Personalised Medicine through Learning in the Model Space

Research output

Research output per year 2014 2014 2017 2018

• 24 Article
• 5 Conference contribution
• 1 Special issue

Research output per year

Research output per year

Search results

• 2018

  Asymptotic Fisher Memory of Randomized Linear Symmetric Echo State Networks

  Tino, P., 12 Jul 2018, In: Neurocomputing. 298, p. 4-8 16 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Echo State Network 100%
  • Collinear 13%
  • Eigenvector 10%
  • State Space 9%
  • Stochastic Matrix 7%
  6 Citations (Scopus)

  230 Downloads (Pure)

• Learning pharmacokinetic models for in vivo glucocorticoid activation

  Bunte, K., Smith, D., Chappell, M. J., Hassan-Smith, Z., Tomlinson, J., Arlt, W. & Tino, P., 14 Oct 2018, In: Journal of Theoretical Biology. 455, p. 222-231

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Glucocorticoids 100%
  • Prednisone 84%
  • Pharmacokinetics 82%
  • Learning 80%
  • Machine Learning 61%
  3 Citations (Scopus)

  223 Downloads (Pure)
• 2017

  A New Framework for Analysis of Coevolutionary Systems - Directed Graph Representation and Random Walks

  Chong, S. Y., Tino, P., He, J. & Yao, X., 20 Nov 2017, (E-pub ahead of print) In: Evolutionary Computation. 33 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Graph Representation 100%
  • Directed Graph 69%
  • Random walk 64%
  • Framework 42%
  • Coevolution 37%
  2 Citations (Scopus)

  144 Downloads (Pure)

• Classification framework for partially observed dynamical systems

  Shen, Y., Tino, P. & Tsaneva-Atanasova, K., 14 Apr 2017, In: Physical Review E. 95, 4, 043303.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • dynamical systems 100%
  • Dynamical system 74%
  • Framework 57%
  • Posterior distribution 33%
  • Model 32%
  2 Citations (Scopus)

• Classification of sparsely and irregularly sampled time series: A learning in model space approach

  Shen, Y., Tino, P. & Tsaneva-Atanasova, K., 3 Jul 2017, 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Institute of Electrical and Electronics Engineers (IEEE), Vol. 2017-May. p. 3696-3703 8 p. 7966321

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Time series 100%
  • Uncertainty 18%
  • Dynamical systems 11%
  • Machine learning 10%
  • Classifiers 10%

• Functional brain networks for learning predictive statistics

  Giorgio, J., Karlaftis, V. M., Wang, R., Shen, Y., Tino, P., Welchman, A. & Kourtzi, Z., 18 Aug 2017, (E-pub ahead of print) In: Cortex.

  Research output: Contribution to journal › Special issue › peer-review

  Open Access

  • brain 100%
  • Statistics 91%
  • statistics 87%
  • Learning 86%
  • Brain 58%
  5 Citations (Scopus)

• Indefinite Core Vector Machine

  Schleif, F-M. & Tino, P., 1 Nov 2017, In: Pattern Recognition. 71, p. 187-195

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Support vector machines 100%
  • Classifiers 43%
  • Costs 23%
  • Experiments 22%
  13 Citations (Scopus)

  260 Downloads (Pure)

• Learning predictive statistics: Strategies and brain mechanisms

  Wang, R., Shen, Y., Tino, P., Welchman, A. E. & Kourtzi, Z., 30 Aug 2017, In: Journal of Neuroscience. 37, 35, p. 8412-8427 16 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Statistics 100%
  • Learning 94%
  • Basal Ganglia 65%
  • Brain 63%
  • Aptitude 44%
  12 Citations (Scopus)

• Learning predictive statistics from temporal sequences: Dynamics and strategies

  Wang, R., Shen, Y., Tino, P., Welchman, A. E. & Kourtzi, Z., 1 Oct 2017, In: Journal of Vision. 17, 12, p. 1-16 16 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Statistics 100%
  • Learning 94%
  • Markov Chains 44%
  • Motivation 26%
  • Aptitude 22%
  4 Citations (Scopus)

• Model-free causality detection: An application to social media and financial data

  Tsapeli, T., Musolesi, M. & Tino, P., 4 May 2017, (E-pub ahead of print) In: Physica A: Statistical Mechanics and its Applications.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Stock Market Returns 100%
  • Stock Market 94%
  • Financial Data 91%
  • Causality 87%
  • Social Media 75%
  4 Citations (Scopus)

  205 Downloads (Pure)

• Ordinal regression based on learning vector quantization

  Tang, F. & Tiňo, P., 1 Sept 2017, In: Neural Networks. 93, p. 76-88

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Vector quantization 100%
  • Learning 65%
  • Cost functions 41%
  • Datasets 35%
  • Weights and Measures 28%
  7 Citations (Scopus)

  256 Downloads (Pure)

• Personalized Medication Response Prediction for Attention-Deficit Hyperactivity Disorder: Learning in the Model Space vs. Learning in the Data Space

  Wong, H. K., Tiffin, P. A., Chappell, M. J., Nichols, T. E., Welsh, P. R., Doyle, O. M., Lopez-Kolkovska, B. C., Inglis, S. K., Coghill, D., Shen, Y. & Tiño, P., 11 Apr 2017, In: Frontiers in Physiology. 8, 199.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Space Simulation 100%
  • Attention Deficit Disorder with Hyperactivity 66%
  • Learning 47%
  • Child 21%
  • Methylphenidate 20%
  7 Citations (Scopus)

  154 Downloads (Pure)

• Unravelling socio-motor biomarkers in schizophrenia

  Słowiński, P., Alderisio, F., Zhai, C., Shen, Y., Tino, P., Bortolon, C., Capdevielle, D., Cohen, L., Khoramshahi, M., Billard, A., Salesse, R., Gueugnon, M., Marin, L., Bardy, B. G., di Bernardo, M., Raffard, S. & Tsaneva-Atanasova, K., 1 Feb 2017, In: NPJ schizophrenia. 3, 8.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Schizophrenia 100%
  • Learning 90%
  • Biomarkers 79%
  • Uncertainty 56%
  • Hand 46%
  17 Citations (Scopus)

  148 Downloads (Pure)
• 2016

  Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment

  Alahmadi, H., Shen, Y., Fouad, S., Di Bernardi Luft, C., Bentham, P., Kourtzi, Z. & Tino, P., 17 Nov 2016, In: Frontiers in Computational Neuroscience. 10, 117.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Machine Learning 100%
  • Cognitive Dysfunction 72%
  • Magnetic Resonance Imaging 58%
  • Learning 40%
  • Psychological Inhibition 18%
  9 Citations (Scopus)

  144 Downloads (Pure)

• Finding small sets of random fourier features for shift-invariant kernel approximation

  Schleif, F. M., Kaban, A. & Tino, P., 9 Sept 2016, (E-pub ahead of print) Artificial Neural Networks in Pattern Recognition - 7th IAPR TC3 Workshop, ANNPR 2016, Proceedings. Springer Verlag, Vol. 9896 LNAI. p. 42-54 13 p. (Lecture Notes in Computer Science; vol. 9896 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • kernel 100%
  • Invariant 81%
  • Approximation 74%
  • Costs 55%
  • Machine learning 53%
  1 Citation (Scopus)

• Kernel regression estimates of time delays between gravitationally lensed fluxes

  Al Otaibi, S., Tiňo, P., Cuevas-tello, J. C., Mandel, I. & Raychaudhury, S., 11 Jun 2016, In: Royal Astronomical Society. Monthly Notices. 459, 1, p. 573-584

  Research output: Contribution to journal › Article › peer-review

  • regression analysis 100%
  • quasars 98%
  • time lag 82%
  • estimates 57%
  • probe 39%
  2 Citations (Scopus)

• Model-coupled autoencoder for time series visualisation

  Gianniotis, N., Kügler, S. D., Tino, P. & Polsterer, K. L., 5 Jun 2016, In: Neurocomputing. 192, p. 139-146

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Time series 100%
  • Visualization 89%
  • Binary sequences 51%
  • Dimensionality reduction 42%
  • Neurons 35%
  8 Citations (Scopus)

  200 Downloads (Pure)

• Oversampling the minority class in the feature space

  Perez-Ortiz, M., Gutiérrez, P. A., Tino, P. & Hervás-Martínez, C., Sept 2016, In: IEEE Transactions on Neural Networks and Learning Systems. 27, 9, p. 1947-1961 15 p.

  Research output: Contribution to journal › Article › peer-review

  • Support vector machines 100%
  • Machine learning 91%
  • Experiments 44%
  25 Citations (Scopus)
• 2015

  Incremental probabilistic classification vector machine with linear costs

  Schleif, F. M., Chen, H. & Tino, P., 28 Sept 2015, Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers (IEEE), 8 p. 7280377

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Costs 100%
  • Classifiers 92%
  • Data storage equipment 79%
  8 Citations (Scopus)

• Indefinite proximity learning: A review

  Schleif, F. M. & Tino, P., 21 Oct 2015, In: Neural Computation. 27, 10, p. 2039-2096 58 p.

  Research output: Contribution to journal › Article › peer-review

  • Proximity 100%
  • Learning 77%
  • Formalism 41%
  • Psychological Generalization 34%
  • Kernel 25%
  48 Citations (Scopus)

• Large scale indefinite kernel fisher discriminant

  Schleif, F. M., Gisbrecht, A. & Tino, P., 2015, Similarity-Based Pattern Recognition : Third International Workshop, SIMBAD 2015, Copenhagen, Denmark, October 12-14, 2015. Proceedings. Feragen, A., Pelillo, M. & Loog, M. (eds.). Springer, Vol. 9370. p. 160-170 11 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9370).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Bioinformatics 100%
  • Vector spaces 94%
  • Discriminant 92%
  • Classifiers 64%
  • kernel 62%
  4 Citations (Scopus)

• The benefits of modeling slack variables in SVMs

  Tang, F., Tiňo, P., Gutiérrez, P. A. & Chen, H., 19 Apr 2015, In: Neural Computation. 27, 4, p. 954-981 28 p.

  Research output: Contribution to journal › Article › peer-review

  • Support Vector Machine 100%
  • Modeling 69%
  • Natural Order 25%
  • Performance 15%
  3 Citations (Scopus)
• 2014

  Cognitive fault diagnosis in Tennessee Eastman Process using learning in the model space

  Chen, H., Tino, P. & Yao, X., 4 Aug 2014, In: Computers & Chemical Engineering. 67, p. 33-42

  Research output: Contribution to journal › Article › peer-review
  32 Citations (Scopus)

• Combining learning in model space fault diagnosis with data validation/reconstruction: Application to the Barcelona water network

  Quevedo, J., Chen, H., Cugueró, M. À., Tino, P., Puig, V., García, D., Sarrate, R. & Yao, X., 1 Apr 2014, In: Engineering Applications of Artificial Intelligence. 30, p. 18-29

  Research output: Contribution to journal › Article › peer-review
  19 Citations (Scopus)

• Dimensionality reduction and topographic mapping of binary tensors

  Mažgut, J., Tino, P., Bodén, M. & Yan, H., Aug 2014, In: Pattern Analysis and Applications. 17, 3, p. 497-515 19 p.

  Research output: Contribution to journal › Article › peer-review

  • Dimensionality reduction 100%
  • Tensors 91%
  • Principal component analysis 38%
  • Decomposition 22%
  • DNA 10%
  6 Citations (Scopus)

• Efficient Probabilistic Classification Vector Machine With Incremental Basis Function Selection

  Chen, H., Tino, P. & Yao, X., 1 Feb 2014, In: IEEE Transactions on Neural Networks and Learning Systems. 25, 2, p. 356-369

  Research output: Contribution to journal › Article › peer-review
  31 Citations (Scopus)

• Learning in the model space for cognitive fault diagnosis

  Chen, H., Tino, P., Rodan, A. & Yao, X., 1 Jan 2014, In: IEEE Transactions on Neural Networks and Learning Systems. 25, 1, p. 124-136 13 p., 6515601.

  Research output: Contribution to journal › Article › peer-review

  • Space Simulation 100%
  • Failure analysis 55%
  • Learning 47%
  • Distance Education 18%
  • Benchmarking 14%
  68 Citations (Scopus)

• Learning the deterministically constructed Echo State Networks

  Tang, F., Tino, P. & Chen, H., 3 Sept 2014, Proceedings of the 2014 International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers (IEEE), p. 77-83 7 p. 6889714

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Computational efficiency 100%
  • Time series 89%
  2 Citations (Scopus)

• Ordinal regression neural networks based on concentric hyperspheres

  Gutiérrez, P. A., Tino, P. & Hervás-martínez, C., Nov 2014, In: Neural Networks. 59, p. 51-60

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Datasets 100%
  • Computer Neural Networks 82%
  • Neural networks 76%
  • Set theory 16%
  16 Citations (Scopus)

  349 Downloads (Pure)

• Spatial–temporal modelling of fMRI data through spatially regularized mixture of hidden process models

  Shen, Y., Mayhew, S. D., Kourtzi, Z. & Tino, P., 1 Jan 2014, In: NeuroImage. 84, p. 657-671

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Magnetic Resonance Imaging 100%
  • Hemodynamics 27%
  • Psychology Recognition 16%
  • Theoretical Models 15%
  • Research Design 14%
  7 Citations (Scopus)

  149 Downloads (Pure)