The strength of co-authorship in gene name disambiguation

Abstract

BackgroundA biomedical entity mention in articles and other free texts is often ambiguous. For example, 13% of the gene names (aliases) might refer to more than one gene. The task of Gene Symbol Disambiguation (GSD) – a special case of Word Sense Disambiguation (WSD) – is to assign a unique gene identifier for all identified gene name aliases in biology-related articles. Supervised and unsupervised machine learning WSD techniques have been applied in the biomedical field with promising results. We examine here the utilisation potential of the fact – one of the special features of biological articles – that the authors of the documents are known through graph-based semi-supervised methods for the GSD task.ResultsOur key hypothesis is that a biologist refers to each particular gene by a fixed gene alias and this holds for the co-authors as well. To make use of the co-authorship information we decided to build the inverse co-author graph on MedLine abstracts. The nodes of the inverse co-author graph are articles and there is an edge between two nodes if and only if the two articles have a mutual author. We introduce here two methods using distances (based on the graph) of abstracts for the GSD task. We found that a disambiguation decision can be made in 85% of cases with an extremely high (99.5%) precision rate just by using information obtained from the inverse co-author graph. We incorporated the co-authorship information into two GSD systems in order to attain full coverage and in experiments our procedure achieved precision of 94.3%, 98.85%, 96.05% and 99.63% on the human, mouse, fly and yeast GSD evaluation sets, respectively.ConclusionBased on the promising results obtained so far we suggest that the co-authorship information and the circumstances of the articles' release (like the title of the journal, the year of publication) can be a crucial building block of any sophisticated similarity measure among biological articles and hence the methods introduced here should be useful for other biomedical natural language processing tasks (like organism or target disease detection) as well.

What the papers say: Text mining for genomics and systems biology

“…These can then be used to train a classifier to distinguish the correct identifier from incorrect ones [ 58 ]. Knowledge of paper co-authorship has been found to be useful in identifier disambiguation,[ 59 ] based on the idea that an author uses gene names consistently across all of their publications or may work on a specific set of genes consistently.…”

NEMO: Extraction and normalization of organization names from PubMed affiliations

“…Author disambiguation has been used for diverse applications such as building social networks, normalizing gene names and analyzing collaborations. Farkas (35) was successful in using authors' information for improving the accuracy of a baseline gene normalization system from 80% to 97%. Large scale social network analysis of disambiguated author information is useful for finding key scientific leaders who are "low publishers" in scientific journals (36).…”

Biomedical word sense disambiguation with ontologies and metadata: automation meets accuracy

“…In the biomedical domain researchers have focused on supervised methods [8][9][10][11] and using established knowledge [12][13][14][15] to perform gene name normalization and resolve abbreviations. According to the recent BioCreAtIvE challenge, the former problem can be solved with up to 81% success rate [14] for human genes, which are challenging with 5.5 synonyms per name (therefore many genes are named identically).…”

“…The above approaches use cosine similarity [12], SVM [10,11], Bayes, decision trees, induced rules [8], and background knowledge sources such as the Unified Medical Language System (UMLS) [16], Medical Subject Headings (MeSH) [17], and the Gene Ontology (GO) [18]. Two approaches use metadata, such as authors [15] and Journal Descriptor Indexing [13]. Most of the unsupervised approaches so far were evaluated outside the biomedical domain [19][20][21][22][23][24][25], with the exception of [26], who used relations between terms given by the UMLS for unsupervised WSD of medical documents and achieved 74% precision and 49% recall.…”