About the Project
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.
References
Below are some publications from my group. These will give you good indications of the work we have done already and the developments of our ideas, techniques, and implementations.
1. 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).
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
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