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Massimiliano Zanin
Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC) (CSIC-UIB), Campus UIB, Palma de Mallorca, Spain

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Article
Published: 28 July 2021 in Scientific Reports
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Functional networks, i.e. networks representing the interactions between the elements of a complex system and reconstructed from the observed elements’ dynamics, are becoming a fundamental tool to unravel the structures created by the movement of information in systems like the human brain. They also present drawbacks, one of the most important being the inherent difficulty in representing and interpreting the resulting structures for large number of nodes and links. I here propose a causality clustering approach, based on grouping nodes into clusters according to their similarity in the overall information dynamics, the latter one being measured by a causality metric. The whole system can then arbitrarily be simplified, with nodes being grouped in e.g. sources, brokers and sinks of information. The advantages and limitations of the proposed approach are discussed using a set of synthetic and real-world data sets, the latter ones representing two neuroscience and technological problems.

ACS Style

Massimiliano Zanin. Simplifying functional network representation and interpretation through causality clustering. Scientific Reports 2021, 11, 1 -12.

AMA Style

Massimiliano Zanin. Simplifying functional network representation and interpretation through causality clustering. Scientific Reports. 2021; 11 (1):1-12.

Chicago/Turabian Style

Massimiliano Zanin. 2021. "Simplifying functional network representation and interpretation through causality clustering." Scientific Reports 11, no. 1: 1-12.

Journal article
Published: 08 June 2021 in Computer Methods and Programs in Biomedicine
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The growing integration of healthcare sources is improving our understanding of diseases. Cross-mapping resources such as UMLS play a very important role in this area, but their coverage is still incomplete. With the aim to facilitate the integration and interoperability of biological, clinical and literary sources in studies of diseases, we built DisMaNET, a system to cross-map terms from disease vocabularies by leveraging the power and interpretability of network analysis. First, we collected and normalized data from 8 disease vocabularies and mapping sources to generate our datasets. Next, we built DisMaNET by integrating the generated datasets into a Neo4j graph database. Then we exploited the query mechanisms of Neo4j to cross-map disease terms of different vocabularies with a relevance score metric and contrasted the results with some state-of-the-art solutions. Finally, we made our system publicly available for its exploitation and evaluation both through a graphical user interface and REST APIs. DisMaNET contains almost half a million nodes and near nine hundred thousand edges, including hierarchical and mapping relationships. Its query capabilities enabled the detection of connections between disease vocabularies that are not present in major mapping sources such as UMLS and the Disease Ontology, even for rare diseases. Furthermore, DisMaNET was capable of obtaining more than 80% of the mappings with UMLS reported in MonDO and DisGeNET, and it was successfully exploited to resolve the missing mappings in the DISNET project. DisMaNET is a powerful, intuitive and publicly available system to cross-map terms from different disease vocabularies. Our study proves that it is a competitive alternative to existing mapping systems, incorporating the potential of network analysis and the interpretability of the results through a visual interface as its main advantages. Expansion with new sources, versioning and the improvement of the search and scoring algorithms are envisioned as future lines of work.

ACS Style

Eduardo P. García del Valle; Gerardo Lagunes García; Lucía Prieto Santamaría; Massimiliano Zanin; Ernestina Menasalvas Ruiz; Alejandro Rodríguez-González. DisMaNET: A network-based tool to cross map disease vocabularies. Computer Methods and Programs in Biomedicine 2021, 207, 106233 .

AMA Style

Eduardo P. García del Valle, Gerardo Lagunes García, Lucía Prieto Santamaría, Massimiliano Zanin, Ernestina Menasalvas Ruiz, Alejandro Rodríguez-González. DisMaNET: A network-based tool to cross map disease vocabularies. Computer Methods and Programs in Biomedicine. 2021; 207 ():106233.

Chicago/Turabian Style

Eduardo P. García del Valle; Gerardo Lagunes García; Lucía Prieto Santamaría; Massimiliano Zanin; Ernestina Menasalvas Ruiz; Alejandro Rodríguez-González. 2021. "DisMaNET: A network-based tool to cross map disease vocabularies." Computer Methods and Programs in Biomedicine 207, no. : 106233.

Journal article
Published: 31 May 2021 in Brain Sciences
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Network-based representations have introduced a revolution in neuroscience, expanding the understanding of the brain from the activity of individual regions to the interactions between them. This augmented network view comes at the cost of high dimensionality, which hinders both our capacity of deciphering the main mechanisms behind pathologies, and the significance of any statistical and/or machine learning task used in processing this data. A link selection method, allowing to remove irrelevant connections in a given scenario, is an obvious solution that provides improved utilization of these network representations. In this contribution we review a large set of statistical and machine learning link selection methods and evaluate them on real brain functional networks. Results indicate that most methods perform in a qualitatively similar way, with NBS (Network Based Statistics) winning in terms of quantity of retained information, AnovaNet in terms of stability and ExT (Extra Trees) in terms of lower computational cost. While machine learning methods are conceptually more complex than statistical ones, they do not yield a clear advantage. At the same time, the high heterogeneity in the set of links retained by each method suggests that they are offering complementary views to the data. The implications of these results in neuroscience tasks are finally discussed.

ACS Style

Ilinka Ivanoska; Kire Trivodaliev; Slobodan Kalajdziski; Massimiliano Zanin. Statistical and Machine Learning Link Selection Methods for Brain Functional Networks: Review and Comparison. Brain Sciences 2021, 11, 735 .

AMA Style

Ilinka Ivanoska, Kire Trivodaliev, Slobodan Kalajdziski, Massimiliano Zanin. Statistical and Machine Learning Link Selection Methods for Brain Functional Networks: Review and Comparison. Brain Sciences. 2021; 11 (6):735.

Chicago/Turabian Style

Ilinka Ivanoska; Kire Trivodaliev; Slobodan Kalajdziski; Massimiliano Zanin. 2021. "Statistical and Machine Learning Link Selection Methods for Brain Functional Networks: Review and Comparison." Brain Sciences 11, no. 6: 735.

Review
Published: 20 May 2021 in Human Brain Mapping
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Graph theory is now becoming a standard tool in system‐level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network representation involves often covert theoretical assumptions and methodological choices which affect the way networks are reconstructed from experimental data, and ultimately the resulting network properties and their interpretation. Here, we review some fundamental conceptual underpinnings and technical issues associated with brain network reconstruction, and discuss how their mutual influence concurs in clarifying the organization of brain function.

ACS Style

Onerva Korhonen; Massimiliano Zanin; David Papo. Principles and open questions in functional brain network reconstruction. Human Brain Mapping 2021, 42, 3680 -3711.

AMA Style

Onerva Korhonen, Massimiliano Zanin, David Papo. Principles and open questions in functional brain network reconstruction. Human Brain Mapping. 2021; 42 (11):3680-3711.

Chicago/Turabian Style

Onerva Korhonen; Massimiliano Zanin; David Papo. 2021. "Principles and open questions in functional brain network reconstruction." Human Brain Mapping 42, no. 11: 3680-3711.

Journal article
Published: 19 May 2021 in IEEE Access
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The Granger test is one of the best known techniques to detect causality relationships among time series, and has been used uncountable times in science and engineering. The quality of its results strongly depends on the quality of the underlying data, and different approaches have been proposed to reduce the impact of, for instance, observational noise or irregular sampling. Less attention has nevertheless been devoted to situations in which the analysed time series are irregularly polluted with missing and extreme values. In this contribution I tackle this problem by comparing four different data pre-processing strategies and evaluating their performance with synthetic time series, both in dyadic tests and functional network contexts. I further apply these strategies to a real-world problem, involving inferring the structure behind the propagation of delays in an air transport system. Finally, some guidelines are provided on when and how these strategies ought to be used.

ACS Style

Massimiliano Zanin. Assessing Granger Causality on Irregular Missing and Extreme Data. IEEE Access 2021, 9, 75362 -75374.

AMA Style

Massimiliano Zanin. Assessing Granger Causality on Irregular Missing and Extreme Data. IEEE Access. 2021; 9 ():75362-75374.

Chicago/Turabian Style

Massimiliano Zanin. 2021. "Assessing Granger Causality on Irregular Missing and Extreme Data." IEEE Access 9, no. : 75362-75374.

Original article
Published: 12 April 2021 in Neuroinformatics
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Anatomical and dynamical connectivity are essential to healthy brain function. However, quantifying variations in connectivity across conditions or between patient populations and appraising their functional significance are highly non-trivial tasks. Here we show that link ranking differences induce specific geometries in a convenient auxiliary space that are often easily recognisable at mere eye inspection. Link ranking can also provide fast and reliable criteria for network reconstruction parameters for which no theoretical guideline has been proposed.

ACS Style

Massimiliano Zanin; Ilinka Ivanoska; Bahar Güntekin; Görsev Yener; Tatjana Loncar-Turukalo; Niksa Jakovljevic; Olivera Sveljo; David Papo. A Fast Transform for Brain Connectivity Difference Evaluation. Neuroinformatics 2021, 1 -15.

AMA Style

Massimiliano Zanin, Ilinka Ivanoska, Bahar Güntekin, Görsev Yener, Tatjana Loncar-Turukalo, Niksa Jakovljevic, Olivera Sveljo, David Papo. A Fast Transform for Brain Connectivity Difference Evaluation. Neuroinformatics. 2021; ():1-15.

Chicago/Turabian Style

Massimiliano Zanin; Ilinka Ivanoska; Bahar Güntekin; Görsev Yener; Tatjana Loncar-Turukalo; Niksa Jakovljevic; Olivera Sveljo; David Papo. 2021. "A Fast Transform for Brain Connectivity Difference Evaluation." Neuroinformatics , no. : 1-15.

Review article
Published: 01 February 2021 in Network and Systems Medicine
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Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields.Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references.Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

ACS Style

Massimiliano Zanin; Nadim A.A. Aitya; José Basilio; Jan Baumbach; Arriel Benis; Chandan K. Behera; Magda Bucholc; Filippo Castiglione; Ioanna Chouvarda; Blandine Comte; Tien-Tuan Dao; Xuemei Ding; Estelle Pujos-Guillot; Nenad Filipovic; David P. Finn; David H. Glass; Nissim Harel; Tomas Iesmantas; Ilinka Ivanoska; Alok Joshi; Karim Zouaoui Boudjeltia; Badr Kaoui; Daman Kaur; Liam P. Maguire; Paula L. McClean; Niamh McCombe; João Luís de Miranda; Mihnea Alexandru Moisescu; Francesco Pappalardo; Annikka Polster; Girijesh Prasad; Damjana Rozman; Ioan Sacala; Jose M. Sanchez-Bornot; Johannes A. Schmid; Trevor Sharp; Jordi Solé-Casals; Vojtěch Spiwok; George M. Spyrou; Egils Stalidzans; Blaž Stres; Tijana Sustersic; Ioannis Symeonidis; Paolo Tieri; Stephen Todd; Kristel Van Steen; Milena Veneva; Da-Hui Wang; Haiying Wang; Hui Wang; Steven Watterson; KongFatt Wong-Lin; Su Yang; Xin Zou; Harald H.H.W. Schmidt. An Early Stage Researcher's Primer on Systems Medicine Terminology. Network and Systems Medicine 2021, 4, 2 -50.

AMA Style

Massimiliano Zanin, Nadim A.A. Aitya, José Basilio, Jan Baumbach, Arriel Benis, Chandan K. Behera, Magda Bucholc, Filippo Castiglione, Ioanna Chouvarda, Blandine Comte, Tien-Tuan Dao, Xuemei Ding, Estelle Pujos-Guillot, Nenad Filipovic, David P. Finn, David H. Glass, Nissim Harel, Tomas Iesmantas, Ilinka Ivanoska, Alok Joshi, Karim Zouaoui Boudjeltia, Badr Kaoui, Daman Kaur, Liam P. Maguire, Paula L. McClean, Niamh McCombe, João Luís de Miranda, Mihnea Alexandru Moisescu, Francesco Pappalardo, Annikka Polster, Girijesh Prasad, Damjana Rozman, Ioan Sacala, Jose M. Sanchez-Bornot, Johannes A. Schmid, Trevor Sharp, Jordi Solé-Casals, Vojtěch Spiwok, George M. Spyrou, Egils Stalidzans, Blaž Stres, Tijana Sustersic, Ioannis Symeonidis, Paolo Tieri, Stephen Todd, Kristel Van Steen, Milena Veneva, Da-Hui Wang, Haiying Wang, Hui Wang, Steven Watterson, KongFatt Wong-Lin, Su Yang, Xin Zou, Harald H.H.W. Schmidt. An Early Stage Researcher's Primer on Systems Medicine Terminology. Network and Systems Medicine. 2021; 4 (1):2-50.

Chicago/Turabian Style

Massimiliano Zanin; Nadim A.A. Aitya; José Basilio; Jan Baumbach; Arriel Benis; Chandan K. Behera; Magda Bucholc; Filippo Castiglione; Ioanna Chouvarda; Blandine Comte; Tien-Tuan Dao; Xuemei Ding; Estelle Pujos-Guillot; Nenad Filipovic; David P. Finn; David H. Glass; Nissim Harel; Tomas Iesmantas; Ilinka Ivanoska; Alok Joshi; Karim Zouaoui Boudjeltia; Badr Kaoui; Daman Kaur; Liam P. Maguire; Paula L. McClean; Niamh McCombe; João Luís de Miranda; Mihnea Alexandru Moisescu; Francesco Pappalardo; Annikka Polster; Girijesh Prasad; Damjana Rozman; Ioan Sacala; Jose M. Sanchez-Bornot; Johannes A. Schmid; Trevor Sharp; Jordi Solé-Casals; Vojtěch Spiwok; George M. Spyrou; Egils Stalidzans; Blaž Stres; Tijana Sustersic; Ioannis Symeonidis; Paolo Tieri; Stephen Todd; Kristel Van Steen; Milena Veneva; Da-Hui Wang; Haiying Wang; Hui Wang; Steven Watterson; KongFatt Wong-Lin; Su Yang; Xin Zou; Harald H.H.W. Schmidt. 2021. "An Early Stage Researcher's Primer on Systems Medicine Terminology." Network and Systems Medicine 4, no. 1: 2-50.

Journal article
Published: 17 November 2020 in Chaos: An Interdisciplinary Journal of Nonlinear Science
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Though carrying considerable economic and societal costs, restricting individuals’ traveling freedom appears as a logical way to curb the spreading of an epidemic. However, whether, under what conditions, and to what extent travel restrictions actually exert a mitigating effect on epidemic spreading are poorly understood issues. Recent studies have actually suggested the opposite, i.e., that allowing some movements can hinder the propagation of a disease. Here, we explore this topic by modeling the spreading of a generic contagious disease where susceptible, infected, or recovered point-wise individuals are uncorrelated random-walkers evolving within a space comprising two equally sized separated compartments. We evaluate the spreading process under different separation conditions between the two spatial compartments and a forced relocation schedule. Our results confirm that, under certain conditions, allowing individuals to move from regions of high to low infection rates may turn out to have a positive effect on aggregate; such positive effect is nevertheless reduced if a directional flow is allowed. This highlights the importance of considering travel restriction policies alternative to classical ones.

ACS Style

Massimiliano Zanin; David Papo. Travel restrictions during pandemics: A useful strategy? Chaos: An Interdisciplinary Journal of Nonlinear Science 2020, 30, 111103 .

AMA Style

Massimiliano Zanin, David Papo. Travel restrictions during pandemics: A useful strategy? Chaos: An Interdisciplinary Journal of Nonlinear Science. 2020; 30 (11):111103.

Chicago/Turabian Style

Massimiliano Zanin; David Papo. 2020. "Travel restrictions during pandemics: A useful strategy?" Chaos: An Interdisciplinary Journal of Nonlinear Science 30, no. 11: 111103.

Journal article
Published: 10 November 2020 in npj Systems Biology and Applications
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Mitochondrial dysfunction is linked to pathogenesis of Parkinson’s disease (PD). However, individual mitochondria-based analyses do not show a uniform feature in PD patients. Since mitochondria interact with each other, we hypothesize that PD-related features might exist in topological patterns of mitochondria interaction networks (MINs). Here we show that MINs formed nonclassical scale-free supernetworks in colonic ganglia both from healthy controls and PD patients; however, altered network topological patterns were observed in PD patients. These patterns were highly correlated with PD clinical scores and a machine-learning approach based on the MIN features alone accurately distinguished between patients and controls with an area-under-curve value of 0.989. The MINs of midbrain dopaminergic neurons (mDANs) derived from several genetic PD patients also displayed specific changes. CRISPR/CAS9-based genome correction of alpha-synuclein point mutations reversed the changes in MINs of mDANs. Our organelle-interaction network analysis opens another critical dimension for a deeper characterization of various complex diseases with mitochondrial dysregulation.

ACS Style

Massimiliano Zanin; Bruno F. R. Santos; Paul M. A. Antony; Clara Berenguer-Escuder; Simone B. Larsen; Zoé Hanss; Peter A. Barbuti; Aidos S. Baumuratov; Dajana Grossmann; Christophe M. Capelle; Joseph Weber; Rudi Balling; Markus Ollert; Rejko Krüger; Nico J. Diederich; Feng Q. He. Mitochondria interaction networks show altered topological patterns in Parkinson’s disease. npj Systems Biology and Applications 2020, 6, 1 -12.

AMA Style

Massimiliano Zanin, Bruno F. R. Santos, Paul M. A. Antony, Clara Berenguer-Escuder, Simone B. Larsen, Zoé Hanss, Peter A. Barbuti, Aidos S. Baumuratov, Dajana Grossmann, Christophe M. Capelle, Joseph Weber, Rudi Balling, Markus Ollert, Rejko Krüger, Nico J. Diederich, Feng Q. He. Mitochondria interaction networks show altered topological patterns in Parkinson’s disease. npj Systems Biology and Applications. 2020; 6 (1):1-12.

Chicago/Turabian Style

Massimiliano Zanin; Bruno F. R. Santos; Paul M. A. Antony; Clara Berenguer-Escuder; Simone B. Larsen; Zoé Hanss; Peter A. Barbuti; Aidos S. Baumuratov; Dajana Grossmann; Christophe M. Capelle; Joseph Weber; Rudi Balling; Markus Ollert; Rejko Krüger; Nico J. Diederich; Feng Q. He. 2020. "Mitochondria interaction networks show altered topological patterns in Parkinson’s disease." npj Systems Biology and Applications 6, no. 1: 1-12.

Original paper
Published: 12 October 2020 in Brain Topography
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In spite of the large attention received by brain activity analyses through functional networks, the effects of uncertainty on such representations have mostly been neglected. We here elaborate the hypothesis that such uncertainty is not just a nuisance, but that on the contrary is condition-dependent. We test this hypothesis by analysing a large set of EEG brain recordings corresponding to control subjects and patients suffering from alcoholism, through the reconstruction of the corresponding Maximum Spanning Trees (MSTs), the assessment of their topological differences, and the comparison of two frequentist and Bayesian reconstruction approaches. A machine learning model demonstrates that the Bayesian reconstruction encodes more information than the frequentist one, and that such additional information is related to the uncertainty of the topological structures. We finally show how the Bayesian approach is more effective in the validation of generative models, over and above the frequentist one, by proposing and disproving two models based on additive noise.

ACS Style

Massimiliano Zanin; Seddik Belkoura; Javier Gomez; César Alfaro; Javier Cano. Uncertainty in Functional Network Representations of Brain Activity of Alcoholic Patients. Brain Topography 2020, 34, 6 -18.

AMA Style

Massimiliano Zanin, Seddik Belkoura, Javier Gomez, César Alfaro, Javier Cano. Uncertainty in Functional Network Representations of Brain Activity of Alcoholic Patients. Brain Topography. 2020; 34 (1):6-18.

Chicago/Turabian Style

Massimiliano Zanin; Seddik Belkoura; Javier Gomez; César Alfaro; Javier Cano. 2020. "Uncertainty in Functional Network Representations of Brain Activity of Alcoholic Patients." Brain Topography 34, no. 1: 6-18.

Journal article
Published: 07 September 2020 in IEEE Access
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Trajectories optimisation is a major research topic in air transport and air traffic management, due to its profound impact both on passengers, airlines and the environment in general, and consequently on the perceived value and cost of air transportation. While the challenges associated to the optimisation of the en-route part of a flight are well understood, relative less attention has been devoted to the last part, i.e. the approach and landing. Here we show how open large-scale data sets of aircraft trajectories can be used to characterise the efficiency of flights landing at an airport, measured through the time and distance flown below 10,000 feet. The yielded picture is highly heterogeneous, with the time spent at low altitude varying from an average of 10 minutes for Zurich, up to 16 minutes for London Heathrow. Flights arriving at the same airport also experience highly different times, e.g. from 12 to 20 minutes for London Heathrow, depending on factors like traffic volumes, time of the year and of the day, and on interactions with other traffic patterns and airports. From a more general perspective, this contribution illustrates how the availability of large data sets can be used to improve our understanding of the real behaviour of the system, and especially its deviation from what planned.

ACS Style

Massimiliano Zanin. Assessing Airport Landing Efficiency Through Large-Scale Flight Data Analysis. IEEE Access 2020, 8, 170519 -170528.

AMA Style

Massimiliano Zanin. Assessing Airport Landing Efficiency Through Large-Scale Flight Data Analysis. IEEE Access. 2020; 8 (99):170519-170528.

Chicago/Turabian Style

Massimiliano Zanin. 2020. "Assessing Airport Landing Efficiency Through Large-Scale Flight Data Analysis." IEEE Access 8, no. 99: 170519-170528.

Journal article
Published: 04 September 2020 in Applied Sciences
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Air transport delays are a major source of direct and opportunity costs in modern societies, being this problem is especially important in the case of China. In spite of this, our knowledge on delay generation is mostly based on intuition, and the scientific community has hitherto devoted little attention to this topic. We here present the first data-driven systemic study of air transport delays in China, of their evolution and causes, based on 11 million flights between 2016 and 2018. A significant fraction of the delays can be explained by a few variables, e.g., weather conditions and traffic levels, the most important factors being the presence of thunderstorms and the season of the year. Remaining delays can often be explained by en-route weather phenomena or by reactionary delays. This study contributes towards a better understanding of delays and their prediction through a data-driven methodology, leveraging on statistics and data mining concepts.

ACS Style

Massimiliano Zanin; Yanbo Zhu; Ran Yan; Peiji Dong; Xiaoqian Sun; Sebastian Wandelt. Characterization and Prediction of Air Transport Delays in China. Applied Sciences 2020, 10, 6165 .

AMA Style

Massimiliano Zanin, Yanbo Zhu, Ran Yan, Peiji Dong, Xiaoqian Sun, Sebastian Wandelt. Characterization and Prediction of Air Transport Delays in China. Applied Sciences. 2020; 10 (18):6165.

Chicago/Turabian Style

Massimiliano Zanin; Yanbo Zhu; Ran Yan; Peiji Dong; Xiaoqian Sun; Sebastian Wandelt. 2020. "Characterization and Prediction of Air Transport Delays in China." Applied Sciences 10, no. 18: 6165.

Journal article
Published: 28 July 2020 in Chaos, Solitons & Fractals
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Italy has been one of the countries hardest hit by the coronavirus disease (COVID-19) pandemic. While the overall policy in response to the epidemic was to a large degree centralized, the regional basis of the healthcare system represented an important factor affecting the natural dynamics of the disease induced geographic specificities. Here, we characterize the region-specific modulation of COVID dynamics with a reduced exponential model leveraging available data on sub-intensive and intensive care unit patients made available by all regional councils from the very onset of the disease. This simple model provides a rather good fit of regional patient dynamics, particularly for regions where the affected population was large, highlighting important region-specific patterns of epidemic dynamics.

ACS Style

David Papo; Marco Righetti; Luciano Fadiga; Fabio Biscarini; Massimiliano Zanin. A minimal model of hospital patients’ dynamics in COVID-19. Chaos, Solitons & Fractals 2020, 140, 110157 -110157.

AMA Style

David Papo, Marco Righetti, Luciano Fadiga, Fabio Biscarini, Massimiliano Zanin. A minimal model of hospital patients’ dynamics in COVID-19. Chaos, Solitons & Fractals. 2020; 140 ():110157-110157.

Chicago/Turabian Style

David Papo; Marco Righetti; Luciano Fadiga; Fabio Biscarini; Massimiliano Zanin. 2020. "A minimal model of hospital patients’ dynamics in COVID-19." Chaos, Solitons & Fractals 140, no. : 110157-110157.

Conference paper
Published: 01 July 2020 in 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)
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While classical disease nosology is based on phenotypical characteristics, the increasing availability of biological and molecular data is providing new understanding of diseases and their underlying relationships, that could lead to a more comprehensive paradigm for modern medicine. In the present work, similarities between diseases are used to study the generation of new possible disease nosologic models that include both phenotypical and biological information. To this aim, disease similarity is measured in terms of disease feature vectors, that stood for genes, proteins, metabolic pathways and PPIs in the case of biological similarity, and for symptoms in the case of phenotypical similarity. An improvement in similarity computation is proposed, considering weighted instead of Booleans feature vectors. Unsupervised learning methods were applied to these data, specifically, density-based DBSCAN clustering algorithm. As evaluation metric silhouette coefficient was chosen, even though the number of clusters and the number of outliers were also considered. As a results validation, a comparison with randomly distributed data was performed. Results suggest that weighted biological similarities based on proteins, and computed according to cosine index, may provide a good starting point to rearrange disease taxonomy and nosology.

ACS Style

Lucía Prieto Santamaría; Eduardo P. Garcia Del Valle; Gerardo Lagunes García; Massimiliano Zanin; Alejandro Rodríguez González; Ernestina Menasalvas Ruiz; Yuliana Perez Gallardo; Gandhi Samuel Hernandez Chan. Analysis of New Nosological Models from Disease Similarities using Clustering. 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) 2020, 183 -188.

AMA Style

Lucía Prieto Santamaría, Eduardo P. Garcia Del Valle, Gerardo Lagunes García, Massimiliano Zanin, Alejandro Rodríguez González, Ernestina Menasalvas Ruiz, Yuliana Perez Gallardo, Gandhi Samuel Hernandez Chan. Analysis of New Nosological Models from Disease Similarities using Clustering. 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS). 2020; ():183-188.

Chicago/Turabian Style

Lucía Prieto Santamaría; Eduardo P. Garcia Del Valle; Gerardo Lagunes García; Massimiliano Zanin; Alejandro Rodríguez González; Ernestina Menasalvas Ruiz; Yuliana Perez Gallardo; Gandhi Samuel Hernandez Chan. 2020. "Analysis of New Nosological Models from Disease Similarities using Clustering." 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) , no. : 183-188.

Journal article
Published: 12 June 2020 in Chaos, Solitons & Fractals
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Among the many efforts done by the scientific community to help coping with the COVID-19 pandemic, one of the most important has been the creation of models to describe its propagation, as these are expected to guide the deployment of containment and health policies. These models are commonly based on exogenous information, as e.g. mobility data, whose limitedness always compromise the reliability of obtained results. In this contribution we propose a different approach, based on extracting relationships between the evolution of the disease in different regions through information theoretical metrics. In a way similar to what is commonly done in neuroscience, propagation is understood as information transfer, and the resulting propagation patterns are represented and studied as functional networks. By applying this methodology to the dynamics of COVID-19 in several countries and regions thereof, we were able to reconstruct static and time-varying propagation graphs. We further discuss the advantages, promises and open research questions associated with this functional approach.

ACS Style

Massimiliano Zanin; David Papo. Assessing functional propagation patterns in COVID-19. Chaos, Solitons & Fractals 2020, 138, 109993 .

AMA Style

Massimiliano Zanin, David Papo. Assessing functional propagation patterns in COVID-19. Chaos, Solitons & Fractals. 2020; 138 ():109993.

Chicago/Turabian Style

Massimiliano Zanin; David Papo. 2020. "Assessing functional propagation patterns in COVID-19." Chaos, Solitons & Fractals 138, no. : 109993.

Journal article
Published: 01 June 2020 in Chaos: An Interdisciplinary Journal of Nonlinear Science
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ACS Style

F. Olivares; M. Zanin; Luciano Zunino; D. G. Pérez. Contrasting chaotic with stochastic dynamics via ordinal transition networks. Chaos: An Interdisciplinary Journal of Nonlinear Science 2020, 30, 063101 .

AMA Style

F. Olivares, M. Zanin, Luciano Zunino, D. G. Pérez. Contrasting chaotic with stochastic dynamics via ordinal transition networks. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2020; 30 (6):063101.

Chicago/Turabian Style

F. Olivares; M. Zanin; Luciano Zunino; D. G. Pérez. 2020. "Contrasting chaotic with stochastic dynamics via ordinal transition networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 30, no. 6: 063101.

Journal article
Published: 03 May 2020 in Artificial Intelligence in Medicine
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The automatic extraction of a patient's natural history from Electronic Health Records (EHRs) is a critical step towards building intelligent systems that can reason about clinical variables and support decision making. Although EHRs contain a large amount of valuable information about the patient's medical care, this information can only be fully understood when analyzed in a temporal context. Any intelligent system should then be able to extract medical concepts, date expressions, temporal relations and the temporal ordering of medical events from the free texts of EHRs; yet, this task is hard to tackle, due to the domain specific nature of EHRs, writing quality and lack of structure of these texts, and more generally the presence of redundant information. In this paper, we introduce a new Natural Language Processing (NLP) framework, capable of extracting the aforementioned elements from EHRs written in Spanish using rule-based methods. We focus on building medical timelines, which include disease diagnosis and its progression over time. By using a large dataset of EHRs comprising information about patients suffering from lung cancer, we show that our framework has an adequate level of performance by correctly building the timeline for 843 patients from a pool of 989 patients, achieving a precision of 0.852.

ACS Style

Marjan Najafabadipour; Massimiliano Zanin; Alejandro Rodríguez-González; Maria Torrente; Beatriz Nuñez García; Juan Luis Cruz Bermudez; Mariano Provencio; Ernestina Menasalvas. Reconstructing the patient’s natural history from electronic health records. Artificial Intelligence in Medicine 2020, 105, 101860 .

AMA Style

Marjan Najafabadipour, Massimiliano Zanin, Alejandro Rodríguez-González, Maria Torrente, Beatriz Nuñez García, Juan Luis Cruz Bermudez, Mariano Provencio, Ernestina Menasalvas. Reconstructing the patient’s natural history from electronic health records. Artificial Intelligence in Medicine. 2020; 105 ():101860.

Chicago/Turabian Style

Marjan Najafabadipour; Massimiliano Zanin; Alejandro Rodríguez-González; Maria Torrente; Beatriz Nuñez García; Juan Luis Cruz Bermudez; Mariano Provencio; Ernestina Menasalvas. 2020. "Reconstructing the patient’s natural history from electronic health records." Artificial Intelligence in Medicine 105, no. : 101860.

Preprint content
Published: 10 April 2020
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While classical disease nosology is based on phenotypical characteristics, the increasing availability of biological and molecular data is providing new understanding of diseases and their underlying relationships, that could lead to a more comprehensive paradigm for modern medicine. In the present work, similarities between diseases are used to study the generation of new possible disease nosologic models that include both phenotypical and biological information. To this aim, disease similarity is measured in terms of disease feature vectors, that stood for genes, proteins, metabolic pathways and PPIs in the case of biological similarity, and for symptoms in the case of phenotypical similarity. An improvement in similarity computation is proposed, considering weighted instead of Booleans feature vectors. Unsupervised learning methods were applied to these data, specifically, density-based DBSCAN clustering algorithm. As evaluation metric silhouette coefficient was chosen, even though the number of clusters and the number of outliers were also considered. As a results validation, a comparison with randomly distributed data was performed. Results suggest that weighted biological similarities based on proteins, and computed according to cosine index, may provide a good starting point to rearrange disease taxonomy and nosology.

ACS Style

Lucía Prieto Santamaría; Eduardo P. García Del Valle; Gerardo Lagunes García; Massimiliano Zanin; Alejandro Rodríguez González; Ernestina Menasalvas Ruiz; Yuliana Pérez Gallardo; Gandhi Samuel Hernández Chan. Analysis of new nosological models from disease similarities using clustering. 2020, 1 .

AMA Style

Lucía Prieto Santamaría, Eduardo P. García Del Valle, Gerardo Lagunes García, Massimiliano Zanin, Alejandro Rodríguez González, Ernestina Menasalvas Ruiz, Yuliana Pérez Gallardo, Gandhi Samuel Hernández Chan. Analysis of new nosological models from disease similarities using clustering. . 2020; ():1.

Chicago/Turabian Style

Lucía Prieto Santamaría; Eduardo P. García Del Valle; Gerardo Lagunes García; Massimiliano Zanin; Alejandro Rodríguez González; Ernestina Menasalvas Ruiz; Yuliana Pérez Gallardo; Gandhi Samuel Hernández Chan. 2020. "Analysis of new nosological models from disease similarities using clustering." , no. : 1.

Preprint content
Published: 10 March 2020
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SUMMARYMitochondrial dysfunction is linked to pathogenesis of Parkinson’s disease (PD). However, individual-mitochondria-based analyses do not show a uniform feature in PD patients. Since mitochondria interact with each other, we hypothesize that PD-related features might exist in topological patterns of mitochondria-mitochondria interaction networks (MINs). Here we showed that MINs form non-classical scale-free supernetworks in colonic ganglia both from healthy controls and PD patients, however, altered topological patterns are observed in PD patients. These patterns highly correlate with PD clinical scores and a machine-learning approach based on the MIN features accurately distinguish between patients and controls with an area-under-curve value of 0.989. The MINs of midbrain dopaminergic neurons (mDANs) derived from several genetic PD patients also display specific changes. CRISPR/CAS9-based genome correction of alpha-synuclein point mutations reverses the changes in MINs of mDANs. Our MIN network analysis opens a new dimension for a deeper characterization of various complex diseases with mitochondrial dysregulation.

ACS Style

Massimiliano Zanin; Bruno F. R. Santos; Paul M.A. Antony; Clara Berenguer-Escuder; Simone B. Larsen; Zoé Hanss; Peter Barbuti; Aidos S. Baumuratov; Dajana Grossmann; Christophe Capelle; Joseph Weber; Rudi Balling; Markus Ollert; Rejko Krueger; Nico J. Diederich; Feng Q. He. Mitochondria-mitochondria interaction networks show altered topological patterns in Parkinson’s disease. 2020, 1 .

AMA Style

Massimiliano Zanin, Bruno F. R. Santos, Paul M.A. Antony, Clara Berenguer-Escuder, Simone B. Larsen, Zoé Hanss, Peter Barbuti, Aidos S. Baumuratov, Dajana Grossmann, Christophe Capelle, Joseph Weber, Rudi Balling, Markus Ollert, Rejko Krueger, Nico J. Diederich, Feng Q. He. Mitochondria-mitochondria interaction networks show altered topological patterns in Parkinson’s disease. . 2020; ():1.

Chicago/Turabian Style

Massimiliano Zanin; Bruno F. R. Santos; Paul M.A. Antony; Clara Berenguer-Escuder; Simone B. Larsen; Zoé Hanss; Peter Barbuti; Aidos S. Baumuratov; Dajana Grossmann; Christophe Capelle; Joseph Weber; Rudi Balling; Markus Ollert; Rejko Krueger; Nico J. Diederich; Feng Q. He. 2020. "Mitochondria-mitochondria interaction networks show altered topological patterns in Parkinson’s disease." , no. : 1.

Original research article
Published: 22 January 2020 in Frontiers in Physiology
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Characterizing brain activity at rest is of paramount importance to our understanding both of general principles of brain functioning and of the way brain dynamics is affected in the presence of neurological or psychiatric pathologies. We measured the time-reversal symmetry of spontaneous electroencephalographic brain activity recorded from three groups of patients and their respective control group under two experimental conditions (eyes open and closed). We evaluated differences in time irreversibility in terms of possible underlying physical generating mechanisms. The results showed that resting brain activity is generically time-irreversible at sufficiently long time scales, and that brain pathology is generally associated with a reduction in time-asymmetry, albeit with pathology-specific patterns. The significance of these results and their possible dynamical etiology are discussed. Some implications of the differential modulation of time asymmetry by pathology and experimental condition are examined.

ACS Style

Massimiliano Zanin; Bahar Güntekin; Tuba Aktürk; Lütfü Hanoğlu; David Papo. Time Irreversibility of Resting-State Activity in the Healthy Brain and Pathology. Frontiers in Physiology 2020, 10, 1 .

AMA Style

Massimiliano Zanin, Bahar Güntekin, Tuba Aktürk, Lütfü Hanoğlu, David Papo. Time Irreversibility of Resting-State Activity in the Healthy Brain and Pathology. Frontiers in Physiology. 2020; 10 ():1.

Chicago/Turabian Style

Massimiliano Zanin; Bahar Güntekin; Tuba Aktürk; Lütfü Hanoğlu; David Papo. 2020. "Time Irreversibility of Resting-State Activity in the Healthy Brain and Pathology." Frontiers in Physiology 10, no. : 1.