Currently browsing: Items authored or edited by Daniel Berrar https://orcid.org/0000-0002-7038-2601

Berrar, Daniel (2025). Cross-validation. In: Ranganathan, Shoba; Cannataro, Mario and Khan, Mohammad Asif eds. Encyclopedia of Bioinformatics and Computational Biology, 2nd edition. Elsevier (In Press).

Berrar, Daniel (2025). Performance Measures for Binary Classification. In: Ranganathan, Shoba; Mario, Cannataro and Asif, Mohammad eds. Encyclopedia of Bioinformatics and Computational Biology, 2nd edition. Elsevier (In Press).

Berrar, Daniel (2025). Bayes' Theorem and Naive Bayes Classifier. In: Ranganathan, Shoba; Cannataro, Mario and Asif Khan, Mohammed eds. Encyclopedia of Bioinformatics and Computational Biology, 2nd edition. Elsevier (In Press).

Berrar, Daniel (2025). Introduction to the Non-Parametric Bootstrap. In: Ranganathan, Shoba; Mario, Cannataro and Asif, Mohammad eds. Encyclopedia of Bioinformatics and Computational Biology, 2nd edition. Elsevier (In Press).

Berrar, Daniel; Lopes, Philippe and Dubitzky, Werner (2024). A data- and knowledge-driven framework for developing machine learning models to predict soccer match outcomes. Machine Learning, 113 pp. 8165–8204.

• Download Published Version (PDF / 1MB)

Berrar, Daniel (2022). Using p-values for the comparison of classifiers: pitfalls and alternatives. Data Mining and Knowledge Discovery, 36(3) pp. 1102–1139.

Berrar, Daniel and Dubitzky, Werner (2021). Deep learning in bioinformatics and biomedicine. Briefings in Bioinformatics, 22(2) pp. 1513–1514.

Berrar, Daniel and Dubitzky, Werner (2019). Should significance testing be abandoned in machine learning? International Journal of Data Science and Analytics, 7(4) pp. 247–257.

Berrar, Daniel; Lopes, Philippe and Dubitzky, Werner (2019). Incorporating domain knowledge in machine learning for soccer outcome prediction. Machine Learning, 108 pp. 97–126.

Berrar, Daniel (2019). Bayes’ Theorem and Naive Bayes Classifier. In: Ranganathan, Shoba; Gribskov, Michael; Nakai, Kenta; Schönbach, Christian and Cannataro, Mario eds. Encyclopedia of Bioinformatics and Computational Biology. Reference Module in Life Sciences, 1. Elsevier, pp. 403–412.

Berrar, Daniel (2019). Cross-validation. In: Ranganathan, Shoba; Gribskov, Michael; Nakai, Kenta; Schönbach, Christian and Cannataro, Mario eds. Encyclopedia of Bioinformatics and Computational Biology. Reference Module in Life Sciences, 1. Elsevier, pp. 542–545.

Berrar, Daniel (2019). Performance Measures for Binary Classification. In: Ranganathan, Shoba; Gribskov, Michael; Nakai, Kenta; Schönbach, Christian and Cannataro, Mario eds. Encyclopedia of Bioinformatics and Computational Biology. Reference Module in Life Sciences, 1. Elsevier, pp. 546–560.

Berrar, Daniel (2019). Introduction to the Non-Parametric Bootstrap. In: Ranganathan, Shoba; Gribskov, Michael; Nakai, Kenta; Schönbach, Christian and Cannataro, Mario eds. Encyclopedia of Bioinformatics and Computational Biology. Reference Module in Life Sciences, 1. Elsevier, pp. 766–773.

Berrar, Daniel and Dubitzky, Werner (2018). On the Jeffreys-Lindley paradox and the looming reproducibility crisis in machine learning. In: 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp. 334–340.

Berrar, Daniel (2016). On the noise resilience of ranking measures. In: Neural Information Processing ICONIP 2016 (Hirose, A.; Ozawa, S.; Doya, K.; Ikeda, K.; Lee, M. and Liu, D. eds.), Lecture Notes in Computer Science (LNTCS), Springer-Verlag, pp. 47–55.

Berrar, Daniel and Flach, Peter (2012). Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them). Briefings in Bioinformatics, 13(1) pp. 83–97.

• Download Published Version (PDF / 455kB)

Berrar, Daniel; Konagaya, Akihiko and Schuster, Alfons (2012). Can machines make us think? In memory of Alan Turing (1912-1954). In: Proceedings of the Annual Conference of JSAI, The Japanese Society for Artificial Intelligence, Tokyo, Japan, 26, article no. 3P2-IOS-2b-2.

• Download Published Version (PDF / 918kB)

Berrar, Daniel and Ohmayer, Georg (2011). Multidimensional scaling with discrimination coefficients for supervised visualization of high-dimensional data. Neural Computing and Applications, 20(8) pp. 1211–1218.

Berrar, Daniel; Sato, Naoyuki and Schuster, Alfons (2010). Artificial Intelligence in Neuroscience and Systems Biology: Lessons Learnt, Open Problems, and the Road Ahead. Advances in Artificial Intelligence, 2010, article no. 578309.

• Download Published Version (PDF / 1MB)

Berrar, Daniel; Sato, Naoyuki and Schuster, Alfons (2010). Quo Vadis, Artificial Intelligence? Advances in Artificial Intelligence, 2010, article no. 629869.

• Download Published Version (PDF / 1MB)

Berrar, Daniel; Sturgeon, Brian; Bradbury, Ian; Downes, C. Stephen and Dubitzky, Werner (2005). Survival Trees for Analyzing Clinical Outcome in Lung Adenocarcinomas Based on Gene Expression Profiles: Identification of Neogenin and Diacylglycerol Kinase α Expression as Critical Factors. Journal of Computational Biology, 12(5) pp. 534–544.

Berrar, Daniel; Stahl, Frederic; Silva, Candida; Rodrigues, J. Rui; Brito, Rui M. M. and Dubitzky, Werner (2005). Towards Data Warehousing and Mining of Protein Unfolding Simulation Data. Journal of clinical monitoring and computing, 19(4-5) pp. 307–317.

Cross, Jeffrey; Berrar, Daniel; Watson, Ian and Smith, Roderick (2023). The UK-Japan Engineering Education League (UKJEEL) Workshop: Rationale, Goals, and Lessons Learned. In: JSEE Annual Conference International Session Proceedings, 07 Sep 2023, Hiroshima, Japan, pp. 14–17.

Deligianni, Elena; Pattison, Sally; Berrar, Daniel; Ternan, Nigel G.; Haylock, Richard W.; Moore, John E.; Elborn, Stuart J. and Dooley, James S. G. (2010). Pseudomonas aeruginosa cystic fibrosis isolates of similar RAPD genotype exhibit diversity in biofilm forming ability in vitro. BMC Microbiology, 10(1), article no. 38 (2010).

• Download Published Version (PDF / 4MB)

Graham, Joanna E.; McGilligan, Victoria E.; Berrar, Daniel; Leccisotti, Antonio; Moore, Jonathan E.; Bron, Anthony J. and Moore, Tara C. B. (2009). Attitudes towards Diagnostic Tests and Therapies for Dry Eye Disease. Ophthalmic Research, 43(1) pp. 11–17.

McCann, Mark J.; Gill, Chris I. R.; Linton, Trevor; Berrar, Daniel; McGlynn, Hugh and Rowland, Ian R. (2008). Enterolactone restricts the proliferation of the LNCaP human prostate cancer cell line in vitro. Molecular Nutrition & Food Research, 52(5) pp. 567–580.

Natarajan, J.; Berrar, D.; Hack, C. J. and Dubitzky, W. (2008). Knowledge discovery in biology and biotechnology texts: a review of techniques, evaluation strategies, and applications. Critical reviews in biotechnology, 25(1-2) pp. 31–52.

Natarajan, Jeyakumar; Berrar, Daniel; Dubitzky, Werner; Hack, Catherine; Zhang, Yonghong; DeSesa, Catherine; Van Brocklyn, James R. and Bremer, Eric G. (2006). Text mining of full-text journal articles combined with gene expression analysis reveals a relationship between sphingosine-1-phosphate and invasiveness of a glioblastoma cell line. BMC Bioinformatics, 7, article no. 373 (2006).

• Download Published Version (PDF / 601kB)

O'Donoghue, P. G.; Dubitzky, W.; Lopes, P.; Berrar, D.; Lagan, K.; Hassan, D.; Bairner, A. and Darby, P. (2004). An evaluation of quantitative and qualitative methods of predicting the 2002 FIFA World Cup. Journal of Sports Sciences, 22(6) pp. 513–514.

Schuster, Alfons and Berrar, Daniel (2011). Special Issue on Omnipresent Intelligent Computing – New Developments and Societal Impact. Journal of Advanced Computational Intelligence and Intelligent Informatics, 15(7) p. 785.

• Download Published Version (PDF / 41kB)

Wiwatcharakoses, Chayut and Berrar, Daniel (2021). A Self-Organizing Incremental Neural Network for Continual Supervised Learning. Expert Systems with Applications, 185, article no. 115662.

• Download Accepted Manuscript (PDF / 3MB)

Wiwatcharakoses, Chayut and Berrar, Daniel (2020). SOINN+, a Self-Organizing Incremental Neural Network for Unsupervised Learning from Noisy Data Streams. Expert Systems with Applications, 143, article no. 113069.

Wiwatcharakoses, Chayut and Berrar, Daniel (2019). Self-organizing incremental neural networks for continual learning. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), pp. 6476–6477.

• Download Published Version (PDF / 266kB)