system for extracting point mutation mentions from text.BIOADI: a machine learning approach to identifying abbreviations and definitions in biological literatureInvestigating heterogeneous protein annotations toward cross-corpora utilization.GENETAG: a tagged corpus for gene/protein named entity recognition.Machine learning and word sense disambiguation in the biomedical domain: design and evaluation issues.Biomedical word sense disambiguation with ontologies and metadata: automation meets accuracyTailoring vocabularies for NLP in sub-domains: a method to detect unused word senseIdentifying the status of genetic lesions in cancer clinical trial documents using machine learning.Quantitative assessment of dictionary-based protein named entity tagging.Developing a hybrid dictionary-based bio-entity recognition techniqueMapping abbreviations to full forms in biomedical articles.What the papers say: text mining for genomics and systems biology.Protein name tagging guidelines: lessons learned.Concept recognition for extracting protein interaction relations from biomedical textFast max-margin clustering for unsupervised word sense disambiguation in biomedical texts.Literature mining, ontologies and information visualization for drug repurposing.Identification of biological relationships from text documents using efficient computational methods.Automatic extraction of mutations from Medline and cross-validation with OMIM.Semantic annotation in biomedicine: the current landscape.Ambiguity of human gene symbols in LocusLink and MEDLINE: creating an inventory and a disambiguation test collection.Bioinformatic analysis of autism positional candidate genes using biological databases and computational gene network prediction.Classification of dog barks: a machine learning approach.A Network Analysis Model for Disambiguation of Names in Lists
P2860
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P2860
Disambiguating proteins, genes, and RNA in text: a machine learning approach.
description
2001 nî lūn-bûn
@nan
2001年の論文
@ja
2001年学术文章
@wuu
2001年学术文章
@zh-cn
2001年学术文章
@zh-hans
2001年学术文章
@zh-my
2001年学术文章
@zh-sg
2001年學術文章
@yue
2001年學術文章
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2001年學術文章
@zh-hant
name
Disambiguating proteins, genes, and RNA in text: a machine learning approach.
@en
Disambiguating proteins, genes, and RNA in text: a machine learning approach.
@nl
type
label
Disambiguating proteins, genes, and RNA in text: a machine learning approach.
@en
Disambiguating proteins, genes, and RNA in text: a machine learning approach.
@nl
prefLabel
Disambiguating proteins, genes, and RNA in text: a machine learning approach.
@en
Disambiguating proteins, genes, and RNA in text: a machine learning approach.
@nl
P2093
P356
P1433
P1476
Disambiguating proteins, genes, and RNA in text: a machine learning approach.
@en
P2093
Hatzivassiloglou V
Rzhetsky A
P304
P356
10.1093/BIOINFORMATICS/17.SUPPL_1.S97
P407
P478
17 Suppl 1
P577
2001-01-01T00:00:00Z