Text mining-based word representations for biomedical data analysis and protein-protein interaction networks in machine learning tasks.

ABSTRACT: Biomedical and life science literature is an essential way to publish experimental results. With the rapid growth of the number of new publications, the amount of scientific knowledge represented in free text is increasing remarkably. There has been much interest in developing techniques that can extract this knowledge and make it accessible to aid scientists in discovering new relationships between biological entities and answering biological questions. Making use of the word2vec approach, we generated word vector representations based on a corpus consisting of over 16 million PubMed abstracts. We developed a text mining pipeline to produce word2vec embeddings with different properties and performed validation experiments to assess their utility for biomedical analysis. An important pre-p

SUBMITTER: Alachram H 

PROVIDER: S-EPMC8519453 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature