Detecting causality from short time-series data based on prediction of topologically equivalent attractors

Zhang, Bengong; Li, Weibo; Shi, Yuexi; Li, Xiaoping; Chen, Luonan

doi:10.1186/s12918-017-0512-3

Cited by 8 publications

(3 citation statements)

References 29 publications

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“…Accurate estimation of connectivity measures requires sufficient sample sizes [ 193 ]. Guidelines for sufficient sample sizes have been presented for different scenarios [ 194 , 195 , 196 ], whereas solutions for different applications with small samples have been proposed [ 197 , 198 , 199 ].…”

Section: Limitations and Pitfalls Of Connectivity Measuresmentioning

confidence: 99%

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

Papana

2021

Entropy

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The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is examined based on symmetric measures, such as correlation. In the second case, a variable drives another one and they are connected with a causal relationship. Therefore, directed connections entail the determination of the interrelationships based on causality measures. The main open question that arises is the following: can symmetric correlation measures or directional causality measures be applied to infer the connectivity network of an examined system? Using simulations, we demonstrate the performance of different connectivity measures in case of contemporaneous or/and temporal dependencies. Results suggest the sensitivity of correlation measures when temporal dependencies exist in the data. On the other hand, causality measures do not spuriously indicate causal effects when data present only contemporaneous dependencies. Finally, the necessity of introducing effective instantaneous causality measures is highlighted since they are able to handle both contemporaneous and causal effects at the same time. Results based on instantaneous causality measures are promising; however, further investigation is required in order to achieve an overall satisfactory performance.

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Section: Limitations and Pitfalls Of Connectivity Measuresmentioning

confidence: 99%

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

Papana

2021

Entropy

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“…Accordingly, through Y's attractor manifold (a mathematical object that represents a geometric space in which the variable moves, usually denoted as M y ), it is possible to make local-neighborhood predictions on X's manifold M x . As local-neighborhood tests can be data demanding, several methods have been proposed to deal with short time series for noisy and high-dimensional systems (e.g., [56][57][58]).…”

Section: Physics-inspired Approachesmentioning

confidence: 99%

Estimating Networks of Sustainable Development Goals

2019

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An increasing number of researchers and practitioners advocate for a systemic understanding of the Sustainable Development Goals (SDGs) through interdependency networks. Ironically, the burgeoning network-estimation literature seems neglected by this community. We provide an introduction to the most suitable estimation methods for SDG networks. Building a dataset with 87 development indicators in four countries over 20 years, we perform a comparative study of these methods. We find important differences in the estimated network structures as well as in synergies and trade-offs between SDGs. Finally, we provide some guidelines on the potentials and limitations of estimating SDG networks for policy advice.

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“… Integration of gene regulatory network inference with constraint-based metabolic models to simulate growth phenotype and exchange fluxes [ 15 ]. Causal relationship detection between gene pairs for short time-series gene expression data ( E. coli ; yeast) based on lagged-coordinate delay embedding theorem [ 16 ]. Cell fate predictions derived from polynomial modeling of human pancreatic single-cell gene expression data [ 17 ].…”

Section: Manuscript Submission and Reviewmentioning

confidence: 99%

A bioinformatics potpourri

Schönbach

et al. 2018

BMC Genomics

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The 16th International Conference on Bioinformatics (InCoB) was held at Tsinghua University, Shenzhen from September 20 to 22, 2017. The annual conference of the Asia-Pacific Bioinformatics Network featured six keynotes, two invited talks, a panel discussion on big data driven bioinformatics and precision medicine, and 66 oral presentations of accepted research articles or posters. Fifty-seven articles comprising a topic assortment of algorithms, biomolecular networks, cancer and disease informatics, drug-target interactions and drug efficacy, gene regulation and expression, imaging, immunoinformatics, metagenomics, next generation sequencing for genomics and transcriptomics, ontologies, post-translational modification, and structural bioinformatics are the subject of this editorial for the InCoB2017 supplement issues in BMC Genomics, BMC Bioinformatics, BMC Systems Biology and BMC Medical Genomics. New Delhi will be the location of InCoB2018, scheduled for September 26–28, 2018.Electronic supplementary materialThe online version of this article (10.1186/s12864-017-4326-x) contains supplementary material, which is available to authorized users.

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Detecting causality from short time-series data based on prediction of topologically equivalent attractors

Cited by 8 publications

References 29 publications

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

Estimating Networks of Sustainable Development Goals

A bioinformatics potpourri

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