Skip to main content

Predicting therapy responses in psoriasis

PGR-P-772

Key facts

Type of research degree
PhD
Application deadline
Friday 5 June 2020
Project start date
Thursday 1 October 2020
Country eligibility
UK and EU
Funding
Funded
Source of funding
Charity
Supervisors
Dr Miriam Wittmann
Schools
School of Medicine
Research groups/institutes
Leeds Institute of Rheumatic and Musculoskeletal Medicine
<h2 class="heading hide-accessible">Summary</h2>

Treatment options for inflammatory skin diseases such as psoriasis have greatly improved over the last decade. However, our current inability to predict who will respond best to which treatment causes frustration for both clinicians and patients.

<h2 class="heading hide-accessible">Full description</h2>

<p>In this project we aim to predict treatment responses by integrating clinical information, serum markers and biological information from the affected organ, the skin. We have previously optimised a non-invasive tape stripping sampling procedure and are able to reliably quantitate several thousand proteins directly from the skin to input in to our &ldquo;treatment prediction algorithm&rdquo;. The key challenge is to find &ldquo;patterns&rdquo; within these large multiparameter datasets which accurately predict responses to therapy for individual patients. For this we will adapt a machine learning approach (i.e. deep Learning methods, such as convolutional neural network (CNN) and recurrent neural network (RNN)). All different types of data obtained will be used &nbsp;to design and implement &nbsp;the pipeline to detect patterns related to clinical outcome.</p> <h6>Training, Supervision and Environment:</h6> <p>The multidisciplinary supervisor team covering clinical dermatology, laboratory-based skin inflammation research and machine learning/AI will ensure a broad development opportunity from clinic, to lab and data analysis. You will join the Leeds PhD training programme.&nbsp;</p> <h6>Specific training opportunities include</h6> <p>This multidisciplinary studentship will provide extensive experience in machine learning, HPC and cloud computing, bioinformatics, proteomics, skin inflammation, molecular and cellular aspects of psoriatic disease as well as clinical trials methodology.<br /> &nbsp; &nbsp;</p>

<h2 class="heading">How to apply</h2>

<p>To apply for this project applicants should complete a<a href="https://medicinehealth.leeds.ac.uk/downloads/download/129/faculty_graduate_school_-_application_form"> Faculty Application Form</a> and send this alongside a full academic CV, degree certificates and transcripts (or marks so far if still studying) to the Faculty Graduate School <a href="mailto:fmhpgradmissions@leeds.ac.uk">fmhpgradmissions@leeds.ac.uk</a>&nbsp;</p> <p>We also require 2 academic references to support your application. Please ask your referees to send these <a href="https://medicinehealth.leeds.ac.uk/downloads/download/130/faculty_graduate_school_-_scholarship_reference_form">references</a> on your behalf, directly to <a href="mailto:fmhpgradmissions@leeds.ac.uk">fmhpgradmissions@leeds.ac.uk</a>&nbsp;by no later than Friday 5 June 2020.</p> <p>If you have already applied for other projects using the Faculty Application Form this academic session you do not need to complete this form again. Instead you should email fmhgrad to inform us you would like to be considered for this project.</p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p><em>We welcome applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.</em></p>

<h2 class="heading heading--sm">Entry requirements</h2>

Candidates are expected to hold/about to obtain a minimum upper second class honours degree or equivalent in a related areas which can include biomedical sciences, medical subjects, biochemistry, computer science, data science or bioinformatics. Candidates with interest in proteomics and inflammatory skin diseases are encouraged to apply. Working experience in machine learning, Python programming or Tensorflow, Keras, Pytorch, GPU systems would be most welcome.

<h2 class="heading heading--sm">English language requirements</h2>

Applicants whose first language is not English must provide evidence that their English language is sufficient to meet the specific demands of their study. The Faculty of Medicine and Health minimum requirements in IELTS and TOEFL tests for PhD, MSc, MPhil, MD are: &bull; British Council IELTS - score of 6.5 overall, with no element less than 6.0 &bull; TOEFL iBT - overall score of 92 with the listening and reading element no less than 21, writing element no less than 22 and the speaking element no less than 23.

<h2 class="heading">Funding on offer</h2>

<p>This PhD scholarship is available for UK and EU citizens only. The scholarship will attract an annual tax-free stipend of &pound;15,285, subject to satisfactory progress and will cover the UK/EU tuition fees.&nbsp;This scholarship project is funded by the&nbsp;Psoriasis Association.&nbsp;</p>

<h2 class="heading">Contact details</h2>

<p>For further information please contact the Graduate School Office<br /> e:&nbsp;<a href="mailto:fmhpgradmissions@leeds.ac.uk">fmhpgradmissions@leeds.ac.uk</a>,&nbsp;</p>