Generating global network structures by triad types

Cugmas, Marjan; Ferligoj, Anuška; Žiberna, Aleš

doi:10.1371/journal.pone.0197514

Cited by 8 publications

(3 citation statements)

References 59 publications

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“…The first digit represents the number of mutual links, the second digit represents the number of directed links and the third digit represents the number of missing links in a triad. If the arrangement of edges in different triads is the same, the additional characters separate them, where C stands for cycle, T for transitivity, U for up, and D for down [ 56 ].…”

Section: Methodsmentioning

confidence: 99%

SAILoR: Structure-Aware Inference of Logic Rules

Pušnik,

Mraz,

Zimic

et al. 2024

PLoS ONE

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Boolean networks provide an effective mechanism for describing interactions and dynamics of gene regulatory networks (GRNs). Deriving accurate Boolean descriptions of GRNs is a challenging task. The number of experiments is usually much smaller than the number of genes. In addition, binarization leads to a loss of information and inconsistencies arise in binarized time-series data. The inference of Boolean networks from binarized time-series data alone often leads to complex and overfitted models. To obtain relevant Boolean models of gene regulatory networks, inference methods could incorporate data from multiple sources and prior knowledge in terms of general network structure and/or exact interactions. We propose the Boolean network inference method SAILoR (Structure-Aware Inference of Logic Rules). SAILoR incorporates time-series gene expression data in combination with provided reference networks to infer accurate Boolean models. SAILoR automatically extracts topological properties from reference networks. These can describe a more general structure of the GRN or can be more precise and describe specific interactions. SAILoR infers a Boolean network by learning from both continuous and binarized time-series data. It navigates between two main objectives, topological similarity to reference networks and correspondence with gene expression data. By incorporating the NSGA-II multi-objective genetic algorithm, SAILoR relies on the wisdom of crowds. Our results indicate that SAILoR can infer accurate and biologically relevant Boolean descriptions of GRNs from both a static and a dynamic perspective. We show that SAILoR improves the static accuracy of the inferred network compared to the network inference method dynGENIE3. Furthermore, we compared the performance of SAILoR with other Boolean network inference approaches including Best-Fit, REVEAL, MIBNI, GABNI, ATEN, and LogBTF. We have shown that by incorporating prior knowledge about the overall network structure, SAILoR can improve the structural correctness of the inferred Boolean networks while maintaining dynamic accuracy. To demonstrate the applicability of SAILoR, we inferred context-specific Boolean subnetworks of female Drosophila melanogaster before and after mating.

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Section: Methodsmentioning

confidence: 99%

SAILoR: Structure-Aware Inference of Logic Rules

Pušnik,

Mraz,

Zimic

et al. 2024

PLoS ONE

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“…In ref. [41] it is stated that these rules can cover institutional, cultural or social identity norms.…”

Section: Model Analysismentioning

confidence: 99%

Analysing the Stock Market as an Economic Lever, Using a Qualitative and a Quantitative Model

et al. 2021

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The article aims to provide a perspective on economic growth by relying on the influence and use of the stock market as an economic lever. Two methods will be used: a quantitative one, determined by a multiple linear regression model, and a qualitative one that encumbers a sustainable vector model for generating economic growth. The data panel covers 36 states, for a period of 21 years. The paper manages to identify the main control functions that the stock exchange has over the macroeconomic context, through the quantitative and qualitative method, and to highlight the most important positive and negative attributes of using qualitative methods, in contrast to quantitative ones. The results show a predominant probabilistic characteristic of quantitative methods, in contrast to the flexibility and complexity of the qualitative method, which has been used. Additionally, the quantitative method offers a strictly cartesian perspective for determining future scenarios, while the sustainable vector model, based on a fractalized vision of reality, manages to capture a plurality of perspectives, as well as the interrelationships between the determining parameters, thus being a complex system of simple equations, as opposed to the quantitative method which is defined as a simple system of complex equations.

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“…triads is the same, the additional characters separate them, where C stands for cycle, T for transitivity, U for up, and D for down [56].…”

Section: Plos Onementioning

confidence: 99%

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Generating global network structures by triad types

Cited by 8 publications

References 59 publications

SAILoR: Structure-Aware Inference of Logic Rules

SAILoR: Structure-Aware Inference of Logic Rules

Analysing the Stock Market as an Economic Lever, Using a Qualitative and a Quantitative Model

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