New Algorithmic Techniques for Cancer Informatics
Lead Research Organisation:
University of Bristol
Department Name: Engineering Mathematics and Technology
Abstract
In this project we will use advanced data analysis techniques to uncover new biological insights within cancer research. Some of these new algorithmic methods are of our own invention and some new developments are proposed. These new methods include techniques to search for fusion genes: these are genetic anomalies if which two genetic coding regions are fused to produce a novel chimeric protein which causes cell proliferation. These and other targets may be found using new algorithmic methods which search through the large amounts od adat currently appearing in the biomedical sciences.
Organisations
Publications
Agius P
(2009)
Bayesian unsupervised learning with multiple data types.
in Statistical applications in genetics and molecular biology
Campbell C
(2011)
Learning with Support Vector Machines
in Synthesis Lectures on Artificial Intelligence and Machine Learning
Campbell C
(2014)
Springer Handbook of Bio-/Neuroinformatics
Huang K
(2009)
GSML: A Unified Framework for Sparse Metric Learning
Huang K
(2010)
Generalized sparse metric learning with relative comparisons
in Knowledge and Information Systems
Peng Li
(2009)
A Variational Approach to Semi-Supervised Clustering
Y Ying
(2009)
Class prediction from disparate biological
Y Ying
(2009)
Sparse metric learning via smooth optimization
Yiming Ying
(2009)
Analysis of SVM with Indefinite Kernels
| Description | A variety of data integration methods e.g. Phaedra Agius, Yiming Ying and Colin Campbell. Bayesian Unsupervised Learning with Multiple Data Types. Statistical Applications in Genetics and Molecular Biology: Volume 8, Issue 1, Article 27 (2009). for both supervised and unsupervised learning, used in a variety of later biomedical applications e.g. •Jose Seoane, Ian Day, Tom Gaunt and Colin Campbell. A pathway-based data integration framework for prediction of disease progression. Bioinformatics (2014) 30 (6): 838-845. •Hashem Shihab, Mark Rogers, Julian Gough, Matthew Mort, David Cooper, Ian Day, Tom Gaunt and Colin Campbell. An Integrative Approach to Predicting the Functional Effects of Non-Coding and Coding Sequence Variation Bioinformatics doi: 10.1093/bioinformatics/btv009 (2015). M. Rogers, C. Campbell and Y. Ying. Probabilistic inference of biological networks via data integration BioMed Research International Article ID 707453 (2014). |
| Exploitation Route | Foundation work underlying some later application work (see later grants) |
| Sectors | Pharmaceuticals and Medical Biotechnology |
| URL | http://seis.bris.ac.uk/~enicgc/index.htm |
| Description | MRC Responsive mode |
| Amount | £557 (GBP) |
| Funding ID | G1000427 |
| Organisation | Medical Research Council (MRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 06/2010 |
| End | 06/2014 |