Data-driven learning of Boolean networks and functions by optimal causation entropy principle

Sun, Jie; AlMomani, Abd AlRahman R.; Bollt, Erik M.

doi:10.1016/j.patter.2022.100631

Cited by 6 publications

(6 citation statements)

References 29 publications

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“…These two problems remain largely unsolved but have been studied with widely different approaches using tools from algebraic geometry, computational algebra, information theory, etc. 1 , 2 , 6 , 7 , 8 …”

Section: Main Textmentioning

confidence: 99%

“…For time courses with few time points, EQ for Boolean models such as the one introduced in this issue by Sun et al. 1 provides an attractive alternative.…”

Section: Main Textmentioning

confidence: 99%

“…In this issue, Sun et al. 1 present a method called Boolean optimal causation entropy (BoCSE). The BoCSE method is based on an optimization procedure of the mutual information between possible input and output vectors which they can solve efficiently.…”

Section: Main Textmentioning

confidence: 99%

“…Finally, we highlight the need of a user-friendly software that can take real data and output a model that can used to make predictions. Although some tools exist that address the inference process, 8 , 1 , 7 the code is either unavailable, not editable, or not in a ready-to-use format.…”

Section: Main Textmentioning

confidence: 99%

“…In this issue, Sun et al. 1 present a method for the inference of Boolean networks along with practical applications.…”

mentioning

confidence: 99%

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An information theoretic approach for the inference of Boolean networks and functions from data: BoCSE

Murrugarra¹,

Veliz-Cuba²

2022

Patterns

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Section: Main Textmentioning

confidence: 99%

“…For time courses with few time points, EQ for Boolean models such as the one introduced in this issue by Sun et al. 1 provides an attractive alternative.…”

Section: Main Textmentioning

confidence: 99%

Section: Main Textmentioning

confidence: 99%

Section: Main Textmentioning

confidence: 99%

“…In this issue, Sun et al. 1 present a method for the inference of Boolean networks along with practical applications.…”

mentioning

confidence: 99%

See 3 more Smart Citations

An information theoretic approach for the inference of Boolean networks and functions from data: BoCSE

Murrugarra¹,

Veliz-Cuba²

2022

Patterns

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Networks of Classical Conditioning Gates and Their Learning

Azuma,

Takakura,

Ariizumi

et al. 2024

New Gener. Comput.

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A research project on chemical AI, called the Molecular Cybernetics Project, was launched in Japan in 2021 with the goal of creating a molecular machine that can learn a type of conditioned reflex through the process of classical conditioning. In this project, we have developed a learning method for the network of such learning molecular machines, which is reported in this paper. First, as a model of a learning molecular machine, we formulate a logic gate that can learn conditioned reflex and introduce the network of the logic gates. Then we derive a key principle for learning, called the flipping principle, by which we present a learning algorithm for the network to realize a desired function.

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