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Mining biomedical publications to the defeat the pandemic: Identifying relational knowledge between scientific claims

Eligibility: UK/International (including EU) graduates with the required entry requirements

Funding details: Bursary plus tuition fees

Duration: Full time - between three and three and a half years fixed term

Application deadline: 31 May 2022

Interview dates: 10 June 2022

Start date: September 2022

To find out more about the project, please contact Dr. Xiaorui Jiang.


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Introduction

The Centre for Computational Science and Mathematical Modelling at Coventry University is inviting applications from suitably qualified graduates for a fully-funded PhD studentship. The successful candidate will join the project ‘Mining biomedical publications to defeat the pandemic: Identifying relational knowledge between scientific claims’ led by Assistant Professor (Senior Lecturer) Dr Xiaorui Jiang (Natural Language Processing & Scientometrics) at Coventry University, and co-supervised by Professor Vasile Palade (Artificial Intelligence).

Project details

In order to deal with the challenges posed by the COVID-19 pandemic, there has been an unprecedented pressure on researchers to quickly understand an explosive number of publications on this topic and try to find a solution within a short timescale. Existing biomedical text mining tools have not proved helpful, as they only support literature browsing, searching, and recommendation.

This project aims at the challenging task of mining deep actionable scientific knowledge, i.e., concluding scientific claims around entities, such as what medical compounds suppress COVID-19 symptoms, how vaccine protects from new variants, etc. Corroborating or contradicting relations between claims will be extracted too. Researchers use such available knowledge in order to generate new knowledge. To better prepare for a potential future pandemic of coronaviruses like COVID-19, this project also tries to overcome the obstacle of (semi-)automated large-scale dataset creation, by using new strategies including self-supervised training and human-in-the-loop approach.

To achieve these objectives, this project will build on the core idea of understanding scientificality. We will embark on the first efforts in developing novel methodologies to empirically qualify phrases and sentences that are of scientific value, and from there we extract evidenced scientific claims and relations between claims. With such knowledge, we foresee a new paradigm for scientific information literacy for the post-COVID world.

Funding

This project is fully-funded, including tuition fee and stipend/bursary. 

Benefits

The project is directed from the Research Centre for Computational Sciences and Mathematical Modelling (CSM). Staff from CSM largely run the MSc Data Science and Computational Intelligence programme.

Should the PhD candidate require training on data science, artificial intelligence, and machine learning (including deep learning), then modules from this programme may be undertaken in the first year. There is also a module on Natural Language Processing (7120CEM) under development that will start running in the next academic year. 

In addition, the Centre runs activities year-round for the development of their PhD students including: the CSM 5-minute thesis competition, thesis writing bootcamps, and round table discussions on the following topics:

  • Giving seminars
  • Writing papers
  • Peer reviewing an article for a journal
  • What is a research question anyway?
  • How should I read an article – there are so many to read!
  • The importance of a good abstract
  • There is of course also the main research seminar program of the centre.

In addition

The Doctoral College together with the Centre for Research Capability and Development (RECAP) will deliver a wide range of research-informed training and development initiatives for the successful PhD candidate, including the PGR Welcome Programme, PGR Development Programme, PDP (Professional Development) Programme, Research Methods Programmes, and Research Writing Support etc. RECAP supports the development of professional research skills through a combination of workshops, digital resources, reading groups, activity-based learning, peer mentoring, and 1-2-1 support.

This project will also seek cross-centre collaboration from experiences scientists with expertise in epidemiology from the Centre for Intelligent Healthcare. We are expected to collaborate at various stages of the project in dataset annotation, algorithm design (for models enhanced with biomedical knowledge) and system evaluation.

Candidate specification

Entry criteria for applicants to PhD at Coventry University:

  • A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 65% mark in the project element or equivalent with a minimum 65% overall module average.

OR

  • A Master’s Degree in a relevant subject area will be considered as an equivalent. The Masters must have been attained with overall marks at 65%. In addition, the dissertation or equivalent element in the Masters must also have been attained with a mark at 65%.

PLUS

  • The potential to engage in innovative research and to complete the PhD within a 3.5 years
  • A minimum of English language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component)
  • Strong, demonstrable programming skills and good mathematical grounding are essential
  • Experience with machine learning techniques in the context of text processing or other applications is desirable.

How to apply

To find out more about the project, please contact Dr. Xiaorui Jiang. Please note all applications require full supporting documentation, and a covering letter – plus the following:

  • For pre-determined (named) projects an up-to 2000 word supporting statement is required showing how the applicant’s expertise and interests are relevant to the project.
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