Carlo V. Cannistraci Prof. PhD. Eng.
Zhou Yahui Chair Professor
Chief Scientist, Tsinghua Laboratory of Brain and Intelligence (THBI)
Director, Center for Complex Network Intelligence (CCNI) at THBI
Adjunct Professor, Department of Computer Science
Adjunct Professor, Department of Biomedical Engineering
Tsinghua University, Beijing, China
Affiliated Faculty, Center for Systems Biology Dresden (CSBD)
An initiative of Max-Planck Society and Technische Universität Dresden, Germany
Dr. Cannistraci is a theoretical engineer and computational innovator. He is a Professor in the Tsinghua Laboratory of Brain and Intelligence (THBI) and an adjunct professor in the Department of Computer Science and in the Department of Biomedical Engineering at Tsinghua University. He directs the Center for Complex Network Intelligence (CCNI) in THBI, which seeks to create pioneering algorithms at the interface between information science, physics of complex systems, complex networks and machine intelligence, with a particular focus in brain/life-inspired computing for big data analysis. These computational methods are often applied to precision biomedicine, neuroscience, social and economic science.
Dr. Cannistraci is also an affiliated faculty of the Center for Systems Biology Dresden (CSBD), which is a joint interdisciplinary research initiative between the Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG), the Max Planck Institute for the Physics of Complex Systems (MPI-PKS) and the Technische Universität Dresden (TUD), one of the eleven German Excellence Universities.
Research strategy at CCNI
Organization chart of research at the Center for Complex Network Intelligence (CCNI). The chart clarifies what is the main strategy behind research at CCNI and how it influences the topics of our interest in theoretical and applied science. There is a clear interrelation between theoretical topics (which are crucial to offer new solutions in applied topics) and applied topics (which play an indispensable role to open new questions and to trigger innovation in theoretical research).
Research directions at CCNI
The CCNI adopts a transdisciplinary approach integrating information theory, machine intelligence and network science to investigate adaptive processes that characterize complex interacting systems at different scales, from molecules to ecological and socio-economic systems. This knowledge is leveraged to create novel and more efficient artificial intelligence algorithms; and to perform advanced analysis of patterns hidden in data, signals and images. Our theoretical effort is to translate advanced mathematical paradigms typically adopted in theoretical physics (like topology and manifold theory) to characterize many-body interactions in quantitative life science. We apply the theoretical frameworks we invent in the mission to develop computational tools for systems and network biology, personalized biomedicine and combinatorial drug therapy, social and economic data science.
Plasticity phenomena – like remodelling, growth and evolution – modify the topology of complex living systems, their internal state and their multidimensional representation in form of networks or high-dimensional datasets. Our theoretical mission is to elucidate the general rules and mechanisms that underlie this type of structural plasticity, which is at the basis of learning and memory processes in living organisms. In particular, we develop methods for topological analysis of self-adaptive and self-organizing learning systems such as protein interaction and bacteria-metabolite networks at the molecular level, and brain networks at the cellular level.
In neuroscience, we are interested in how the brain networks wire at synaptic and functional levels to modulate learning processes. And, on a molecular pathway scale, we seek to identify the network patterns that could suggest which broken functional modules are responsible for memory aberrations in neurodegenerative diseases. Since general paradigms of regeneration and degeneration can be significantly inspired by developmental biology models, we study regulatory patterns of tissue differentiation in normal and cancer conditions.
Our mission in translation and network medicine is to adopt advanced machine learning and network science approaches to integrate molecular networks and omic profiles for the definition of personalised therapeutical plans and individualised drug treatments. Furthermore, as the cardiovascular system is a paradigmatic example of an adaptive complex system, we apply our pattern recognition algorithms to explore normal/pathological conditions in cardiovascular patients.
Finally, since the CCNI aims to study the complexity of life systems and their mechanisms of adaptation across scales, we collaborate with experts in ecological, social and economic science in order to apply our algorithms to their data and to reveal whether generalized rules of self-organization characterize life matter from molecules and cells to microbes and animals (including humans).
Italian Inter-polytechnic School of Doctorate (Turin, Milan and Bari), PhD, 2010.
Polytechnic School of Milan, Milan, Italy, MS, 2005.
Biotechnologisches Zentrum (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Technische Universität Dresden (TUD), Germany. Principal Investigator (Tenured)，2019 -2020
Biotechnologisches Zentrum (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Technische Universität Dresden (TUD), Germany. Principal Investigator (Tenure-track), 2014-2018
King Abdullah University of Science and Technology (KAUST), Jeddah, Saudi Arabia. Researcher Scientist, 2013
King Abdullah University of Science and Technology (KAUST), Jeddah, Saudi Arabia. Postdoctoral Researcher, 2010-2012
University of California, San Diego, USA. Visiting Scholar, 2009
San Raffaele Scientific Institute, Milan, Italy. Research Fellow, 2006-2010
Biomedical Engineering Institute of the Italian National Research Council (ISIB-CNR, Milan department), Milan, Italy. Research Fellow, 2005-2006
Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome
Claudio Duran, …, Giovanni Gasbarrini, Antonio Gasbarrini & Carlo Vittorio Cannistraci
Nature Communications, 2021
Modular gateway-ness connectivity and structural core organization in maritime network science
Mengqiao Xu, Qian Pan, Alessandro Muscoloni, Haoxiang Xia & Carlo Vittorio Cannistraci
Nature Communications, 2020
Three-dimensional facial-image analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle
Xian Xia, … , Carlo Vittorio Cannistraci, Yong Zhou & Jing-Dong J Han.
Nature Metabolism, 2020
Intrinsic plasticity of silicon nanowire neurotransistors for dynamic memory and learning functions
Eunhye Baek, Nikhil Ranjan Das, Carlo Vittorio Cannistraci, … & Gianaurelio Cuniberti
Nature Electronics, 2020
Navigability evaluation of complex networks by greedy routing efficiency
Alessandro Muscoloni & Carlo Vittorio Cannistraci
Proceedings of the National Academy of Sciences, 2019
Pioneering topological methods for network-based drug–target prediction by exploiting a brain-network self-organization theory
Claudio Durán, Simone Daminelli, Josephine M Thomas, V Joachim Haupt, Michael Schroeder & Carlo Vittorio Cannistraci
Briefings in Bioinformatics, 2018
Machine learning meets complex networks via coalescent embedding in the hyperbolic space
Alessandro Muscoloni, … , Ginestra Bianconi & Carlo Vittorio Cannistraci
Nature Communications, 2017
Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks
Simone Daminelli, Josephine Maria Thomas, Claudio Durán & Carlo Vittorio Cannistraci
New Journal of Physics, 2015
A promoter-level mammalian expression atlas
Fantom Consortium (including Carlo Vittorio Cannistraci)
From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks
Carlo Vittorio Cannistraci, Gregorio Alanis-Lobato, Timothy Ravasi
Scientific reports, 2013
Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding
Carlo Vittorio Cannistraci, Gregorio Alanis-Lobato, Timothy Ravasi
Identification and Predictive Value of Interleukin-6+ Interleukin-10+ and Interleukin-6− Interleukin-10+ Cytokine Patterns in ST-Elevation Acute Myocardial Infarction
Enrico Ammirati^, Carlo Vittorio Cannistraci^, … & Attilio Maseri
Circulation Research, 2012 (^first co-authorship)
An atlas of combinatorial transcriptional regulation in mouse and man
Timoty Ravasi^, Harukazu Suzuki^, Carlo Vittorio Cannistraci^, et al.
Cell, 2010 (^first co-authorship)
Email: firstname.lastname@example.org or email@example.com
Office: Chengfu Road 160, Haidian District, Beijing