Guest Editorial: Data Mining in Bioinformatics
Reps, J.M., Garibaldi, J.M., Aickelin, U., Soria, D., Gibson, J.E. and Hubbard, R.B. 2014. Guest Editorial: Data Mining in Bioinformatics. IEEE Journal of Biomedical and Health Informatics. 18 (2), p. 483. doi:10.1109/JBHI.2014.2306988
A methodology for automatic classification of breast cancer immunohistochemical data using semi-supervised Fuzzy c-Means
Lai, D.T.C., Garibaldi, J.M., Soria, D. and Roadknight, C.M. 2014. A methodology for automatic classification of breast cancer immunohistochemical data using semi-supervised Fuzzy c-Means. Central European Journal of Operations Research. 22 (3), pp. 475-499. doi:10.1007/s10100-013-0318-3
Nottingham Prognostic Index Plus: Validation of a clinical decision making tool in breast cancer in an independent series
Green, A.R., Soria, D., Stephen, J., Powe, D.G., Nolan, C.C., Kunkler, I., Thomas, J., Kerr, G.R., Jack, W., Cameron, D., Piper, T., Ball, G.R., Garibaldi, J.M., Rakha, E.A., Bartlett, J.M.S. and Ellis, I.O. 2016. Nottingham Prognostic Index Plus: Validation of a clinical decision making tool in breast cancer in an independent series. The Journal of Pathology: Clinical Research. 1 (2), pp. 32-40. doi:10.1002/cjp2.32
Practical detection of a definitive biomarker panel for Alzheimer’s Disease; comparisons between plasma and cerebrospinal fluid
Richens, J.L., Vere, K.-A., Light, R.A., Soria, D., Garibaldi, J.M., Smith, A.D., Warden, D., Wilcock, G., Bajaj, N., Morgan, K. and O’Shea, P. 2014. Practical detection of a definitive biomarker panel for Alzheimer’s Disease; comparisons between plasma and cerebrospinal fluid. International Journal of Molecular Epidemiology and Genetics. 5 (2), pp. 53-70.
Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs
Reps, J.M., Aickelin, U., Garibaldi, J.M., Soria, D., Gibson, J.E. and Hubbard, R.B. 2014. Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs. Drug Safety. 37 (3), pp. 163-170. doi:10.1007/s40264-014-0137-z
A Novel Semisupervised Algorithm for Rare Prescription Side Effect Discovery
Reps, J.M., Garibaldi, J.M., Aickelin, U., Soria, D., Gibson, J.E. and Hubbard, R.B. 2014. A Novel Semisupervised Algorithm for Rare Prescription Side Effect Discovery. IEEE Journal of Biomedical and Health Informatics. 18 (2), pp. 537-547. doi:10.1109/JBHI.2013.2281505
Nottingham Prognostic Index Plus (NPI+): A Modern Clinical Decision Making Tool in Breast Cancer
Rakha, E., Soria, D., Green, A.R., Lemetre, C., Powe, D.G., Nolan, C.C., Garibaldi, J.M., Ball, G.R. and Ellis, I.O. 2014. Nottingham Prognostic Index Plus (NPI+): A Modern Clinical Decision Making Tool in Breast Cancer. British Journal of Cancer. 110 (7), pp. 1688-1697. doi:10.1038/bjc.2014.120
Nottingham prognostic index plus (NPI+) predicts risk of distant metastases in primary breast cancer
Green, A.R., Soria, D., Powe, G., Nolan, C.C., Aleskandarany, N.M., Szász, M.A., Tőkés, A.M., Ball, G.R., Garibaldi, J.M., Rakha, E.A., Kulka, J. and Ellis, I.O. 2016. Nottingham prognostic index plus (NPI+) predicts risk of distant metastases in primary breast cancer. Breast Cancer Research and Treatment. 157 (1), pp. 65-75. doi:10.1007/s10549-016-3804-1
Cancer subtype identification pipeline: A classifusion approach
Agrawal, U., Soria, D. and Wagner, C. 2016. Cancer subtype identification pipeline: A classifusion approach. Evolutionary Computation (CEC), 2016 IEEE Congress on. 24 - 29 Jul 2016 IEEE . doi:10.1109/CEC.2016.7744150
Markers of Progression in Early-Stage Invasive Breast Cancer: a Predictive Immunohistochemical Panel Algorithm for Distant Recurrence Risk Stratification
Aleskandarany, M.A., Soria, D., Green, A.R., Nolan, C., Diez-Rodriguez, M., Ellis, I.O. and Rakha, E.A. 2015. Markers of Progression in Early-Stage Invasive Breast Cancer: a Predictive Immunohistochemical Panel Algorithm for Distant Recurrence Risk Stratification. Breast Cancer Research and Treatment. 151 (2), pp. 325-333. doi:10.1007/s10549-015-3406-3
KI67 and DLX2 predict increased risk of metastasis formation in prostate cancer–a targeted molecular approach
Green, W.J.F., Ball, G., Hulman, G., Johnson, C., Van Schalwyk, G., Ratan, H.L., Soria, D., Garibaldi, J.M., Parkinson, R., Hulman, J., Rees, R. and Powe, D.G. 2016. KI67 and DLX2 predict increased risk of metastasis formation in prostate cancer–a targeted molecular approach. British Journal of Cancer. 115 (2), pp. 236-242. doi:10.1038/bjc.2016.169
Validation of a quantifier-based fuzzy classification system for breast cancer patients on external independent cohorts
Soria, D. and Garibaldi, J.M. 2016. Validation of a quantifier-based fuzzy classification system for breast cancer patients on external independent cohorts. IEEE International Conference on Machine Learning and Applications (ICMLA2016). Anaheim, California, USA 18 - 20 Dec 2016 IEEE . doi:10.1109/ICMLA.2016.0101
Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom
Figueredo, G.P., Agrawal, U., Mase, J.M.M., Mesgarpour, M., Wagner, C., Soria, D., Garibaldi, J.M., Siebers, P.O. and John, R.I. 2019. Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom. IEEE Transactions on Intelligent Transportation Systems. 20 (9), pp. 3324-3336. doi:10.1109/TITS.2018.2875343
Interpretability and Complexity of Design in the Creation of Fuzzy Logic Systems - A User Study
Razak, T.R., Garibaldi, J.M., Wagner, C., Pourabdollah, A. and Soria, D. 2018. Interpretability and Complexity of Design in the Creation of Fuzzy Logic Systems - A User Study. 2018 IEEE Symposium Series on Computational Intelligence. Bengaluru, India 18 - 21 Nov 2018 IEEE . doi:10.1109/SSCI.2018.8628924
Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures
Agrawal, U., Wagner, C., Garibaldi, J.M. and Soria, D. 2019. Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures. International Conference on Fuzzy Systems (FUZZ-IEEE 2019). 23 - 26 Jun 2019 IEEE . doi:10.1109/FUZZ-IEEE.2019.8858821
Comparison of Fuzzy Integral-Fuzzy Measure based Ensemble Algorithms with the State-of-the-art Ensemble Algorithms
Agrawal, U., Pinar, A.J., Wagner, C., Havens, T.C., Soria, D. and Garibaldi, J.M. 2018. Comparison of Fuzzy Integral-Fuzzy Measure based Ensemble Algorithms with the State-of-the-art Ensemble Algorithms. Medina, J., Ojeda-Aciego, M., Verdegay, J.L., Perfilieva, I., Bouchon-Meunier, B. and Yager, R.R. (ed.) 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems. Cadiz, Spain 11 - 15 Jun 2018 Springer. doi:10.1007/978-3-319-91479-4
Interpretability indices for hierarchical fuzzy systems
Razak, T.R., Garibaldi, J.M., Wagner, C., Pourabdollah, A. and Soria, D. 2017. Interpretability indices for hierarchical fuzzy systems. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017). Naples, Italy 09 - 12 Jul 2017 IEEE . doi:10.1109/FUZZ-IEEE.2017.8015616
A multicentre integration of a computer-led follow-up of prostate cancer is valid and safe
Salem, H., Caddeo, G., McFarlane, J., Patel, K., Cochrane, L., Soria, D., Henley, M. and Lund, J. 2018. A multicentre integration of a computer-led follow-up of prostate cancer is valid and safe. BJU international. 122 (3), pp. 418-426. doi:10.1111/bju.14157
MYC regulation of Glutamine-Proline regulatory axis is key in Luminal B breast cancer
Craze, M.L., Cheung, H., Jewa, N., Coimbra, N.D.M., Soria, D., El-Ansari, R., Aleskandarany, M.A., Cheng, K.W., Diez-Rodriguez, M., Nolan, C.C., Ellis, I.O., Rakha, E. and Green, A.R. 2018. MYC regulation of Glutamine-Proline regulatory axis is key in Luminal B breast cancer. British Journal of Cancer. 118 (2), pp. 258-265. doi:10.1038/bjc.2017.387
Machine Learning Classification of Females Susceptibility to Visceral Fat Associated Diseases
Aldraimli, M., Soria, D., Parkinson, J., Whitcher, B., Thomas, E.L., Bell, J.D., Chaussalet, T.J. and Dwek, M. 2019. Machine Learning Classification of Females Susceptibility to Visceral Fat Associated Diseases. MEDICON 2019: XV Mediterranean Conference on Medical and Biological Engineering and Computing. Coimbra, Portugal 26 - 28 Sep 2019 Springer. doi:10.1007/978-3-030-31635-8_81