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(MCRC Non-Clinical) Neural information extraction for literature-based discovery of cancer mechanisms

Project Description

The PhD will advance NLP research related with the automated extraction of cancer mechanisms from text by focussing on the identification of events, i.e., those involving entities that could explain interrelated phenomena occurring at various levels of biological organisation (e.g., molecule, cell, tissue, organ, organism), and the context under which they were observed. Event extraction will be based on novel deep learning techniques using joint training and multi-task learning. Contextual information of the extracted mechanisms will be quantitatively assessed based on the analysis of the language used to describe them. This information will support literature-based discovery for cancer mechanisms by inferring and ranking complex associations in context, and by combining results from text mining and omics data. Validation of the text mining methods will be conducted by replicating existing cancer associations. The proposed research will also facilitate signalling pathway reconstructions through the development of advanced text mining that will offer a paradigm shift in cancer model development, making the synthesis of information about cancer mechanisms from the literature more accurate and manageable.

Entry Requirements
Candidates must hold, or be about to obtain, a minimum upper second class (or equivalent) undergraduate degree in relevant subject. A related master’s degree would be an advantage.

Funding Notes

The Studentship will cover an annual stipend (currently at £19,000 per annum), running expenses and PhD tuition fees at UK/EU rates. Where international student fees are payable, please provide evidence within your application of how the shortfall will be covered (approximately £19,000 per annum).

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

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