Using word evolution to predict drug repurposing

TL;DR

This paper explores using changes in word contexts over time to identify drugs that could be repurposed for new medical uses.

Contribution

A novel approach using word evolution and time series word embeddings to predict drug repurposing is introduced.

Findings

Word embeddings from MEDLINE publications were used to detect drugs suitable for repurposing.

Classification performance reached 65% with UMLS labels and 81% with SemRep labels.

Different deep learning architectures perform better depending on the annotation method and training data quantity.

Abstract

Traditional literature based discovery is based on connecting knowledge pairs extracted from separate publications via a common mid point to derive previously unseen knowledge pairs. To avoid the over generation often associated with this approach, we explore an alternative method based on word evolution. Word evolution examines the changing contexts of a word to identify changes in its meaning or associations. We investigate the possibility of using changing word contexts to detect drugs suitable for repurposing.

Word embeddings, which represent a word’s context, are constructed from chronologically ordered publications in MEDLINE at bi-monthly intervals, yielding a time series of word embeddings for each word. Focusing on clinical drugs only, any drugs repurposed in the final time segment of the time series are annotated as positive examples. The decision regarding the drug’s repurposing is based either on the Unified Medical Language System (UMLS), or semantic triples extracted using SemRep from MEDLINE.

The annotated data allows deep learning classification, with a 5-fold cross validation, to be performed and multiple architectures to be explored. Performance of 65% using UMLS labels, and 81% using SemRep labels is attained, indicating the technique’s suitability for the detection of candidate drugs for repurposing. The investigation also shows that different architectures are linked to the quantities of training data available and therefore that different models should be trained for every annotation approach.

The online version contains supplementary material available at 10.1186/s12911-024-02496-1.

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