Showing results 60 to 79 of 104
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Issue Date | Title | Author(s) |
2009 | Literature-based priors for gene regulatory networks | Steele, E; Tucker, A; 't Hoen, PAC; Schuemie, MJ |
2016 | Machine-learning approaches for modelling fish population dynamics | Trifonova, Neda |
2018 | Modelling air pollution, climate and health data using Bayesian Networks: a case study of the English regions | Vitolo, C; Scutari, M; Ghalaieny, M; Tucker, A; Russell, A |
24-Jan-2019 | MuG: A Multilevel Graph Representation for Big Data Interpretation | Colace, F; Lombardi, M; Pascale, F; Santaniello, D; Tucker, A; Villani, P |
- | Nearest Consensus Clustering Classification to Identify Subclasses and Predict Disease | Alyousef, AA; Nihtyanova, S; Denton, C; Bosoni, P; Bellazzi, R; Tucker, A |
2012 | The North Atlantic Oscillation, climate change and the ecology of British insects | Westgarth-Smith, Angus |
2005 | Object-oriented cohesion as a surrogate of software comprehension: An empirical study | Counsell, S; Swift, S; Tucker, A; Mendes, E |
24-Jan-2019 | Opening the Black Box: Discovering and Explaining Hidden Variables in Type 2 Diabetic Patient Modelling | Yousefi, L; Swift, S; Arzoky, M; Saachi, L; Chiovato, L; Tucker, A |
2020 | Opening the black box: personalised disease prediction using hidden variables and dynamic bayesian networks | Yousefi, Leila |
29-Mar-2020 | Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules | Yousefi, L; Swift, S; Arzoky, M; Saachi, L; Chiovato, L; Tucker, A |
2-Feb-2021 | Practical Lessons from Generating Synthetic Healthcare Data with Bayesian Networks | de Benedetti, J; Oues, N; Wang, Z; Myles, P; Tucker, A |
13-Nov-2017 | Predicting Comorbidities Using Resampling and Dynamic Bayesian Networks with Latent Variables | Yousefi, L; Saachi, L; Bellazzi, R; Chiovato, L; Tucker, A |
2017 | Predicting ecosystem responses to changes in fisheries catch, temperature, and primary productivity with a dynamic Bayesian network model | Tucker, A; Trifonova, N; Maxwell, D; Pinnegar, J; Kenny, A |
2019 | The Prevalence of Errors in Machine Learning Experiments | Shepperd, M; Guo, Y; Li, N; Arzoky, M; Capiluppi, A; Counsell, S; Destefanis, G; Swift, S; Tucker, A; Yousefi, L |
2009 | The pseudotemporal bootstrap for predicting glaucoma from cross-sectional visual field data | Tucker, A; Garway-Heath, D |
2011 | Python for teaching introductory programming: A quantitative evaluation | Jayal, A; Lauria, S; Tucker, A; Swift, S |
1-Feb-2011 | Python for teaching introductory programming: A quantitative evaluation | Jayal, A; Lauria, S; Tucker, A; Swift, S |
2015 | Quantifying stocktwits semantic terms' trading behavior in financial markets: An effective application of decision tree algorithms | Al Nasseri, A; Tucker, A; de Cesare, S |
2016 | rdefra: Interact with the UK AIR Pollution Database from DEFRA | Vitolo, C; Russell, A; Tucker, A |
2-Feb-2021 | Reasoning About Neural Network Activations: An Application in Spatial Animal Behaviour from Camera Trap Classifications | Evans, BC; Tucker, A; Wearn, OR; Carbone, C |