Dataset Information

BioConceptVec: Creating and evaluating literature-based biomedical concept embeddings on a large scale.

ABSTRACT: A massive number of biological entities, such as genes and mutations, are mentioned in the biomedical literature. The capturing of the semantic relatedness of biological entities is vital to many biological applications, such as protein-protein interaction prediction and literature-based discovery. Concept embeddings-which involve the learning of vector representations of concepts using machine learning models-have been employed to capture the semantics of concepts. To develop concept embeddings, named-entity recognition (NER) tools are first used to identify and normalize concepts from the literature, and then different machine learning models are used to train the embeddings. Despite multiple attempts, existing biomedical concept embeddings generally suffer from suboptimal NER tools, small-scale evaluation, and limited availability. In response, we employed high-performance machine learning-based NER tools for concept recognition and trained our concept embeddings, BioConceptVec, via four different machine learning models on ~30 million PubMed abstracts. BioConceptVec covers over 400,000 biomedical concepts mentioned in the literature and is of the largest among the publicly available biomedical concept embeddings to date. To evaluate the validity and utility of BioConceptVec, we respectively performed two intrinsic evaluations (identifying related concepts based on drug-gene and gene-gene interactions) and two extrinsic evaluations (protein-protein interaction prediction and drug-drug interaction extraction), collectively using over 25 million instances from nine independent datasets (17 million instances from six intrinsic evaluation tasks and 8 million instances from three extrinsic evaluation tasks), which is, by far, the most comprehensive to our best knowledge. The intrinsic evaluation results demonstrate that BioConceptVec consistently has, by a large margin, better performance than existing concept embeddings in identifying similar and related concepts. More importantly, the extrinsic evaluation results demonstrate that using BioConceptVec with advanced deep learning models can significantly improve performance in downstream bioinformatics studies and biomedical text-mining applications. Our BioConceptVec embeddings and benchmarking datasets are publicly available at https://github.com/ncbi-nlp/BioConceptVec.

SUBMITTER: Chen Q

PROVIDER: S-EPMC7237030 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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BioConceptVec: Creating and evaluating literature-based biomedical concept embeddings on a large scale.

Chen Qingyu Q Lee Kyubum K Yan Shankai S Kim Sun S Wei Chih-Hsuan CH Lu Zhiyong Z

PLoS computational biology 20200423 4

A massive number of biological entities, such as genes and mutations, are mentioned in the biomedical literature. The capturing of the semantic relatedness of biological entities is vital to many biological applications, such as protein-protein interaction prediction and literature-based discovery. Concept embeddings-which involve the learning of vector representations of concepts using machine learning models-have been employed to capture the semantics of concepts. To develop concept embeddings ...[more]

PMID: 32324731

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Project description:MotivationClustering methods can be useful for automatically grouping documents into meaningful clusters, improving human comprehension of a document collection. Although there are clustering algorithms that can achieve the goal for relatively large document collections, they do not always work well for small and homogenous datasets.MethodsIn this article, we present Retro-a novel clustering algorithm that extracts meaningful clusters along with concise and descriptive titles from small and homogenous document collections. Unlike common clustering approaches, our algorithm predicts cluster titles before clustering. It relies on the hypergeometric distribution model to discover key phrases, and generates candidate clusters by assigning documents to these phrases. Further, the statistical significance of candidate clusters is tested using supervised learning methods, and a multiple testing correction technique is used to control the overall quality of clustering.ResultsWe test our system on five disease datasets from OMIM(®) and evaluate the results based on MeSH(®) term assignments. We further compare our method with several baseline and state-of-the-art methods, including K-means, expectation maximization, latent Dirichlet allocation-based clustering, Lingo, OPTIMSRC and adapted GK-means. The experimental results on the 20-Newsgroup and ODP-239 collections demonstrate that our method is successful at extracting significant clusters and is superior to existing methods in terms of quality of clusters. Finally, we apply our system to a collection of 6248 topical sets from the HomoloGene(®) database, a resource in PubMed(®). Empirical evaluation confirms the method is useful for small homogenous datasets in producing meaningful clusters with descriptive titles.Availability and implementationA web-based demonstration of the algorithm applied to a collection of sets from the HomoloGene database is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Wilbur/IRET/CLUSTERING_HOMOLOGENE/index.html.Contactlana.yeganova@nih.govSupplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

BioConceptVec: Creating and evaluating literature-based biomedical concept embeddings on a large scale.

Publications

BioConceptVec: Creating and evaluating literature-based biomedical concept embeddings on a large scale.

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