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Dendritic Morphology Predicts Pattern Recognition Performance in Multi-compartmental Model Neurons with and without Active Conductances

De Sousa, Giseli; Maex, Reinoud; Adams, R.G.; Davey, N.; Steuber, Volker (2015-04-01)

In this paper we examine how a neuron’s dendritic morphology can affect its pattern recognition performance. We use two different algorithms to systematically explore the space of dendritic morphologies: an algorithm that ...

Integrating genomic binding site predictions using real-valued meta classifiers

Sun, Yi.; Robinson, M.; Adams, R.G.; Kaye, Paul H.; Te Boekhorst, R.; Rust, A.G.; Davey, N. (2009)

Currently the best algorithms for predicting transcription factor binding sites in DNA sequences are severely limited in accuracy. There is good reason to believe that predictions from different classes of algorithms could ...

Word Segmentation of Handwritten Text Using Supervised Classification Techniques

Sun, Yi.; Butler, T.; Shafarenko, A.; Adams, R.G.; Loomes, M.J.; Davey, N. (2007)

Using Feature Selection Filtering Methods for Binding Site Predictions

Sun, Yi.; Robinson, M.; Adams, R.G.; Te Boekhorst, R.; Rust, A.G.; Davey, N. (IEEE, 2006)

Using sampling methods to improve binding site predictions

Sun, Yi.; Robinson, M.; Adams, R.G.; Te Boekhorst, R.; Rust, A.G.; Davey, N. (2006)

Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. In previous work we combine random selection under-sampling into SMOTE over-sampling technique, working with ...

Using real-valued metaclassifiers to integrate binding site predictions

Sun, Yi; Robinson, M.; Adams, Roderick; Kaye, Paul H.; Rust, A.G.; Davey, N. (IEEE, 2005)

Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. There is good reason to believe that predictions from these different classes of algorithms could be used in ...