Bacciu, Davide; Lisboa, Paulo J. G.; Vellido, Alfredo Deep Learning in Biology and Medicine Book World Scientific Publisher, 2022, ISBN: 978-1-80061-093-4. Bacciu, Davide; Micheli, Alessio Deep Learning for Graphs Book Chapter In: Oneto, Luca; Navarin, Nicolo; Sperduti, Alessandro; Anguita, Davide (Ed.): Recent Trends in Learning From Data: Tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019), vol. 896, pp. 99-127, Springer International Publishing, 2020, ISBN: 978-3-030-43883-8. Ovidiu, Vermesan; Arne, Broring; Elias, Tragos; Martin, Serrano; Davide, Bacciu; Stefano, Chessa; Claudio, Gallicchio; Alessio, Micheli; Mauro, Dragone; Alessandro, Saffiotti; Pieter, Simoens; Filippo, Cavallo; Roy, Bahr In: Vermesan, Ovidiu; Bacquet, Joel (Ed.): Cognitive Hyperconnected Digital Transformation: Internet of Things Intelligence Evolution, Chapter 4, pp. 97-155, River Publishers, 2017, ISBN: 9788793609105. Bacciu, Davide; Lisboa, Paulo J. G.; Sperduti, Alessandro; Villmann, Thomas Probabilistic Modeling in Machine Learning Book Chapter In: Kacprzyk, Janusz; Pedrycz, Witold (Ed.): pp. 545–575, Springer Berlin Heidelberg, Berlin, Heidelberg, 2015, ISBN: 978-3-662-43505-2.2022
@book{BacciuBook2022,
title = {Deep Learning in Biology and Medicine},
author = {Davide Bacciu and Paulo J. G. Lisboa and Alfredo Vellido},
doi = {doi.org/10.1142/q0322 },
isbn = {978-1-80061-093-4},
year = {2022},
date = {2022-02-01},
urldate = {2022-02-01},
publisher = {World Scientific Publisher},
abstract = {Biology, medicine and biochemistry have become data-centric fields for which Deep Learning methods are delivering groundbreaking results. Addressing high impact challenges, Deep Learning in Biology and Medicine provides an accessible and organic collection of Deep Learning essays on bioinformatics and medicine. It caters for a wide readership, ranging from machine learning practitioners and data scientists seeking methodological knowledge to address biomedical applications, to life science specialists in search of a gentle reference for advanced data analytics.
With contributions from internationally renowned experts, the book covers foundational methodologies in a wide spectrum of life sciences applications, including electronic health record processing, diagnostic imaging, text processing, as well as omics-data processing. This survey of consolidated problems is complemented by a selection of advanced applications, including cheminformatics and biomedical interaction network analysis. A modern and mindful approach to the use of data-driven methodologies in the life sciences also requires careful consideration of the associated societal, ethical, legal and transparency challenges, which are covered in the concluding chapters of this book.},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
With contributions from internationally renowned experts, the book covers foundational methodologies in a wide spectrum of life sciences applications, including electronic health record processing, diagnostic imaging, text processing, as well as omics-data processing. This survey of consolidated problems is complemented by a selection of advanced applications, including cheminformatics and biomedical interaction network analysis. A modern and mindful approach to the use of data-driven methodologies in the life sciences also requires careful consideration of the associated societal, ethical, legal and transparency challenges, which are covered in the concluding chapters of this book.
2020
@inbook{graphsBDDL2020,
title = {Deep Learning for Graphs},
author = {Davide Bacciu and Alessio Micheli},
editor = {Luca Oneto and Nicolo Navarin and Alessandro Sperduti and Davide Anguita
},
url = {https://link.springer.com/chapter/10.1007/978-3-030-43883-8_5},
doi = {10.1007/978-3-030-43883-8_5},
isbn = {978-3-030-43883-8},
year = {2020},
date = {2020-04-04},
booktitle = {Recent Trends in Learning From Data: Tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019)},
volume = {896},
pages = {99-127},
publisher = {Springer International Publishing},
series = {Studies in Computational Intelligence Series},
abstract = {We introduce an overview of methods for learning in structured domains covering foundational works developed within the last twenty years to deal with a whole range of complex data representations, including hierarchical structures, graphs and networks, and giving special attention to recent deep learning models for graphs. While we provide a general introduction to the field, we explicitly focus on the neural network paradigm showing how, across the years, these models have been extended to the adaptive processing of incrementally more complex classes of structured data. The ultimate aim is to show how to cope with the fundamental issue of learning adaptive representations for samples with varying size and topology.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
2017
@inbook{iotBook17,
title = {Internet of Robotic Things - Converging Sensing/Actuating, Hyperconnectivity, Artificial Intelligence and IoT Platforms},
author = {Vermesan Ovidiu and Broring Arne and Tragos Elias and Serrano Martin and Bacciu Davide and Chessa Stefano and Gallicchio Claudio and Micheli Alessio and Dragone Mauro and Saffiotti Alessandro and Simoens Pieter and Cavallo Filippo and Bahr Roy},
editor = {Ovidiu Vermesan and Joel Bacquet},
url = {http://www.riverpublishers.com/downloadchapter.php?file=RP_9788793609105C4.pdf},
doi = {10.13052/rp-9788793609105},
isbn = {9788793609105},
year = {2017},
date = {2017-06-28},
booktitle = {Cognitive Hyperconnected Digital Transformation: Internet of Things Intelligence Evolution},
pages = {97-155},
publisher = {River Publishers},
chapter = {4},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
2015
@inbook{Bacciu2015,
title = {Probabilistic Modeling in Machine Learning},
author = {Davide Bacciu and Paulo J.G. Lisboa and Alessandro Sperduti and Thomas Villmann},
editor = {Janusz Kacprzyk and Witold Pedrycz},
url = {http://dx.doi.org/10.1007/978-3-662-43505-2_31},
doi = {10.1007/978-3-662-43505-2_31},
isbn = {978-3-662-43505-2},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
pages = {545--575},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Books
Deep Learning in Biology and Medicine Book World Scientific Publisher, 2022, ISBN: 978-1-80061-093-4. Deep Learning for Graphs Book Chapter In: Oneto, Luca; Navarin, Nicolo; Sperduti, Alessandro; Anguita, Davide (Ed.): Recent Trends in Learning From Data: Tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019), vol. 896, pp. 99-127, Springer International Publishing, 2020, ISBN: 978-3-030-43883-8. In: Vermesan, Ovidiu; Bacquet, Joel (Ed.): Cognitive Hyperconnected Digital Transformation: Internet of Things Intelligence Evolution, Chapter 4, pp. 97-155, River Publishers, 2017, ISBN: 9788793609105. Probabilistic Modeling in Machine Learning Book Chapter In: Kacprzyk, Janusz; Pedrycz, Witold (Ed.): pp. 545–575, Springer Berlin Heidelberg, Berlin, Heidelberg, 2015, ISBN: 978-3-662-43505-2.2022
2020
2017
2015