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Retrieving, representing and measuring scientific method from the literature

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  • Full or part time
    Dr G Nenadic
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Methods are the vital, but often neglected, under-pinnings of science. One problem facing scientists is finding out what methods are being used to do what experimental task. This is what we want to do in the context of computational biology - retrieve, represent and measure the use of associated methods. Using text-mining we have had success in recovering a general view of the methods described in the computational biology literature. Now what we want to do in this project is to recover and represent specific methods from specific computational biology articles. This raises interesting and difficult questions about ordering of entities in text; recovery of parameters, versions, data, and then co-ordinating, reconciling and representing all of these entities. On top of this, measures of completeness - how much of a method is present - as well as of - best practice - would be fascinating elements of this work.

This project will be suitable for a highly motivated individual with a first class (or equivalent) undergraduate degree in computer science or bioinformatics, with excellent experience in programming. Completed (or soon to be completed) master studies in a related area (including health or bio-informatics, knowledge representation, text analytics) would be an advantage.

Funding Notes

Candidates who have been offered a place for PhD study in the School of Computer Science may be considered for funding by the School. Further details on School funding can be found at: http://www.cs.manchester.ac.uk/study/postgraduate-research/programmes/phd/funding/school-studentships/.


The minimum requirements to get a place in our PhD programme are available from:

How good is research at University of Manchester in Computer Science and Informatics?

FTE Category A staff submitted: 44.86

Research output data provided by the Research Excellence Framework (REF)

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