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X-CNN: Cross-modal convolutional neural networks for sparse datasets.

P. Velickovic, D. Wang, N. Lane, and P. Liò. SSCI, page 1-8. IEEE, (2016)

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Methodologies for Systems Medicine: Time to Join the Forces of Bioengineering and Bioinformatics.P. Liò. BIOINFORMATICS, page IS-27. SciTePress, (2013)Computing with Metabolic Machines.C. Angione, G. Carapezza, J. Costanza, P. Liò, and G. Nicosia. Turing-100, volume 10 of EPiC Series in Computing, page 1-15. EasyChair, (2012)The Bio-Inspired and Social Evolution of Node and Data in a Multilayer Network.M. Scatà, A. Stefano, E. Giacchi, A. Corte, and P. Liò. DCNET, page 41-46. SciTePress, (2014)Identifying Regulatory Sites Using Neighborhood Species.C. Angelini, L. Cutillo, I. Feis, R. der Wath, and P. Liò. EvoBIO, volume 4447 of Lecture Notes in Computer Science, page 1-10. Springer, (2007)An integrated modelling approach for R5-X4 mutation and HAART therapy assessment.A. Sorathiya, A. Bracciali, and P. Liò. Swarm Intelligence, 4 (4): 319-340 (2010)Muxstep: an open-source C ++ multiplex HMM library for making inferences on multiple data types.P. Velickovic, and P. Liò. Bioinformatics, 32 (16): 2562-2564 (2016)Directional communication with movement prediction in mobile wireless sensor networks.X. Lu, Z. Qu, P. Liò, P. Hui, Q. Li, P. Lu, and R. Bie. Personal and Ubiquitous Computing, 18 (8): 1941-1953 (2014)Making life difficult for Clostridium difficile: augmenting the pathogen's metabolic model with transcriptomic and codon usage data for better therapeutic target characterization.S. Kashaf, C. Angione, and P. Liò. BMC Systems Biology, 11 (1): 25:1-25:13 (2017)Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation.T. Brouwer, J. Frellsen, and P. Liò. ECML/PKDD (1), volume 10534 of Lecture Notes in Computer Science, page 513-529. Springer, (2017)NuChart-II: A Graph-Based Approach for Analysis and Interpretation of Hi-C Data.F. Tordini, M. Drocco, I. Merelli, L. Milanesi, P. Liò, and M. Aldinucci. CIBB, volume 8623 of Lecture Notes in Computer Science, page 298-311. Springer, (2014)

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X-CNN: Cross-modal convolutional neural networks for sparse datasets.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML X-CNN: Cross-modal convolutional neural networks for sparse datasets.

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X-CNN: Cross-modal convolutional neural networks for sparse datasets.