Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network.

ABSTRACT: The increased diversity and scale of published biological data has to led to a growing appreciation for the applications of machine learning and statistical methodologies to gain new insights. Key to achieving this aim is solving the Relationship Extraction problem which specifies the semantic interaction between two or more biological entities in a published study. Here, we employed two deep neural network natural language processing (NLP) methods, namely: the continuous bag of words (CBOW), and the bi-directional long short-term memory (bi-LSTM). These methods were employed to predict relations between entities that describe protein subcellular localisation in plants. We applied our system to 1700 published Arabidopsis protein subcellular studies from the SUBA manually curated dataset. T

SUBMITTER: David R 

PROVIDER: S-EPMC7813825 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature