Here you can find a consolidated (a.k.a. slowly updated) list of my publications. A frequently updated (and possibly noisy) list of works is available on my Google Scholar profile.
Please find below a short list of highlight publications for my recent activity.
Davide, Bacciu; A, Etchells Terence; JG, Lisboa Paulo; Joe, Whittaker
Efficient identification of independence networks using mutual information Journal Article
In: Computational Statistics, vol. 28, no. 2, pp. 621-646, 2013, ISSN: 0943-4062.
@article{bgm2013,
title = {Efficient identification of independence networks using mutual information},
author = {Bacciu Davide and Etchells Terence A and Lisboa Paulo JG and Whittaker Joe},
url = {http://dx.doi.org/10.1007/s00180-012-0320-6},
doi = {10.1007/s00180-012-0320-6},
issn = {0943-4062},
year = {2013},
date = {2013-01-01},
journal = {Computational Statistics},
volume = {28},
number = {2},
pages = {621-646},
publisher = {Springer-Verlag},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
G, Lisboa Paulo J; H, Jarman Ian; A, Etchells Terence; J, Chambers Simon; Davide, Bacciu; Joe, Whittaker; M, Garibaldi Jon; Sandra, Ortega-Martorell; Alfredo, Vellido; O, Ellis Ian
Discovering Hidden Pathways in Bioinformatics Conference
Lecture Notes in Computer ScienceComputational Intelligence Methods for Bioinformatics and Biostatistics, vol. 7548, 2012.
@conference{11568_465481,
title = {Discovering Hidden Pathways in Bioinformatics},
author = {Lisboa Paulo J G and Jarman Ian H and Etchells Terence A and Chambers Simon J and Bacciu Davide and Whittaker Joe and Garibaldi Jon M and Ortega-Martorell Sandra and Vellido Alfredo and Ellis Ian O},
doi = {10.1007/978-3-642-35686-5_5},
year = {2012},
date = {2012-01-01},
booktitle = {Lecture Notes in Computer ScienceComputational Intelligence Methods for Bioinformatics and Biostatistics},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
volume = {7548},
pages = {49--60},
abstract = {The elucidation of biological networks regulating the metabolic basis of disease is critical for understanding disease progression and in identifying therapeutic targets. In molecular biology, this process often starts by clustering expression profiles which are candidates for disease phenotypes. However, each cluster may comprise several overlapping processes that are active in the cluster. This paper outlines empirical results using methods for blind source separation to map the pathways of biomarkers driving independent, hidden processes that underpin the clusters. The method is applied to a protein expression data set measured in tissue from breast cancer patients (n=1,076)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The elucidation of biological networks regulating the metabolic basis of disease is critical for understanding disease progression and in identifying therapeutic targets. In molecular biology, this process often starts by clustering expression profiles which are candidates for disease phenotypes. However, each cluster may comprise several overlapping processes that are active in the cluster. This paper outlines empirical results using methods for blind source separation to map the pathways of biomarkers driving independent, hidden processes that underpin the clusters. The method is applied to a protein expression data set measured in tissue from breast cancer patients (n=1,076)