Deep Self-Organising Maps for efficient heterogeneous biomedical signatures extraction Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN) Sokolovska, N. Nguyen Thanh Hai Clément, K. /Zucker, Jean-Daniel Feature selection is used to preserve significant properties of data in a compact space. In particular, feature selection is needed in applications, where information comes from multiple heterogeneous high dimensional sources. Data integration, however, is a challenge in itself. In our contribution, we introduce a feature selection framework based on powerful visualisation capabilities of self-organising maps, where the deep structure can be learned in a supervised or unsupervised manner. For a supervised version of the deep SOM, we propose to carry out inference with a linear SVM. A forward-backward procedure helps to converge to an optimal feature set. We show by experiments on real large-scale biomedical data set that the proposed methods embed data in a new compact meaningful representation, allow to visualise biomedical signatures, and also lead to a reasonable classification accuracy compared to the state-of-the-art methods. IEEE 2016 text https://www.documentation.ird.fr/hor/fdi:010069089 fdi:010069089 Sokolovska N., Nguyen Thanh Hai, Clément K., Zucker Jean-Daniel. Deep Self-Organising Maps for efficient heterogeneous biomedical signatures extraction. In : . Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN) IEEE, 2016, 5079-5086 International Joint Conference on Neural Networks (IJCNN), Vancouver (CAN), 2016/07/24-29 EN