cfaed Seminar Series
Dr. Carlo Vittorio Cannistraci , Institute for Materials Science, Biomedical Cybernetics Group, Technical University Dresden
Machine learning and complex networks for precision and systems biomedicine
29.06.2017 (Thursday)
, 13:00 - 14:00
HAL, Seminar Room 115 , Hallwachsstr. 3 , 01069 Dresden
About the talk
The talk will present our research at the Biomedical Cybernetics Group that I established about three years ago in Dresden. We adopt a transdisciplinary approach integrating information theory, machine learning and network science to investigate the physics of adaptive processes that characterize complex interacting systems at different scales, from molecules to ecosystems, with a particular attention to biology and medicine. Our theoretical effort is to translate advanced mathematical paradigms typically adopted in theoretical physics (such as topology, network and manifold theory) to characterize many-body interactions in complex systems and quantitative biomedicine. We apply the theoretical frameworks we invent in the mission to develop computational tools for systems and network analysis1,2,3,4,5,6. In
particular, in biomedicine we deal with: prediction of wiring in biological networks1,2, combinatorial and multiscale biomarkers design4, precision biomedicine4, drug repositioning3 and combinatorial drug therapy3. In general, we devise theoretical models of structural organization in complex networks and we leverage this knowledge to create novel and more efficient bioinspired algorithms and to perform advanced analyses and predictions of patterns in complex systems.
About the speaker
Carlo Vittorio Cannistraci is a theoretical engineer with a background in biomedical cybernetics. He is currently head of the Biomedical Cybernetics Group at the Centre for Molecular and Cellular Bioengineering (CMCB) and faculty member of the Department of Physics in the Technical University Dresden. His area of research embraces information theory, machine learning and complex network theory including also applications in computational network and systems biomedicine. Nature Biotechnology selected Carlo’s article (Cell 2010)7 on machine learning in developmental biology to be nominated in the list of 2010 notable breakthroughs in computational biology. Circulation Research featured Carlo’s work (Circulation Research 2012)8 on leveraging a cardiovascular systems biology strategy to predict future outcomes in heart attacks, commenting: “a space-aged evaluation using computational biology”. The Technical University Dresden honoured Carlo of the Young Investigator Award 2016 in Physics for his recent work on the local community-paradigm theory and link prediction in monopartite1 and bipartite2 networks.
References (* indicates first co-authorship)
1. Cannistraci, C. V., Alanis-Lobato, G. & Ravasi, T. From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in
complex networks. Sci. Rep. 3, 1–13 (2013).
2. Daminelli, S., Thomas, J. M., Durán, C. & Cannistraci, C.V. Common neighbours and the local-community-paradigm for topological link prediction in
bipartite networks. New J. Phys. 17, 113037 (2015).
3. Duran, C., … & Cannistraci, C.V.. Pioneering topological methods for network-based drug-target prediction. Briefings in Bioinformatics (2017)
4. Ciucci, S., … & Cannistraci,C.V.. Enlightening discriminative network functional modules behind Principal Component Analysis separation in differentialomic
science studies. Sci. Rep. 7, (2017).
5. Cannistraci, C. V. et al. Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes.
Bioinformatics 26 (18), i531-i539 (2010)
6. Cannitstraci, C.V. et al. Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding Bioinformatics 29 (13), i199-
i209 (2013)
7. Ravasi, T.*, Cannistraci, C.V.* et al. An atlas of combinatorial transcriptional regulation in mouse and man. Cell 140, 744–52 (2010).
8. Ammirati, E.*, Cannistraci C.V.* et al. Identification and predictive value of interleukin-6+ interleukin-10+ and interleukin-6-interleukin-10+ cytokine patterns
in st-elevation acute myocardial infarction. Circ. Res. 111, 1336–1348 (2012).