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Explainable and mechanism-aware deep learning for biomedicine and biotechnology


   Centre for Digital Innovation

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  Dr C Angione  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

In the machine learning era, the effective integration of data-driven and knowledge-driven approaches is being increasingly recognised as key to improving biotechnological processes and biomarker detection in several diseases. Several “black box” machine learning methods have recently been developed using tumour images and gene/protein expression profiles. However, “black box” approaches based on machine learning suffer from a lack of interpretability, which often prevents the applicability to real case studies.

Methodology

 This project aims at developing a “white box” deep learning approach whose predictions are aware of biochemical mechanisms. Specifically, the approach will be a multi-omic and multi-platform biomarker discovery method based on a combination of deep learning and metabolic modelling. The pipeline will combine biological models, computational models, and purpose-built machine learning algorithms. Two applications are planned for the pipeline: (i) breast cancer data from The Cancer Genome Atlas; (ii) bioprocessing data from our ongoing collaboration with FUJIFILM Diosynth Biotechnology.

For the breast cancer case study, we aim to: (i) classify cancer patients into different sub-groups associated with the prediction of poor survival, recurrence, and metastatic status, while highlighting system-level pathway differences; (ii) discover, and mechanistically explain, the role of novel biomarkers of tumour aggressiveness leading to poor survival in breast cancer, suggesting targets for improving treatment and preventing metastasis.

For the biotechnology-related application, we will evaluate the applicability of the pipeline for bacterial cultures. Leveraging data provided by FUJIFILM Diosynth Biotechnology, we will connect bioprocess outcomes to metabolic events underlying the same processes. By integrating a mechanistic model of Escherichia coli with bioprocess data from the FUJIFILM ambr250 system, we will: (i) predict end-stage process performance during production; (ii) identify metabolic drivers underlying process performance; and (iii) determine the metabolic impact of experimental design parameters.

The validation of the proposed pipeline will be achieved through ongoing collaborations with the National Horizon Centre and FUJIFILM. The top-5 biomarkers will be further validated at the protein level, using analytical proteomics and quantitative protein expression measurements with immunohistochemically-stained fixed tissue samples.

This would provide, for the first time, accurate predictions fully based on both transcriptomics and multi-omic activity at the genome scale. Importantly, these predictions will be interpreted mechanistically and transparently (“white box”) through the underlying metabolic model, therefore with significantly higher potential for prognosis and therapeutic interventions compared to traditional black-box machine learning approaches.

Project team

The supervision team will include: (i) two members of the School of Computing, Engineering and Digital Technologies to supervise the development and optimisation of the AI pipeline; (ii) one member of the National Horizon Centre to help validate the pipeline; (iii) two leading staff members at Fujifilm Diosynth Biotechnologies, to provide a case study and datasets on biotherapeutics and biotechnologies.

  • Dr Claudio Angione (Senior Lecturer in Data Analytics)
  • Dr Annalisa Occhipinti (Associate Professor in Computer Science)
  • Dr Panagiota Filippou (Lecturer in Biomedical Sciences, National Horizon Centre)
  • Dr Chris Lennon (Subject Matter Expert in Molecular & Microbiology at FUJIFILM Diosynth Biotechnologies
  • Dr Graham McCreath (Senior Director, Data Science at FUJIFILM Diosynth Biotechnologies)

Entry Requirements

Applicants should hold or expect to obtain a good honours degree (2:1 or above) in a relevant discipline. A masters level qualification in a relevant discipline is desirable, but not essential, as well as a demonstrable understanding of the research area. Further details of the expected background may appear in the specific project details. International students will be subject to the standard entry criteria relating to English language ability, ATAS clearance and, when relevant, UK visa requirements and procedures.

How to Apply

Applicants should apply online for this opportunity at: https://e-vision.tees.ac.uk/si_prod/userdocs/web/apply.html?CourseID=1191

Please use the Online Application (Funded PHD) application form. When asked to specify funding select “other” and enter ‘RDS’ and the title of the PhD project that you are applying for. You should ensure that you clearly indicate that you are applying for a Funded Studentship and the title of the topic or project on the proposal that you will need to upload when applying. If you would like to apply for more than one project, you will need to complete a further application form and specify the relevant title for each application to a topic or project.

Applications for studentships that do not clearly indicate that the application is for a Funded Studentship and state the title of the project applied for on the proposal may mean that your application may not be considered for the appropriate funding.

For academic enquiries, please contact Dr Claudio Angione, [Email Address Removed]  

For administrative enquiries before or when making your application, contact [Email Address Removed]


Funding Notes

The Fees-Paid PhD studentship will cover all tuition fees for the period of a full-time PhD Registration of up to four years. Successful applicants who are eligible will be able to access the UK Doctoral Loan scheme https://www.gov.uk/doctoral-loan to support with living costs. The Fully Funded PhD Studentship covers tuition fees for the period of a full-time PhD Registration of up to four years and provide an annual tax-free stipend of £15,000 for three years, subject to satisfactory progress. Applicants who are employed and their employer is interested in funding a PhD, can apply for a Collaborative Studentship.
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