GAPORE: Boolean network inference using a genetic algorithm with novel polynomial representation and encoding scheme

Liu, Xiang; Wang, Yan; Shi, Ning; Ji, Zhicheng; He, Shan

doi:10.1016/j.knosys.2021.107277

Cited by 11 publications

(7 citation statements)

References 47 publications

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“…In order to better present the effect of the college English education model, this section also uses the original GA (genetic algorithm) [18,19] and BSA (backtracking search optimization algorithm) [20,21] foretell similar 15 groups of test set information in the sample information for comparative analysis. The evaluation outcomes of GA and BSA are displayed in Figures 4 and 5.…”

Section: Network Training and Experimental Analysismentioning

confidence: 99%

College English Audio-Visual-Oral Teaching Mode from the Perspective of Artificial Intelligence

Ling

2022

Advances in Multimedia

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The “New Era Artificial Brain Power Improvement Plan” issued by the State Council and the “Computer Reasoning Development Activity Plan for Colleges and Universities” issued by the Ministry of Education clearly pointed out that it is necessary to attach importance to the combination of modern education and artificial intelligence. As a global language, English is becoming more and more important in global trade. Under the premise of educational plan changes in the new environment, English teaching is gradually reflected in the cultivation of college students’ English application ability. This research studies the current college English audio-visual-oral teaching mode from the perspective of artificial intelligence and analyzes the current situation of traditional English teaching by explaining the meaning of artificial intelligence, which mainly includes four stages: literature research, model construction, empirical research, and result analysis. In light of BP neural network, the showing assessment model of school English audio-visual-oral is laid out, and the change technique of showing method of school English audio-visual-oral under AI is advanced. Experiments show that this model achieves better teaching quality than the models constructed by traditional GA algorithm and BSA algorithm and is more suitable for promotion in college English teaching.

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Section: Network Training and Experimental Analysismentioning

confidence: 99%

College English Audio-Visual-Oral Teaching Mode from the Perspective of Artificial Intelligence

Ling

2022

Advances in Multimedia

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“…Therefore, once A is constructed, detection and enumeration of singleton attractors become trivial [5] . Although most of existing STP-based methods need to handle

or larger size matrices and thus can only handle small-size BNs, some efforts have been done to address this complexity issue [89] , [90] .…”

Section: Practical Algorithmsmentioning

confidence: 99%

Attractor detection and enumeration algorithms for Boolean networks

Mori

Akutsu

2022

Computational and Structural Biotechnology Journal

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“…From the systems biology point of view, inferring GRN plays an extremely crucial role in revealing underlying regulatory mechanisms to uncover potential genes ( Akutsu et al 2000 ; Zhang et al 2012 ; Liu et al 2021 ). A vast number of network inference/reconstruction methods have been widely developed to infer GRN using transcriptomic profiles.…”

Section: Introductionmentioning

confidence: 99%

“…A vast number of network inference/reconstruction methods have been widely developed to infer GRN using transcriptomic profiles. To the best of our knowledge, the existing network inference/reconstruction methods can be categorized into the following groups according to their principles ( Liu 2015 ): regression-based method [multiple regression model ( Zhang et al 2010 ), SINCERITIES ( Papili Gao et al 2018 ), and GNIPLR ( Zhang et al 2021 )], tree-based method [GENIE3 ( Huynh-Thu et al 2010 )], stability selection method [TIGRESS ( Haury et al 2012 )], correlation-based method [ARACNE ( Margolin et al 2006 ) and CLR ( Faith et al 2007 )], knowledge-based method [RegNetwork ( Liu et al 2015 )], ordinary differential equation method [linear ODE ( Wu et al 2014 ), SCODE ( Matsumoto et al 2017 ), and GRISLI ( Aubin-Frankowski and Vert 2020 )], Bayesian-based method [SSMs ( Beal et al 2005 ), Vireo ( Huang et al 2019 ), and NAE ( Wang et al 2022 )], Boolean network (BN) model method [ATEN ( Shi et al 2020 ) and GAPORE ( Liu et al 2021 )], and deep learning model [DeepDRIM ( Chen et al 2021 ), dynDeepDRIM ( Xu et al 2022 ), and DeepSEM ( Shu et al 2021 )].…”

Section: Introductionmentioning

confidence: 99%

LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data

Sun

Chen

et al. 2023

Bioinformatics

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Motivation From a systematic perspective, it is crucial to infer and analyze gene regulatory network (GRN) from high-throughput single-cell RNA sequencing (scRNA-seq) data. However, most existing GRN inference methods mainly focus on the network topology, only few of them consider how to explicitly describe the updated logic rules of regulation in GRNs to obtain their dynamics. Moreover, some inference methods also fail to deal with the over-fitting problem caused by the noise in time series data. Results In this paper, we propose a novel embedded Boolean threshold network method called LogBTF, which effectively infers GRN by integrating regularized logistic regression and Boolean threshold function. First, the continuous gene expression values are converted into Boolean values and the elastic net regression model is adopted to fit the binarized time series data. Then, the estimated regression coefficients are applied to represent the unknown Boolean threshold function of the candidate Boolean threshold network as the dynamical equations. To overcome the multi-collinearity and over-fitting problems, a new and effective approach is designed to optimize the network topology by adding a perturbation design matrix to the input data and thereafter setting sufficiently small elements of the output coefficient vector to zeros. In addition, the cross-validation procedure is implemented into the Boolean threshold network model framework to strengthen the inference capability. Finally, extensive experiments on one simulated Boolean value dataset, dozens of simulation datasets and three real scRNA-seq datasets demonstrate that the LogBTF method can infer GRNs from time series data more accurately than some other alternative methods for GRN inference. Availability and implementation The source data and code are available at https://github.com/zpliulab/LogBTF. Supplementary information Supplementary data are available at Bioinformatics online.

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GAPORE: Boolean network inference using a genetic algorithm with novel polynomial representation and encoding scheme

Cited by 11 publications

References 47 publications

College English Audio-Visual-Oral Teaching Mode from the Perspective of Artificial Intelligence

College English Audio-Visual-Oral Teaching Mode from the Perspective of Artificial Intelligence

Attractor detection and enumeration algorithms for Boolean networks

LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data

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