Blog

Research: Biomedical relation extraction (BERT)

In natural language processing, words must be given numerical representations before they can be passed as input to a machine learning model.

A word is usually represented as a vector (a list of numbers). These word vectors must adequately capture the meaning of the words; semantically related words should have similar numbers. The better these representations, the stronger the performance of the machine learning model is likely to be.

Often, many methods of learning word vectors give a word the same vector regardless of the context that it appears in. However, it is not unusual for a word to have several different meanings. A classic example of this is the word “bank” in the following two sentences:

“I walked along the river bank”,
“I deposited some money into my bank account”.

If the same vector is used to represent “bank” in both instances, this impairs the performance of the downstream machine learning model which takes these word vectors as input.

Over the last few years, a lot of research has been done on learning contextual word vectors; these are word vectors which vary depending on the context in which the word appears. This enables the same word to have different vectors depending on how it is used in a sentence. A recent paper (Devlin et al, 2018) introduced BERT (Bidirectional Encoder Representations from Transformers), a new way of learning contextual word vectors. BERT utilises a powerful encoder architecture which is capable of modelling longer range dependencies between words. It also proposed an innovative way of capturing the context both before and after the word in the sentence. With these better contextual vectors, BERT achieved state of the art performance on several tasks within natural language processing.

In a recent paper, we proposed a new relation extraction model built on top of BERT. Given any paragraph of text (for example, the abstract of a biomedical journal article), our model will extract all gene-disease pairs which exhibit a pre-specified relation. In our paper, the relations we were interested in concerned the function change experienced by a gene mutation which affects the disease progression. The word vectors supplied by BERT provide our model with a way of encoding the meaning expressed in the text in regard to our entities of interest. We then further fine-tune this encoding so that it can more accurately identify when a paragraph of text contains a gene-disease relation of interest. Such relation extraction models are crucial in drug discovery; there are too many journal articles published every day for a human to read and summarise. A machine learning model capable of automatically extracting relevant gene-disease pairs can greatly accelerate this process.

More Posts

You Might Also Like

News
BenevolentAI Announces Board Changes
BenevolentAI today announces the appointment of Dr. Susan Liautaud as a member of the board of directors of the Company with effect from 30 June 2022. Dr. Susan Liautaud will act as Independent Non-Executive Director of the Company.
May 25, 2022
News
BenevolentAI achieves third milestone in its AI-enabled drug discovery collaboration with AstraZeneca
AstraZeneca selects another novel target for idiopathic pulmonary fibrosis from the collaboration for its drug development portfolio.
May 17, 2022
Blog
FDA converts emergency approval of baricitinib — first identified as a COVID treatment by BenevolentAI — to a full approval
The FDA has converted its emergency approval of baricitinib to a full approval, underscoring the strength of BenevolentAI’s AI-derived hypothesis.
May 12, 2022
News
BenevolentAI Begins Trading On Euronext Amsterdam
BenevolentAI, a leading, clinical-stage AI-enabled drug discovery company, announces that trading in its shares is expected to begin today, following completion of its business combination with Odyssey Acquisition S.A. on 22 April 2022.
Apr 25, 2022
Video
BenevolentAI · AI-Enabled Drug Discovery
Advanced technologies, combined with an exponential increase in biomedical data and research, provide an unparalleled opportunity to unravel the mysteries of diseases that have gone untreated for too long.
Apr 25, 2022
News
BenevolentAI joins the World Economic Forum’s Global Innovators Community
BenevolentAI joins the World Economic Forum’s Global Innovators Community to help ethically deploy AI to create a more sustainable, inclusive and resilient world.
Apr 21, 2022