Natural gradient works efficiently in learning
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Principle of Learning Metrics for Exploratory Data Analysis
Journal of VLSI Signal Processing Systems
The evidence framework applied to classification networks
Neural Computation
Artificial neural network for the joint modelling of discrete cause-specific hazards
Artificial Intelligence in Medicine
IEEE Transactions on Neural Networks
Proceedings of the 2009 conference on Computational Intelligence and Bioengineering: Essays in Memory of Antonina Starita
Artificial Intelligence in Medicine
Bankruptcy analysis with self-organizing maps in learning metrics
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Different methodologies for patient stratification using survival data
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
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Early characterization of patients with respect to their predicted response to treatment is a fundamental step towards the delivery of effective, personalized care. Starting from the results of a time-to-event model with competing risks using the framework of partial logistic artificial neural networks with automatic relevance determination (PLANNCR-ARD), we discuss an effective semi-supervised approach to patient stratification with application to Acute Myeloid Leukaemia (AML) data (n = 509) acquired prospectively by the GIMEMA consortium. Multiple prognostic indices provided by the survival model are exploited to build a metric based on the Fisher information matrix. Cluster number estimation is then performed in the Fisher-induced affine space, yielding to the discovery of a stratification of the patients into groups characterized by significantly different mortality risks following induction therapy in AML. The proposed model is shown to be able to cluster the input data, while promoting specificity of both target outcomes, namely Complete Remission (CR) and Induction Death (ID). This generic clustering methodology generates an affine transformation of the data space that is coherent with the prognostic information predicted by the PLANNCR-ARD model.