Showing results 1 to 20 of 107
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| Issue Date | Title | Author(s) |
| 2016 | The AGIS metric and time of test: A replication study | Counsell, S; Swift, S; Tucker, A |
| 19-Nov-2019 | Analysis of Friedreich's ataxia patient clinical data reveals importance of accurate GAA repeat determination in disease prognosis and gender differences in cardiac measures | Ghorbani, M; Pousset, F; Tucker, A; Swift, S; Giunti, P; Parkinson, M; Gilbert, D; Liu, XH; Payne, A |
| 14-May-2019 | An application of generalised simulated annealing towards the simultaneous modelling and clustering of glaucoma | Jilani, MZMB; Tucker, A; Swift, SM |
| 2019 | Automating the construction of higher order data representations from heterogeneous biodiversity datasets | Nicolson, Nicky |
| 1-Sep-2021 | Bayesboost: Identifying and Handling Bias Using Synthetic Data Generators | Draghi, B; Wang, Z; Myles, P; Tucker, A |
| 2016 | A Bayesian Belief Network to assess rate of changes in coral reef ecosystems | Franco, C; Hepburn, LA; Smith, DJ; Nimrod, S; Tucker, A |
| 2004 | A Bayesian network approach to explaining time series with changing structure | Tucker, A; Liu, X |
| 2005 | Bayesian network classifiers for time-series microarray data | Tucker, A; Vinciotti, V; Hoen, PAC't; Liu, X |
| 2012 | Bioinformatics tools in predictive ecology: Applications to fisheries | Tucker, A; Duplisea, D |
| 2013 | Building trajectories through clinical data to model disease progression | Li, Yuanxi |
| 2004 | Clustering with niching genetic K-means algorithm | Sheng, W; Tucker, A; Liu, X |
| 2022 | Combined supervised and unsupervised learning to identify subclasses of disease for better prediction | Alsaid Alyousef, Awad |
| 2010 | Combining heterogeneous sources of data for the reverse-engineering of gene regulatory networks | Steele, Emma |
| 2016 | Combining Unsupervised and Supervised Learning for Discovering Disease Subclasses | Tucker, A; Bosoni, P; Bellazzi, R; Nihtyanova, S; Denton, C |
| 2008 | Consensus and meta-analysis regulatory networks for combining multiple microarray gene expression datasets | Steele, E; Tucker, A |
| 2004 | Consensus clustering and functional interpretation of gene expression data | Swift, S; Tucker, A; Vinciotti, V; Martin, N; Orengo, C; Liu, X; Kellam, P |
| 2018 | Construction of a network describing asparagine metabolism in plants and its application to the identification of genes affecting asparagine metabolism in wheat under drought and nutritional stress | Curtis, TY; Bo, V; Tucker, A; Halford, NG |
| 21-Aug-2021 | DeepHistoClass: A novel strategy for confident classification of immunohistochemistry images using Deep Learning | Ghoshal, B; Hikmet, F; Pineau, C; Tucker, A; Lindskog, C |
| 2019 | Developing an intrusion detection model for distributed denial of service attacks in cloud computing | Ahmadi, Roja |
| 2014 | Discovering study-specific gene regulatory networks | Bo, V; Curtis, T; Lysenko, A; Saqi, M; Swift, S; Tucker, A |
