Access keys

Skip to content Accessibility Home News, events and publications Site map Search Privacy policy Help Contact us Terms of use

Machine Learning to Generate New Biological Understanding 2018

Copyright: BeeBright, iStock

Call status: Open
18 January 2018 - 24 April 2018

Funded through responsive mode

Background

Machine learning is a powerful technique that allows the construction of algorithms that can learn from data and make predictions. As such, it has current and future potential to play a crucial role in one of the pressing problems in current biological research, namely how to interpret increasingly large and complex datasets to further understanding of complex biological systems. This may include extracting information from datasets that may be ambiguous or contain significant noise. The potential applications to biological sciences are broad, with the techniques being increasingly applied to both numerical and image datasets.

Machine Learning is part of the Data Driven Biology strategic priority (see related links).

Scope

Applications should seek to utilise machine learning techniques to derive new biological knowledge in the following areas:

  1. Machine Learning to elucidate gene function
  2. New and innovative applications of Machine Learning, including novel application to different types of biological datasets

Effective machine learning requires the development of suitable well described and annotated datasets, at a scale sufficient to train algorithms which is a non-trivial task. All applications within the highlight should clearly describe the development of these datasets, and how they will be subsequently made available to other researchers.

How to apply

In order to ensure fit to the scope of the highlight and to manage/understand the demand, applicants interested in applying under the highlight should submit an Expression of Interest (EOI). This should describe the proposed research topic and the fit to the call, using the template provided. EOIs should be sent by email to the Genomics, Data and Technologies inbox (GDT@BBSRC.ac.uk) by 12:00 on 26 February 2018.  

Applicants submitting an EOI will then be contacted in early March 2018 to inform them whether their proposal is appropriate for this highlight. Applicants with applications appropriate to the highlight will submit through the current responsive mode round (18RM2, application deadline 24 April 2018, 4pm) and their applications will be assessed at the Research Committee meeting on 19-20 September 2018.

Exclusions

Although machine learning can be considered as a subset of both artificial intelligence, and computational statistics, this highlight is specifically seeking to exploit advances in machine learning, rather than looking at using alternative, related technologies. However, applications relevant to furthering biological understanding using these related technologies are welcome in responsive mode under the Data Driven Biology priority.

Additional notes

We recognise the highly interdisciplinary nature of these projects, and we welcome projects led by researchers from disciplines other than biosciences.

We also welcome appropriate industry partnerships which may include ‘stand-alone LINK’ awards or Industrial Partnership awards (see related links).

Given the importance of well described datasets of suitable volume in the development and deployment of machine learning techniques, applicants must give appropriate consideration to making these available in line with the BBSRC data sharing policy (see related links). We are happy to discuss any questions that applicants may have regarding sharing data.

We note the significant contribution of staff such as Research Software Engineers (see external links) to interdisciplinary computational projects, and support recognition of their contributions and encourage applicants to cost them appropriately on research grant applications.

Machine Learning EoI template (DOCX 22KB)

You may need to download additional plug-ins to open this file.

Contact

Daniela Hensen



Related calls: bioinformatics data multidisciplinary research tecnologies responsive mode responsive mode