Prof. J.P.G. Lisboa
School of Computing and Mathematical Sciences
Liverpool John Moores University
Byrom Street
Liverpool
L3 3AF
United Kingdom
E mail: P.J.Lisboa@ljmu.ac.uk
Advances in systems biology, together with growing expectations about referring the
evidence base to the needs of the individual patient, have given rise to the so-called 4P
agenda of preventive, predictive, personalised and participatory healthcare. This
agenda gained momentum as a way to contain spiralling healthcare costs with more
timely and effective interventions, requiring better modelling of the phenomenology of
disease, in particular for cancer.
The following two talks are linked. They will review the application of generic non-linear
models in two aspects of cancer care, namely:
* supervised modelling of failure time series, i.e. survival modelling of longitudinal
cohort data.
* robust methods for unsupervised clustering of disease sub-types and for cohort-based
visualisation of high-dimensional data.
Talk 1. Generic non-linear models of prognostic outcome
Longitudinal cohort studies provide evidence for patient stratification by outcome. This
has both interpretative value by characterizing outcome differentials and predictive
value by estimating expected survival. In this talk, linear statistical methods are
extended into a generic non-linear time dependent framework for outcome modelling. A
prototype clinical interface is shown and developments of the model to integrate
proteomic data are discussed.
A detailed case study is presented of survival of patients with early breast cancer. This
shows the predictive power of new data (ER status and histological grade) and also
provides smooth estimates over time for the hazard ratios and covariate effects.
Stratification methods are shown to generalize well from the calibration data to data
from another clinical centre, on which the AdjuvantOnline model was validated.
Talk 2. Robust methodologies for partition clustering
Clustering issues are fundamental to exploratory data analysis. This process may
follow algorithms that give unique answers by making assumptions about the ability to
find global optima. Most methods, such as k-means, are sensitive to the initial
conditions. This talk presents a methodology to make the variability in cluster solutions
dependent on the initial conditions, to find a hierarchy of useful cluster partitions which
should be profiled for practical interpretation by domain experts.
The methodology is generic and it is presented in the talk by reference to a
bioinformatics, specifically the sub-typing of cancer using a protein expression data set
derived from resected biopsies (n=1076).
References.
Lisboa. P.J.G., Vellido, A. and Wong, H. 'Bias Reduction in Skewed Binary Classification
with Bayesian Neural Networks' Neural Networks, 13, 407-410, 2000.
Etchells, T.A. and Lisboa, P.J.G. 'Orthogonal search-based rule extraction (OSRE) from
trained neural networks: a practical and efficient approach' IEEE Transactions on Neural
Networks, 17 (2):374-384, 2006.
Lisboa, P.J.G. and Taktak, A.F.G. 'The use of artificial neural networks in decision
support in cancer: a systematic review', Neural Networks, 19: 408-415, 2006.
Aung, M.S.H, Lisboa, P.J.G., Etchells, T.A., Testa, A.C., Van Calster, B., Van Huffel, S.,
Valentin, L. and Timmerman, D. 'Comparing Analytical Decision Support Models
Through Boolean Rule Extraction: A Case Study of Ovarian Tumour Malignancy' Lecture
Notes in Computer Science 4492:1177-1186 , 2007.
Taktak, A., Antolini, L., Aung, M.H., Boracchi, P., Campbell, I., Damato, B., Ifeachor,
E.C, Lama, N., Lisboa, P.J.G, Setzkorn, C., Stalbovskaya, V. and Biganzoli, E.M.
'Double-blind evaluation and benchmarking of survival models in a multi-centre study'
Computers in Biology and Medicine, 37 (8): 1108-1120, 2007.
Lisboa, P.J.G., Etchells, T.A., Jarman, I.H., Aung, M.S.H., Chabaoud, S., Bachelot, T.,
Perol, D., Gargi, T., Bourdès, V, Bonnevay, S and Négrier, S. 'Time-to-event analysis
with artificial neural networks: an integrated analytical and rule-based study for breast
cancer' Neural Networks, 21(2-3): 414-426, 2008.
Jarman, I.H., Etchells, T.A., Martín, J.D. and Lisboa, P.J.G. 'An integrated framework for
risk profiling of breast cancer patients following surgery' Artificial Intelligence in
Medicine, 42: 165-188, 2008.
Lisboa, P.J.G., Ellis, I.O., Green, A.R., Ambrogi, F. and M.B. Dias 'Cluster-based
visualisation with scatter matrices', Pattern Recognition Letters, 29(13): 1814-1823,
2008.
Lisboa, P.J.G., Etchells, T.A., Jarman, I.H., Arsene, C.T.C., Aung, M.S.H., Eleuteri, A.,
Taktak, A. F. G., Ambrogi, F., Boracchi, P. and Biganzoli, E.M. 'Partial Logistic Artificial
Neural Network for Competing Risks Regularised with Automatic Relevance
Determination ' I.E.E.E. Transactions on Neural Networks, 20(9):1403-1416, 2009.
Fernandes, A.S., Fonseca, J.M., Jarman, I.H., Etchells, T.A., Lisboa, P.J.G., Biganzoli,
E.M. and Bajdik, C. 'Evaluation of missing data imputation in longitudinal cohort studies
in breast cancer survival' International Journal of Knowledge Engineering and Soft Data
Paradigms, 1(3):257-275, 2009.
Lisboa, P.J.G., Vellido, A., Tagliaferri, R., Napolitano, F., Ceccarelli, M., Martín-Guerrero,
J.D. and Biganzoli, E. 'Data Mining in Cancer Research', Invited Paper, IEEE
Computational Intelligence Magazine, February 2010:14-18.
Soria, D., Garibaldi, J.M., Ambrogi, F., Green, A.R., Powe, D., Rakha, E., Douglas
Macmillan, R., Blamey, R.W., Ball, G., Lisboa, P.J.G., Etchells, T.A., Boracchi, P.,
Biganzoli, E. And Ellis, I.O. 'A methodology to identify consensus classes from clustering
algorithms applied to immunohistochemical data from breast cancer patients' accepted
for Computers in Biology and Medicine, 40:318-330, 2010.