Gene regulatory network inference: Data integration in dynamic models—A review

Cytoscape: A software Environment for integrated models of biomolecular interaction networks

Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888

The Elements of Statistical Learning

Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162

System Identification: Theory for the User

Lennart Ljung, A. (1987) · Cited by 17,690

Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans

Fire, A., Xu, S., Montgomery, M. K., Kostas, S. A., Driver, S. E., & Mello, C. C. (1998) · Nature · doi:10.1038/35888 · Cited by 15,429

System identification—Theory for the user

Sinha, N. K. (1989) · Automatica · doi:10.1016/0005-1098(89)90019-8 · Cited by 9,310

The Large-Scale Organization of Metabolic Networks

Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35036627 · Cited by 4,972

Metabolic stability and epigenesis in randomly constructed genetic nets

Kauffman, S. A. (1969) · Journal of Theoretical Biology · doi:10.1016/0022-5193(69)90015-0 · Cited by 4,871

Using Bayesian Networks to Analyze Expression Data

Friedman, N., Michal Linial, B., Nachman, I., & Pe’er, D. (2000) · Journal of Computational Biology · doi:10.1089/106652700750050961 · Cited by 3,248

Network motifs in the transcriptional regulation network of Escherichia coli

Shen-Orr, S. S., Milo, R., Mangan, S., & Alon, U. S. (2002) · Nature Genetics · doi:10.1038/ng881 · Cited by 2,970

Transcriptional Regulatory Networks in Saccharomyces cerevisiae

Lee, T. I., Rinaldi, N. J., Robert, F., Odom, D. T., Bar-Joseph, Z., Gerber, G. K., Hannett, N. M., Harbison, C. T., Thompson, C. M., Simon, I., et al. (2002) · Science · doi:10.1126/science.1075090 · Cited by 2,956

A Novel Reverse Engineering Approach for Gene Regulatory Networks

Zito, F., Cutello, V., & Pavone, M. F. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-21127-0_26 · Cited by 8

Rangeland species potential mapping using machine learning algorithms

Sharifipour, B., Gholinejad, B., Shirzadi, A., Shahabi, H., Al-Ansari, N. A., Farajollahi, A., Mansorypour, F., & Clague, J. J. (2023) · Ecological Engineering · doi:10.1016/j.ecoleng.2023.106900 · Cited by 7

Optimizing Multi-variable Time Series Forecasting Using Metaheuristics

Zito, F., Cutello, V., & Pavone, M. F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26504-4_8 · Cited by 7

Modeling regulatory cascades using Artificial Neural Networks: the case of transcriptional regulatory networks shaped during the yeast stress response

Manioudaki, M., & Poirazi, P. (2013) · Frontiers in Genetics · doi:10.3389/fgene.2013.00110 · Cited by 7

Challenges in Understanding Genome-Wide DNA Methylation

Zhang, M. Q., & Smith, A. D. (2010) · Journal of Computer Science and Technology · doi:10.1007/s11390-010-9302-8 · Cited by 7

Cascade: a R package to study, predict and simulate the diffusion of a signal through a temporal gene network

Jung, N., Bertrand, F., Bahram, S., Vallat, L. D., & Maumy‐Bertrand, M. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt705 · Cited by 7

CyNetworkBMA: a Cytoscape app for inferring gene regulatory networks

Fronczuk, M., Raftery, A. E., & Yeung, K. Y. (2015) · Source Code for Biology and Medicine · doi:10.1186/s13029-015-0043-5 · Cited by 7

Inferring sparse networks for noisy transient processes

Tran, H. M., & Bukkapatnam, S. (2016) · Scientific Reports · doi:10.1038/srep21963 · Cited by 7

Integrating Microarray Data and GRNs

Koumakis, L., Potamias, G., Tsiknakis, M., Zervakis, M. E., & Moustakis, V. S. (2015) · Methods in molecular biology · doi:10.1007/7651_2015_252 · Cited by 7

Backward-stochastic-differential-equation approach to modeling of gene expression

Shamarova, E., Chertovskih, R., Ramos, A. F., & Aguiar, P. (2017) · Physical review. E · doi:10.1103/physreve.95.032418 · Cited by 9

Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to psychiatric disorders

Kim, D., Kang, M., Biswas, A. K., Liu, C., & Gao, J. (2016) · BMC Medical Genomics · doi:10.1186/s12920-016-0202-9 · Cited by 8

Reverse Engineering of Gene Regulatory Networks Combining Dynamic Bayesian Networks and Prior Biological Knowledge

de Souza, M. C., & Higa, C. H. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-95162-1_22 · Cited by 7

ARMADA: Using motif activity dynamics to infer gene regulatory networks from gene expression data

Pemberton-Ross, P. J., Pachkov, M. V., & van Nimwegen, E. (2015) · Methods · doi:10.1016/j.ymeth.2015.06.024 · Cited by 6

A multi-view genomic data simulator

Fratello, M., Serra, A., Fortino, V., Raiconi, G., Tagliaferri, R., & Greco, D. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0577-1 · Cited by 7

Coordinated control of neuronal differentiation and wiring by a sustained code of transcription factorsPreprint

Özel, M. N., Gibbs, C. S., Holguera, I., Soliman, M., Bonneau, R., & Desplan, C. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.01.490216 · Cited by 7

Evaluating the Utilities of Large Language Models in Single-cell Data AnalysisPreprint

Zhao, H., Liu, T., Li, K., Wang, Y., & Li, H. (2023) · Research Square · doi:10.21203/rs.3.rs-3376641/v1 · Cited by 7

Emergent correlations in gene expression dynamics as footprints of resource competition

Chakraborty, P., & Ghosh, S. (2021) · The European Physical Journal E · doi:10.1140/epje/s10189-021-00122-w · Cited by 6

Inferring gene regulatory networks using transcriptional profiles as dynamical attractors

Li, R., Rozum, J. C., Quail, M. M., Qasim, M. N., Sindi, S., Nobile, C. J., Albert, R., & Hernday, A. D. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010991 · Cited by 7

Recurrences Reveal Shared Causal Drivers of Complex Time Series

Gilpin, W. (2025) · Physical Review X · doi:10.1103/physrevx.15.011005 · Cited by 7

Analysis of Gene Interactions Using Restricted Boolean Networks and Time-Series Data

Higa, C. H. A., Louzada, V. H. P., & Hashimoto, R. F. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13078-6_9 · Cited by 6

Inferring gene correlation networks from transcription factor binding sites

Mahdevar, G., Nowzari-Dalini, A., & Sadeghi, M. (2013) · Genes & Genetic Systems · doi:10.1266/ggs.88.301 · Cited by 6

Correlation Imputation for Single-Cell RNA-seq

Gan, L., Vinci, G., & Allen, G. I. (2022) · Journal of Computational Biology · doi:10.1089/cmb.2021.0403 · Cited by 6

Generating Stochastic Gene Regulatory Networks Consistent With Pathway Information and Steady-State Behavior

Knight, J. M., Datta, A. A., & Dougherty, E. R. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2192117 · Cited by 6

Proteomics of a plant pathogen: Agrobacterium tumefaciens

Rosen, R., & Ron, E. Z. (2011) · PROTEOMICS · doi:10.1002/pmic.201100019 · Cited by 6

Developing computational model-based diagnostics to analyse clinical chemistry data

van Schalkwijk, D. B., van Bochove, K., van Ommen, B., Freidig, A. P., van Someren, E. P., van der Greef, J., & de Graaf, A. A. (2010) · Briefings in Bioinformatics · doi:10.1093/bib/bbp071 · Cited by 8

ConReg: Analysis and Visualization of Conserved Regulatory Networks in Eukaryotes

Pesch, R., Böck, M., & Zimmer, R. (2012) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/oasics.gcb.2012.69 · Cited by 6

Data Integration in Functional Analysis of MicroRNAs

Oğul, H., & Akkaya, M. S. (2011) · Current Bioinformatics · doi:10.2174/157489311798072945 · Cited by 6

Boolean modeling techniques for protein co-expression networks in systems medicine

Mayer, G., Marcus, K., Eisenacher, M., & Kohl, M. (2016) · Expert Review of Proteomics · doi:10.1080/14789450.2016.1181546 · Cited by 6

Crosstalk pathway inference using topological information and biclustering of gene expression data

Dussaut, J. S., Gallo, C. A., Cecchini, R. L., Carballido, J. A., & Ponzoni, I. (2016) · Biosystems · doi:10.1016/j.biosystems.2016.08.002 · Cited by 6

SiGNet: A signaling network data simulator to enable signaling network inference

Coker, E. A., Mitsopoulos, C., Workman, P., & Al‐Lazikani, B. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177701 · Cited by 6

The feasibility of genome-scale biological network inference using Graphics Processing Units

Thiagarajan, R., Alavi, A., Podichetty, J. T., Bazil, J. N., & Beard, D. A. (2017) · Algorithms for Molecular Biology · doi:10.1186/s13015-017-0100-5 · Cited by 6

Detection of network motifs using three-way ANOVA

Tavakkolkhah, P., Zimmer, R., & Küffner, R. (2018) · PLoS ONE · doi:10.1371/journal.pone.0201382 · Cited by 6

The SQUAD Method for the Qualitative Modeling of Regulatory Networks

Méndez, A., Ramírez, C., Martínez, M. P., & Mendoza, L. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8618-7_9 · Cited by 6

Computational Inference of Gene Co-Expression Networks for the identification of Lung Carcinoma Biomarkers: An Ensemble Approach

Delgado-Chaves, F. M., Gómez-Vela, F., García-Torres, M., Divina, F., & Noguera, J. L. V. (2019) · Genes · doi:10.3390/genes10120962 · Cited by 6

A control theoretic three timescale model for analyzing energy management in mammalian cancer cells

Dasgupta, A., Bakshi, A., Chowdhury, N., & De, R. K. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.12.019 · Cited by 7

Mathematical Modelling of Leukemia Treatment

Samuilik, I., & Sadyrbaev, F. (2021) · WSEAS Transactions on Computers archive · doi:10.37394/23205.2021.20.30 · Cited by 6

Combining kinetic orders for efficient S-System modelling of gene regulatory network

Gill, J. K., Chetty, M., Shatte, A. B. R., & Hallinan, J. S. (2022) · Biosystems · doi:10.1016/j.biosystems.2022.104736 · Cited by 6

Pattern Recognition in Biological Time Series

Gómez-Vela, F., Alvarez, F. M., Barranco, C. D., Díaz–Díaz, N., Rodríguez–Baena, D. S., & Aguilar–Ruiz, J. S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-25274-7_17 · Cited by 6

Reconstructing Genetic Regulatory Networks Using Two-Step Algorithms with the Differential Equation Models of Neural Networks

Chen, C.-K. (2017) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-017-0254-3 · Cited by 6

On dynamic network modeling of stationary multivariate processes

Bottegal, G., Chiuso, A., & den Hof, P. M. J. V. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.09.118 · Cited by 6

Gene Regulatory Network Inference

Babtie, A. C., Stumpf, M. P. H., & Thorne, T. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.11346-7 · Cited by 9

Reconstructing irreducible links in temporal networks: which tool to choose depends on the network size

Nadini, M., Rizzo, A., & Porfiri, M. (2020) · Journal of Physics Complexity · doi:10.1088/2632-072x/ab6727 · Cited by 6

High-Dimensional Ordinary Differential Equation Models for Reconstructing Genome-Wide Dynamic Regulatory Networks

Wu, S., Liu, Z., Qiu, X., & Wu, H. (2013) · Springer proceedings in mathematics & statistics · doi:10.1007/978-1-4614-7846-1_15 · Cited by 6

Redes complexas de expressão gênica: síntese, identificação, análise e aplicações

Lopes, F. M. (2011) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.95.2011.tde-27072011-105810 · Cited by 6

Biomarkers for Environmental Exposure

Gallagher, J. E., Hubal, E. A. C., & Edwards, S. W. (2010) · Biomarkers · doi:10.1002/9780470918562.ch20 · Cited by 6

Continuous lifelong learning for modeling of gene regulation from single cell multiome data by leveraging atlas-scale external dataPreprint

Yuan, Q., & Duren, Z. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.01.551575 · Cited by 6

GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation ModelPreprint

Zhang, M., Swamy, V. S., Cassius, R., Dupire, L., Kanatsoulis, C. I., Paull, E., AlQuraishi, M., Karaletsos, T., & Califano, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.03.663009 · Cited by 6

Learning of Tree-Structured Gaussian Graphical Models on Distributed Data Under Communication Constraints

Tavassolipour, M., Motahari, S. A., & Manzuri, M. T. (2018) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2876325 · Cited by 5

Interactive exploration of integrated biological datasets using context-sensitive workflows

Horn, F., Rittweger, M., Taubert, J., Lysenko, A., Rawlings, C., & Guthke, R. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00021 · Cited by 5

Gene Networks Inference through One Genetic Algorithm Per Gene

Jimínez, R. D., Martins, D., & Santos, C. S. (2014) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2014.9 · Cited by 5

Model Discrimination in Dynamic Molecular Systems: Application to Parotid De-differentiation Network

Kim, J., Li, J., Venkatesh, S. G., Darling, D. S., & Rempała, G. A. (2013) · Journal of Computational Biology · doi:10.1089/cmb.2011.0222 · Cited by 5

Using Effective Subnetworks to Predict Selected Properties of Gene Networks

Gunaratne, G. H., Gunaratne, P. H., Seemann, L., & Török, A. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013080 · Cited by 5

A pattern-oriented specification of gene network inference processes

Trepode, N. W., de Farias, C. R. G., & Barrera, J. (2013) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2013.07.008 · Cited by 5

GRN2SBML: automated encoding and annotation of inferred gene regulatory networks complying with SBML

Vlaic, S., Hoffmann, B., Kupfer, P., Weber, M., & Dräger, A. (2013) · Bioinform. · doi:10.1093/bioinformatics/btt370 · Cited by 4

Overview of Computational Approaches for Inference of MicroRNA-Mediated and Gene Regulatory Networks

Ristevski, B. (2015) · Advances in computers · doi:10.1016/bs.adcom.2014.12.001 · Cited by 5

Faster MCMC for Gaussian latent position network models

Spencer, N. A., Junker, B. W., & Sweet, T. M. (2022) · Network Science · doi:10.1017/nws.2022.1 · Cited by 5

The finding and researching algorithm for potentially oscillating enzymatic systems

Lakhova, T. N., Владимирович, К. Ф., Lashin, S. A., & Matushkin, Y. G. (2021) · Vavilov Journal of Genetics and Breeding · doi:10.18699/vj21.035 · Cited by 5

Fuzzy and Rough Set Theory Based Computational Framework for Mining Genetic Interaction Triplets From Gene Expression Profiles for Lung Adenocarcinoma

Majumder, S., Yogita, Y., Pal, V., & Singh, K. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3120844 · Cited by 5

A system of recurrent neural networks for modularising, parameterising and dynamic analysis of cell signalling networks

Samarasinghe, S., & Ling, H. (2017) · Biosystems · doi:10.1016/j.biosystems.2017.01.001 · Cited by 6

A Knowledge Based Decision Support System for Bioinformatics and System Biology

Fiannaca, A., Gaglio, S., Rosa, M. L., Peri, D., Rizzo, R., & Urso, A. M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21946-7_17 · Cited by 5

Learning sparse Fuzzy Cognitive Maps by Ant Colony Optimization

Ye, N., Gao, M. Z., Zhang, R., Wang, D., He, X., Lu, J., Wu, Z., & Zheng, Q. (2013) · International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) · doi:10.1109/fskd.2013.6816169 · Cited by 5

Gene Regulatory Network Inference from Perturbed Time-Series Expression Data via Ordered Dynamical Expansion of Non-Steady State Actors

Zamanighomi, M., Zamanian, M., Kimber, M. J., & Wang, Z. (2015) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2015.2509992 · Cited by 5

Optimized time-lag differential method for constructing gene regulatory network

Paul, A., & Sil, J. (2018) · Information Sciences · doi:10.1016/j.ins.2018.11.019 · Cited by 5

False Discovery Rates in Biological NetworksPreprint

Yu, L., Kaufmann, T., & Lederer, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.03808 · Cited by 5

CyEnGNet—App: A new Cytoscape app for the reconstruction of large co-expression networks using an ensemble approach

Saz-Navarro, D. M., López, A. L., Gómez-Vela, F., & Rodríguez–Baena, D. S. (2024) · SoftwareX · doi:10.1016/j.softx.2024.101634 · Cited by 5

Recon8D: A metabolic regulome network from oct-omics and machine learningPreprint

Schildcrout, R., Smith, K., Bhowmick, R., Lu, Y., Menon, S., Kapadia, M., Kurtz, E., Coffeen-Vandeven, A., Nelakuditi, S., & Chandrasekaran, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.17.608400 · Cited by 5

Effects of cDNA microarray time-series data size on gene regulatory network inference accuracy

Chaitankar, V., Ghosh, P., Zhang, C., Gong, P., & Perkins, E. J. (2010) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/1854776.1854842 · Cited by 4

Predictive Minimum Description Length Principle Approach to Inferring Gene Regulatory Networks

Chaitankar, V., Zhang, C., Ghosh, P., Gong, P., Perkins, E. J., & Deng, Y. (2011) · Advances in experimental medicine and biology · doi:10.1007/978-1-4419-7046-6_4 · Cited by 4

Gene regulatory network inference by point-based Gaussian approximation filters incorporating the prior information

Jia, B., & Wang, X. (2013) · EURASIP Journal on Bioinformatics and Systems Biology · doi:10.1186/1687-4153-2013-16 · Cited by 4

Gene networks in cancer are biased by aneuploidies and sample impurities

Schubert, M., Colomé‐Tatché, M., & Foijer, F. (2019) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2019.194444 · Cited by 4

Inferring a Gene Regulatory Network from Gene Expression Data. An Overview of Best Methods and a Reverse Engineering Approach

Cutello, V., Pavone, M. F., & Zito, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-55248-9_9 · Cited by 6

Inference of Differential Equation Models by Multi Expression Programming for Gene Regulatory Networks

Yang, B., Chen, Y., & Meng, Q. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04020-7_105 · Cited by 4

Improvement of reliabilities of regulations using a hierarchical structure in a genetic network

Kimura, S., & Okada, M. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280362 · Cited by 4

Gene Networks Inference through Linear Grouping of Variables

Cubas, C. F. M., Martins, D., Santos, C. S., & Barrera, J. (2014) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2014.10 · Cited by 4

Evaluating the performance of partitioning techniques for gene network inference

Zainudin, S., & Mohamed, N. S. (2010) · International Conference on Intelligent Systems Design and Applications (ISDA) · doi:10.1109/isda.2010.5687035 · Cited by 4

Inference of gene regulatory networks via multiple data sources and a recommendation method

Özsoy, M. G., Polat, F., & Alhajj, R. (2015) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2015.7359764 · Cited by 4

A multiscale model of intestinal crypts dynamics

Graudenzi, A., Caravagna, G., Matteis, G. D., Mauri, G., & Antoniotti, M. (2012) · ArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/) · Cited by 4

Array-based approaches to bacterial transcriptome analysis

Mäder, U., & Nicolas, P. (2012) · Methods in microbiology · doi:10.1016/b978-0-08-099387-4.00006-5 · Cited by 4

Systems Biology of Infectious Diseases and Vaccines

Nakaya, H. I. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-405926-9.00015-0 · Cited by 4

Inference of genetic networks from time-series of gene expression levels using random forests

Kimura, S., Tokuhisa, M., & Okada, M. (2017) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2017.8058522 · Cited by 4

Emerging Machine Learning Techniques for Modelling Cellular Complex Systems in Alzheimer’s Disease

Vrahatis, A. G., Vlamos, P., Avramouli, A., Exarchos, T., & Gonidi, M. (2021) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-78775-2_24 · Cited by 4

Inference of Genetic Networks using Random Forests: A Quantitative Weighting Method for Gene Expression Data

Kimura, S., Sota, K., & Tokuhisa, M. (2022) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb55180.2022.9863035 · Cited by 4

Molecular Mechanisms Regulating the Oil Biosynthesis in Olive (Olea europaea L.) Fruits Revealed by Transcriptomic Analysis

Qu, J., Chen, Z., Wang, B., Feng, S., Tong, Z., Chen, T., Zhou, L., Peng, Z., & Ding, C. (2022) · Agronomy · doi:10.3390/agronomy12112718 · Cited by 4

One genetic algorithm per gene to infer gene networks from expression data

Jimínez, R. D., Martins, D., & Santos, C. S. (2015) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-015-0092-3 · Cited by 4

Reverse Engineering Boolean Networks: From Bernoulli Mixture Models to Rule Based Systems

Saeed, M., Ijaz, M., Javed, K., & Babri, H. A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0051006 · Cited by 4

Supervised method for construction of microRNA-mRNA networks: Application in cardiac tissue aging dataset

Dimitrakopoulos, G. Ν., Dimitrakopoulou, K., Maraziotis, I. A., Sgarbas, K., & Bezerianos, A. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6943593 · Cited by 4

Diversity of temporal correlations between genes in models of noisy and noiseless gene networks

Ribeiro, A. S., Lloyd‐Price, J., Chowdhury, S., & Yli-Harja, O. P. (2011) · Biosystems · doi:10.1016/j.biosystems.2011.02.003 · Cited by 4

Exact and Heuristic Methods for Network Completion for Time-Varying Genetic Networks

Nakajima, N., & Akutsu, T. (2014) · BioMed Research International · doi:10.1155/2014/684014 · Cited by 4

Systems approaches in understanding evolution and evolvability

Agarwal, S. (2013) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2013.09.004 · Cited by 4

From Genes to Networks: Characterizing Gene-Regulatory Interactions in Plants

Kaufmann, K., & Chen, D. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7125-1_1 · Cited by 4

Inference of gene networks from gene expression time series using recurrent neural networks and sparse MAP estimation

Chen, C.-K. (2018) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720018500099 · Cited by 4

Network using Michaelis–Menten kinetics: constructing an algorithm to find target genes from expression data

Krishnan, M., SMALL, M. P., Bosco, A., & Stemler, T. (2019) · Journal of Complex Networks · doi:10.1093/comnet/cnz016 · Cited by 4

Network Inference and Reconstruction in Bioinformatics

Petti, M., Punzi, C., Alfano, C., Farina, L., Astolfi, L., Paci, P., Guzzi, P. H., Castiglione, F., & Tieri, P. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00028-2 · Cited by 3

A novel tool for multi-omics network integration and visualization: A study of glioma heterogeneity

Coletti, R., Carrilho, J. F., Martins, E. P., Gonçalves, C. S., Costa, B. M., & Lopes, M. B. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109811 · Cited by 3

LogicSR: prior-guided symbolic regression for gene regulatory network inference from single-cell transcriptomics data

Zhang, D., Liu, Z., & Gao, R. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf621 · Cited by 3

SPSID: A Single-Parameter Shrinkage Inverse-Diffusion for Denoising Gene-Regulatory Networks

Chen, H., Han, G., Ding, W., & Grazian, C. (2026) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2026.3674779 · Cited by 4

Placental Gene Co-expression Network for Maternal Plasma Lipids Revealed Enrichment of Inflammatory Response Pathways

Ouidir, M., Chatterjee, S., Mendola, P., Zhang, C., Grantz, K. L., & Ayele, F. T. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.681095 · Cited by 3

Inference of genetic networks using random forests:Performance improvement using a new variable importance measure

Kimura, S., Takeda, Y., Tokuhisa, M., & Okada, A. M. (2022) · Chem-Bio Informatics Journal · doi:10.1273/cbij.22.88 · Cited by 3

Oscillations in delayed positive feedback systems

Ryzowicz, C. J., Bertram, R., & Karamched, B. R. (2024) · Physical Chemistry Chemical Physics · doi:10.1039/d4cp01867b · Cited by 4

Incremental data modeling based on neural ordinary differential equations

Chen, Z., Bian, H., & Zhu, W. Q. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-01793-0 · Cited by 3

Reverse engineering of gene regulatory networks

Cho, K.-H., Choo, S., Jung, S. H., Kim, J., Choi, H. S., & Kim, J. (2007) · IET Systems Biology · doi:10.1049/iet-syb:20060075 · Cited by 120

Reverse engineering of gene regulatory networks from biological data

Liu, L., Wu, F., & Zhang, W. (2012) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1068 · Cited by 14

Computational methods for Gene Regulatory Networks reconstruction and analysis: A review

Delgado-Chaves, F. M., & Gómez-Vela, F. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.10.006 · Cited by 213

iLSGRN: inference of large-scale gene regulatory networks based on multi-model fusion

Wu, Y., Qian, B., Wang, A., Dong, H., Zhu, E., & Ma, B. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad619 · Cited by 5

Integrating heterogeneous gene expression data for gene regulatory network modelling

Sîrbu, A., Ruskin, H. J., & Crane, M. (2011) · Theory in Biosciences · doi:10.1007/s12064-011-0133-0 · Cited by 8