TY - JOUR A1 - Novine, Masiar A1 - Mattsson, Cecilie Cordua A1 - Groth, Detlef T1 - Network reconstruction based on synthetic data generated by a Monte Carlo approach JF - Human biology and public health N2 - Background: Network models are useful tools for researchers to simplify and understand investigated systems. Yet, the assessment of methods for network construction is often uncertain. Random resampling simulations can aid to assess methods, provided synthetic data exists for reliable network construction. Objectives: We implemented a new Monte Carlo algorithm to create simulated data for network reconstruction, tested the influence of adjusted parameters and used simulations to select a method for network model estimation based on real-world data. We hypothesized, that reconstructs based on Monte Carlo data are scored at least as good compared to a benchmark. Methods: Simulated data was generated in R using the Monte Carlo algorithm of the mcgraph package. Benchmark data was created by the huge package. Networks were reconstructed using six estimator functions and scored by four classification metrics. For compatibility tests of mean score differences, Welch’s t-test was used. Network model estimation based on real-world data was done by stepwise selection. Samples: Simulated data was generated based on 640 input graphs of various types and sizes. The real-world dataset consisted of 67 medieval skeletons of females and males from the region of Refshale (Lolland) and Nordby (Jutland) in Denmark. Results: Results after t-tests and determining confidence intervals (CI95%) show, that evaluation scores for network reconstructs based on the mcgraph package were at least as good compared to the benchmark huge. The results even indicate slightly better scores on average for the mcgraph package. Conclusion: The results confirmed our objective and suggested that Monte Carlo data can keep up with the benchmark in the applied test framework. The algorithm offers the feature to use (weighted) un- and directed graphs and might be useful for assessing methods for network construction. KW - Monte Carlo method KW - network reconstruction KW - mcgraph KW - random sampling KW - linear enamel hypoplasia Y1 - 2022 U6 - https://doi.org/10.52905/hbph2021.3.26 SN - 2748-9957 VL - 2021 IS - 3, Summer School Supplement PB - Universitätsverlag Potsdam CY - Potsdam ER - TY - JOUR A1 - Kralemann, Bjoern A1 - Pikovskij, Arkadij A1 - Rosenblum, Michael T1 - Reconstructing effective phase connectivity of oscillator networks from observations JF - New journal of physics : the open-access journal for physics N2 - We present a novel approach for recovery of the directional connectivity of a small oscillator network by means of the phase dynamics reconstruction from multivariate time series data. The main idea is to use a triplet analysis instead of the traditional pairwise one. Our technique reveals an effective phase connectivity which is generally not equivalent to a structural one. We demonstrate that by comparing the coupling functions from all possible triplets of oscillators, we are able to achieve in the reconstruction a good separation between existing and non-existing connections, and thus reliably reproduce the network structure. KW - network reconstruction KW - coupled oscillators KW - connectivity KW - data analysis Y1 - 2014 U6 - https://doi.org/10.1088/1367-2630/16/8/085013 SN - 1367-2630 VL - 16 PB - IOP Publ. Ltd. CY - Bristol ER - TY - JOUR A1 - Hake, Tim A1 - Bodenberger, Bernhard A1 - Groth, Detlef T1 - In Python available: St. Nicolas House Algorithm (SNHA) with bootstrap support for improved performance in dense networks JF - Human biology and public health N2 - The St. Nicolas House Algorithm (SNHA) finds association chains of direct dependent variables in a data set. The dependency is based on the correlation coefficient, which is visualized as an undirected graph. The network prediction is improved by a bootstrap routine. It enables the computation of the empirical p-value, which is used to evaluate the significance of the predicted edges. Synthetic data generated with the Monte Carlo method were used to firstly compare the Python package with the original R package, and secondly to evaluate the predicted network using the sensitivity, specificity, balanced classification rate and the Matthew's correlation coefficient (MCC). The Python implementation yields the same results as the R package. Hence, the algorithm was correctly ported into Python. The SNHA scores high specificity values for all tested graphs. For graphs with high edge densities, the other evaluation metrics decrease due to lower sensitivity, which could be partially improved by using bootstrap,while for graphs with low edge densities the algorithm achieves high evaluation scores. The empirical p-values indicated that the predicted edges indeed are significant. KW - Python KW - correlation KW - network reconstruction KW - bootstrap KW - St. Nicolas House Algorithm Y1 - 2023 U6 - https://doi.org/10.52905/hbph2023.1.63 SN - 2748-9957 VL - 1 PB - Universitätsverlag Potsdam CY - Potsdam ER - TY - THES A1 - Cecchini, Gloria T1 - Improving network inference by overcoming statistical limitations T1 - Verbesserung der Netzwerkrekonstruktion durch überwinden statistischer Limits N2 - A reliable inference of networks from data is of key interest in many scientific fields. Several methods have been suggested in the literature to reliably determine links in a network. These techniques rely on statistical methods, typically controlling the number of false positive links, but not considering false negative links. In this thesis new methodologies to improve network inference are suggested. Initial analyses demonstrate the impact of falsepositive and false negative conclusions about the presence or absence of links on the resulting inferred network. Consequently, revealing the importance of making well-considered choices leads to suggest new approaches to enhance existing network reconstruction methods. A simulation study, presented in Chapter 3, shows that different values to balance false positive and false negative conclusions about links should be used in order to reliably estimate network characteristics. The existence of type I and type II errors in the reconstructed network, also called biased network, is accepted. Consequently, an analytic method that describes the influence of these two errors on the network structure is explored. As a result of this analysis, an analytic formula of the density of the biased vertex degree distribution is found (Chapter 4). In the inverse problem, the vertex degree distribution of the true underlying network is analytically reconstructed, assuming the probabilities of type I and type II errors. Chapters 4-5 show that the method is robust to incorrect estimates of α and β within reasonable limits. In Chapter 6, an iterative procedure to enhance this method is presented in the case of large errors on the estimates of α and β. The investigations presented so far focus on the influence of false positive and false negative links on the network characteristics. In Chapter 7, the analysis is reversed - the study focuses on the influence of network characteristics on the probability of type I and type II errors, in the case of networks of coupled oscillators. The probabilities of α and β are influenced by the shortest path length and the detour degree, respectively. These results have been used to improve the network reconstruction, when the true underlying network is not known a priori, introducing a novel and advanced concept of threshold. N2 - Eine zuverlässige Rekonstruktion eines Netzwerks aus Daten ist von entscheidender Bedeutung in der Wissenschaft. Einige Methoden werden in der Literatur vorgeschlagen um Verbindungen in einem Netzwerk akkurat zu bestimmen. Diese Methoden vertrauen auf die Anwendung der Statistik, indem sie falsch positive Verbindungen berücksichtigen, allerdings positiv falsche Verbindungen ignoriert. In dieser Arbeit werden neue Methoden vorgeschlagen, um die Rekonstruktion zu verbessern. Erste Analysen veranschaulichen den Einfluss falsch positiver und positiv falscher Verbindungen auf das resultierende Netzwerk. Daraus wird die Bedeutsamkeit ersichtlich, die eine gut gewählte Entscheidung hinsichtlich der Faktoren auf die Qualität der Rekonstruktion hat, wodurch sich neu Methoden ableiten lassen. Eine Simulation, welche in Kapitel 3 zu finden ist, veranschaulicht, dass verschiedene Werte für falsch postive und positiv falsche Verbindungen zu verwenden sind, um genaue Vorhersagen bezüglich des Netzwerkverhaltens zu treffen. Die Existenz Fehler erster und zweiter Art in der Rekonstruktion sind unvermeidbar und werden akzeptiert. Ein analytischer Ansatz, der den Einfluss dieser beiden Fehler beschreibt wird gesucht. Aus dieser Analyse in Kapitel 4 folgt eine Formel welche die Verteilung der Quantität der Knotenverbindungen beschreibt. Bei dem inversen Problem wird die Knotengrad-Verteilung des originalen Netzwerkes, mit Berücksichtigung der Wahrscheinlichkeit von Fehlern erster und zweiter Art, analytisch berechnet. Kapitel 4-5 zeigen, dass die Methode auch bei einigermaßen falschen Schätzungen von α und β Resultate innerhalb vertretbarer Grenzen liefert. In Kapitel 6 wird ein iteratives Verfahren vorgestellt, welches diese Methode bei außerordentlich falschen Schätzungen von α und β verbessert. Die bis jetzt vorgestellte Recherche fokussiert sich auf den Einfluss falsch positiver und positiv falscher Verbindungen auf die Netzwerkscharakteristik. Im Kapitel 7 wird der Prozess umgedreht. Die Arbeit fokussiert sich auf den Einfluss auf die Netzwerkkarakteristik durch die Fehler erster und zweiter Art im Falle eines Netzwerkes mit gekoppelten Oszillatoren. Die Wahrscheinlichkeit von α und β wird beeinflusst durch den kürzesten Verbindungsweg und dem Detour Grad. Diese Ergebnisse wurden genutzt um die Netzwerk Rekonstruktion zu verbessern, wenn das originale Netzwerk nicht zuvor bekannt war. Dies beschreibt einen neuen fortgeschritten Weg der Grenzwertbestimmung. KW - network inference KW - network reconstruction KW - statistical methods KW - Netzwerk Inferenz KW - Netzwerk Rekonstruktion KW - statistische Methoden Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-426705 ER -