Dr Daniel Soria
I am an Assistant Professor. My research interests are data mining and artificial intelligence techniques for real-world problems.
|Telephone||0115 95 14212|
|Fax||0115 95 14254|
My main research interests include computational intelligence methodologies (especially in biomedical domains), data mining and data analysis, clustering techniques and classification algorithms.
My PhD thesis title was: Novel methods to elucidate core classes in multi-dimensional biomedical data.
Teaching & Supervision
Current Teaching (2015/2016)
Past Teaching (2014/2015, 2013/2014 & 2012/2013)
Current Supervision duties (2015/2016)
I am the first supervisor of Utkarsh Agrawal and second supervisor of Tajul Rosli Bin Razak.
I am currently second supervisor of a PhD student in the School of Medicine / Royal Derby Hospital working on a decision support system for the management of patients with stable prostate cancer.
Past Supervision duties
Supervisor of two internship students, summer 2014.
I have been the second supervisor of Daphne Lai, a former IMA PhD student who completed her studies in 2014.
Currently I am working on a number of projects, as member of the Advanced Data Analysis Centre (ADAC). Among those:
- NPI+: Improving the Nottingham Prognostic Index (NPI) through the refinement of breast cancer treatment groups, their relationship with survival time and the identification of few critical biological markers.
- WildTech: Novel Technologies for Surveillance of Emerging and Re-emerging Infections of Wildlife. Online resource development and maintenance. The online resource database provides a mechanism for the integration of data critical to the surveillance of wildlife diseases. Data on the WildTech samples are used to populate the database, including the historical data on the sample and the array screening results (EU – FP7 funded).
- ER-related genes and nuclear receptors: identification of key genes and nuclear receptors responsible for the ER positive / negative status of breast cancer patients.
My first post-doc project was supported by the MRC (Medical Research Council).
My PhD was supported by the BIOPTRAIN FP6 Marie-Curie EST Fellowship.
September 2014 onwards: Member of the School’s Research Ethics Committee.
October 2013: Part of the work on NPI+ and breast cancer has hit the news (link).
June 2013: Third best SET scores for teaching large modules, School of Computer Science.
Member of the Technical Committee, International Conference on Biomedical Engineering and Biotechnology (iCBEB), 2013.
Reviewer, Applied Sciences, MDPI AG, 2015.
Reviewer, International Journal of Computational Bioscience, ACTA Press, 2013 and 2014.
Reviewer, Artificial Intelligence in Medicine, Elsevier Journal, 2011 and 2014.
Reviewer, Knowledge-Based Systems, Elsevier Journal, 2010.
For my personal list of publications, please see Google Scholar
The following list is a portion of my publications and a work in progress. For the complete list, please visit HERE.
|(2015): Markers of progression in early-stage invasive breast cancer: a predictive immunohistochemical panel algorithm for distant recurrence risk stratification. In: Breast cancer research and treatment, 151 (2), pp. 325-333, 2015.|
|(2014): A methodology for automatic classification of breast cancer immunohistochemical data using semi-supervised Fuzzy c-Means. In: Central European Journal of Operations Research, 22 (3), pp. 475-499, 2014.|
|(2014): Practical detection of a definitive biomarker panel for Alzheimer’s disease; comparisons between matched plasma and cerebrospinal fluid. In: International Journal of Molecular Epidemiology and Genetics, 5 (2), pp. 53-70, 2014.|
|(2014): Nottingham Prognostic Index Plus (NPI+): a modern clinical decision making tool in breast cancer. In: British Journal of Cancer, 110 (7), pp. 1688-1697, 2014.|
|(2014): Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs. In: Drug Safety, 37 (3), pp. 163-170, 2014.|
|(2014): A Novel Semi-Supervised Algorithm for Rare Prescription Side Effect Discovery. In: IEEE Journal of Biomedical and Health Informatics, 18 (2), pp. 537-547, 2014.|
|(2013): A quantifier-based fuzzy classification system for breast cancer patients. In: Artificial Intelligence in Medicine, 58 (3) , pp. 175-184, 2013.|
|(2013): Biology of primary breast cancer in older women treated by surgery: with correlation with long-term clinical outcome and comparison with their younger counterparts. In: British Journal of Cancer, 108 , pp. 1042-1051, 2013.|
|(2013): Characteristics of basal cytokeratin expression in breast cancer. In: Breast Cancer Research and Treatment, 139 , pp. 23-37, 2013.|
|(2013): Comparison of algorithms that detect drug side effects using electronic healthcare databases. In: Soft Computing, 17(12) , pp. 2381-2397, 2013.|
|(2013): Identification of Key Clinical Phenotypes of Breast Cancer Using a Reduced Panel of Protein Biomarkers. In: British Journal of Cancer, 109 , pp. 1886-1894, 2013.|
|(2012): Novel biological features of early operable primary breast cancer in older women — Based on molecular characterisation using partitional clustering. In: Journal of Geriatric Oncology, 3 Supplement 1 , pp. S36-S37, 2012.|
|(2011): A ‘Non-Parametric’ Version of the Naive Bayes Classifier. In: Knowledge-Based Systems, 24 , pp. 775-784, 2011.|
|(2011): p53 Status Identifies Two Subgroups of Triple-negative Breast Cancers with Distinct Biological Features. In: Japanese Journal of Clinical Oncology, 41 (2) , pp. 172-179, 2011.|
|(2010): A Methodology to Identify Consensus Classes from Clustering Algorithms Applied to Immunohistochemical Data from Breast Cancer Patients. In: Computers in Biology and Medicine, 40 , pp. 318-330, 2010.|
|(2009): Cancer profiles by affinity propagation. In: International Journal of Knowledge Engineering and Soft Data Paradigms (IJKESDP), 1 , pp. 195-215, 2009.|
|(2009): Global Histone Modifications in Breast Cancer Correlate with Tumor Phenotypes, Prognostic Factors, and Patient Outcome. In: Cancer Research, 69 (9) , pp. 3802-3809, 2009.|
|(2008): Identification and Definition of Novel Clinical Phenotypes of Breast Cancer through Concensus Derived from Automated Clustering Methods. In: Breast Cancer Research, 10 , pp. S36, 2008.|
|(2008): Identification of Key Breast Cancer Phenotypes. In: European Journal of Cancer Supplements, 6 (7) , pp. 183, 2008.|
|(2008): Identification of Novel Clinical Phenotypes of Breast Cancer by Immunohistochemical Analysis. In: Modern Pathology, 21 , pp. 34a, 2008.|
|(2007): Identification of Sub-Classes of Breast Cancer Through Consensus Derived From Automated Clustering Methods. In: EJC Supplements, 5 (3) , pp. 59-59, 2007.|
|(2013): Attributes for Causal Inference in Electronic Healthcare Databases. In: CBMS 2013, The 26th IEEE International Symposium on Computer-Based Medical Systems, Porto, pp. 548 - 549, 2013.|
|(2012): Biomarker Clustering of Colorectal Cancer Data to Complement Clinical Classification. In: Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 187 - 191, 2012.|
|(2012): Comparing Data-mining Algorithms Developed for Longitudinal Observational Databases. In: UKCI 2012, the 12th Annual Workshop on Computational Intelligence, Heriot-Watt University, pp. 1-8, 2012.|
|(2012): Discovering Sequential Patterns in a UK General Practice Database. In: 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, pp. 960-963, 2012.|
|(2011): Molecular characterization of primary breast cancer in older women using partitional clustering and correlation with long-term clinical outcome. In: J Clin Oncol 2011, ASCO Annual Meeting Proceedings I 29, pp. 15S:10549, 2011.|
|(2010): A Novel Framework to Elucidate Core Classes in a Dataset. In: Proceedings of 2010 IEEE World Congress on Computational Intelligence (WCCI 2010), Spain, pp. 533-540, 2010.|
|(2010): Consensus Clustering and Fuzzy Classification for Breast Cancer Prognosis. In: The 24th European Conference on Modelling and Simulation, June 1st - 4th, 2010 - Kuala Lumpur, Malaysia, 2010.|
|(2009): Application of Affinity Propagation on a large breast cancer data set. In: Proceedings of Conference on Statistical Methods for the analysis of large data-sets, SIS2009, Pescara, Italy, pp. 531-534, 2009.|
|(2008): A Comparison of Three Different Methods for Classification of Breast Cancer Data. In: Proceedings of the 7th International Conference on Machine Learning and Applications (ICMLA08), San Diego, US, pp. 619-624, 2008.|
|(2008): Cancer profiles by Affinity Propagation. In: Proceedings of the 7th International Conference on Machine Learning and Applications (ICMLA08), San Diego, US, pp. 650-655, 2008.|
|(2008): Clustering Breast Cancer Data by Consensus of Different Validity Indices. In: The 4th International Conference on Advances in Medical, Signal and Information Processing (MEDSIP), Santa Margherita Ligure, Italy, 2008.|
|(2010): Novel Methods to Elucidate Core Classes in Multi-Dimensional Biomedical Data. PhD Thesis, School of Computer Science, University of Nottingham, 2010.|