Clustering in bioinformatics
There are many bioinformatics datasets available publically. Extracting consistent information from these require development of novel techniques and tools. This has many applications in medical (and biological) fields with direct consequences for developing drugs, helping doctors to deliver services, and finally curing patients.
Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. In addition, most algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. The primary aim is to develop a clustering paradigm with better performance specifically for biological studies with a view to gene discovery.
This project will involve programming, signal processing, machine learning, mathematical analysis, and good writing ability for presentation of technical work. An ideal candidate will have a very good Master degree or a First Class Bachelor degree. Below are some publications from my group. These will give you some indications of the work we have done already.
- B Abu Jamous, R Fa, and A K Nandi, "Integrative Cluster Analysis in Bioinformatics", Published by John Wiley & Sons, Chichester, West Sussex, UK, 2015 (ISBN 978-1-118-90653-8).
- B Abu Jamous, F M Buffa, A L Harris, and A K Nandi, “In vitro downregulated hypoxia transcriptome is associated with poor prognosis in breast cancer", Molecular Cancer, DOI: 10.1186/s12943-017-0673-0, vol. 16, no. 105, (19 pages), 2017.
- B Abu Jamous, R Fa, D J Roberts, and A K Nandi, “UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets", BMC Bioinformatics, DOI: 10.1186/s12859-015-0614-0, vol. 16, no. 184, 2015.
- B Abu Jamous, R Fa, D J Roberts, and A K Nandi, “Comprehensive analysis of forty yeast microarray datasets reveals a novel subset of genes (APha-RiB) consistently negatively associated with ribosome biogenesis", BMC Bioinformatics, DOI: 10.1186/1471-2105-15-322, vol. 15, no. 322, 2014.
- B Abu Jamous, R Fa, D J Roberts, and A K Nandi, “Paradigm of tunable clustering using binarization of consensus partition matrices (Bi-CoPaM) for gene discovery", PLoS ONE vol. 8, no. 2, doi:10.1371/journal.pone.0056432, 2013.
- B Abu Jamous, R Fa, D J Roberts, and A K Nandi, “Yeast gene CMR1/YDL156W is consistently co-expressed with genes participating in DNA-metabolic processes in a variety of stringent clustering experiments", J. R. Soc. Interface, vol. 10, no. 81, doi: 10.1098/rsif.2012.0990, 2013.
This project will involve programming, signal processing, machine learning, mathematical analysis and require good writing ability for presentation of technical work.
An ideal candidate will have a very good Master degree or a First Class Bachelor degree.
How to apply
If you are interested in applying for the above PhD topic please follow the steps below:
- Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
- Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
- Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.
This is a self funded topic
Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.