Inferring gene networks from time series microarray data using dynamic Bayesian networks

Exploring the metabolic and genetic control of gene expression on a genomic scale.

Derisi, J., Iyer, V., & Brown, P. (1997) · Science · doi:10.1126/science.278.5338.680 · Cited by 4,832

Comprehensive Identification of Cell Cycle–regulated Genes of the YeastSaccharomyces cerevisiaeby Microarray Hybridization

Spellman, P. T., Sherlock, G. J., Zhang, M. Q., Iyer, V. R., Anders, K. R., Eisen, M. B., Brown, P. O., Botstein, D., & Futcher, B. (1998) · Molecular Biology of the Cell · doi:10.1091/mbc.9.12.3273 · Cited by 4,724

Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Heckerman, D. E., Geiger, D., & Chickering, D. M. (1995) · Machine Learning · doi:10.1023/a:1022623210503 · Cited by 3,251

Accurate Approximations for Posterior Moments and Marginal Densities

Tierney, L., & Kadane, J. B. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478240 · Cited by 2,136

MODELING GENE EXPRESSION WITH DIFFERENTIAL EQUATIONS

Chen, T., He, H., & Church, G. M. (1998) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814447300_0004 · Cited by 700

Learning the Structure of Dynamic Probabilistic NetworksPreprint

Friedman, N., Murphy, K., & Russell, S. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.7374 · Cited by 560

Inferring subnetworks from perturbed expression profiles

Pe’er, D., Regev, A., Elidan, G., & Friedman, N. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.suppl_1.s215 · Cited by 548

Inferring qualitative relations in genetic networks and metabolic pathways

Akutsu, T., Miyano, S., & Kuhara, S. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.8.727 · Cited by 381

ESTIMATION OF GENETIC NETWORKS AND FUNCTIONAL STRUCTURES BETWEEN GENES BY USING BAYESIAN NETWORKS AND NONPARAMETRIC REGRESSION

Imoto, S., Goto, T., & Miyano, S. (2001) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789812799623_0017 · Cited by 297

COMBINING LOCATION AND EXPRESSION DATA FOR PRINCIPLED DISCOVERY OF GENETIC REGULATORY NETWORK MODELS

Hartemink, A. J., Gifford, D. K., Jaakkola, T., & Young, R. A. (2001) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789812799623_0041 · Cited by 267

Parallel Exact Dynamic Bayesian Network Structure Learning with Application to Gene Networks

Md, V., & Aluru, S. (2017) · IEEE International Conference on High Performance Computing, Data, and Analytics · doi:10.1109/hipc.2017.00015 · Cited by 3

Time-Dependent Reliability Analysis of System Based on Dynamic Bayesian Fault Network

Feng, Y., Song, Z., Lu, C. J., & Yin, C. (2021) · International Conference on System Reliability and Safety Engineering (SRSE) · doi:10.1109/srse54209.2021.00061 · Cited by 3

Mutual Information Based on Multiple Level Discretization Network Inference from Time Series Gene Expression Profiles

Anh, C.-T., & Kwon, Y. (2023) · Applied Sciences · doi:10.3390/app132111902 · Cited by 3

Bayesian Networks in the Absence of Temporal Information

Nagarajan, R., Scutari, M., & Lèbre, S. (2013) · Springer eBooks · doi:10.1007/978-1-4614-6446-4_2 · Cited by 3

Informatics approaches for identifying biologic relationships in time‐series data

McKinney, B. A. (2008) · Wiley Interdisciplinary Reviews Nanomedicine and Nanobiotechnology · doi:10.1002/wnan.12 · Cited by 3

Enhancing Automatic Biological Pathway Generation with GO-Based Gene Similarity

Sanfilippo, A. P., Baddeley, B., Beagley, N., Riensche, R., & Gopalan, B. (2009) · International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing · doi:10.1109/ijcbs.2009.96 · Cited by 3

ProPhyC: A Probabilistic Phylogenetic Model for Refining Regulatory Networks

Zhang, X., & Moret, B. M. E. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21260-4_33 · Cited by 3

Independence screening for high dimensional nonlinear additive ODE models with applications to dynamic gene regulatory networks

Xue, H., Wu, S., Wu, Y., Ramírez, J. C., & Wu, H. (2018) · Statistics in Medicine · doi:10.1002/sim.7669 · Cited by 3

An Approximate Modelling Method for Industrial l-lysine Fermentation Process

Wang, H., Khan, F. I., Chen, B., & Lu, Z. (2015) · Computer-aided chemical engineering/Computer aided chemical engineering · doi:10.1016/b978-0-444-63578-5.50072-4 · Cited by 3

An improved method for inference of piecewise linear systems by detecting jumps using derivative estimation

Selçuk, A., & Öktem, H. (2009) · Nonlinear Analysis Hybrid Systems · doi:10.1016/j.nahs.2009.01.006 · Cited by 3

Data-driven modeling and prediction of microglial cell dynamics in the ischemic penumbra

Amato, S., & Arnold, A. (2025) · Mathematical Biosciences · doi:10.1016/j.mbs.2025.109549 · Cited by 2

Identification of Gene Networks with Time Delayed Regulation Based on Temporal Expression Profiles

Kim, J., Choo, S., Choi, H.-S., & Cho, K. (2015) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2015.2394312 · Cited by 2

EIEPCF: accurate inference of functional gene regulatory networks by eliminating indirect effects from confounding factors

Peng, H., Xu, J., Liu, K., Liu, F., Zhang, A., & Zhang, X. (2023) · Briefings in Functional Genomics · doi:10.1093/bfgp/elad040 · Cited by 2

Gene Networks: Estimation, Modeling, and Simulation

Imoto, S., Matsuno, H., & Miyano, S. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-405926-9.00006-x · Cited by 2

Modified variational method for genes regulatory network learning

Sánchez-Castillo, M., Tienda-Luna, I. M., Blanco, D., & Carrión-Pérez, M. C. (2010) · International Conference on Signal Processing · doi:10.1109/icosp.2010.5656711 · Cited by 2

A dynamic qualitative probabilistic network approach for extracting gene regulatory network motifs

Ibrahim, Z. M., Ngom, A., & Tawfik, A. Y. (2010) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2010.5706595 · Cited by 2

The Analysis of Arabidopsis thaliana Circadian Network Based on Non-stationary DBNs Approach with Flexible Time Lag Choosing Mechanism

Jia, Y., & Huan, J. (2009) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2009.81 · Cited by 2

Learning Gene Network Using Bayesian Network Framework

Tiefei, L. (2005) · Cited by 2

Necessary and Sufficient Informativity Conditions for Robust Network Reconstruction Using Dynamical Structure Functions

Chetty, V. (2012) · ScholarsArchive (Brigham Young University) · Cited by 2

Using Phylogenetic Relationships to Improve the Inference of Transcriptional Regulatory Networks

Zhang, X., Zaheri, M., & Moret, B. M. E. (2008) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2008.247 · Cited by 2

Switched Adaptive Observer for Structure Identification in Gene Regulatory Networks

Mohammadian, M., Momeni, H., Zahiri, J., & Karimi, H. S. (2020) · Iranian Conference on Electrical Engineering · doi:10.1109/icee50131.2020.9261072 · Cited by 2

Bayesian inference for protein signalling networks

Oates, C. J. (2026) · Warwick Research Archive Portal (University of Warwick) · doi:10.82444/warw.33634336 · Cited by 2

Unraveling the functional interaction structure of a biomolecular network through alternate perturbation of initial conditions

Cho, K., Choi, H.-S., & Choo, S. (2007) · Journal of Biochemical and Biophysical Methods · doi:10.1016/j.jbbm.2007.01.008 · Cited by 2

Bioinformatics

Akutsu, T. (2007) · Wiley eBooks · doi:10.1002/9780470050118.ecse042 · Cited by 2

Reconstruction of Gene Regulatory Networks using sparse graph recovery modelsPreprint

Shrivastava, H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.02.535294 · Cited by 2

Fundamental Boolean network modelling for childhood acute lymphoblastic leukaemia pathways

Chen, L., Kulasiri, D., & Samarasinghe, S. (2021) · Quantitative Biology · doi:10.15302/j-qb-021-0280 · Cited by 1

Risk and Reliability Assessment of Metal Lathe Machining Operation with DBN‐FFTA Hybrid Approach

Mandali, H., Ghasemi, F., Farshad, A. A., Hanifi, S. M., Abedi, K., Ghorbani, M., & Ebrahimi, H. (2023) · Mathematical Problems in Engineering · doi:10.1155/2023/8873531 · Cited by 1

Practical Approaches to Biological Network Discovery

Haynes, B. C. (2012) · Open Scholarship Institutional Repository (Washington University in St. Louis) · doi:10.7936/k72805pn · Cited by 1

Discovery: Computational Systems Biology (CSB) in Health and Disease I

Prokop, A., & Michelson, S. (2012) · SpringerBriefs in pharmaceutical science & drug development · doi:10.1007/978-94-007-2849-3_5 · Cited by 1

A consensus approach to predict regulatory interactions

Mohammed, S., Akman, O. E., & Yang, Z. R. (2014) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2014.7002876 · Cited by 1

Construction of Gene Regulatory Networks Based on Genetic Algorithm of Greedy Equivalence Search Mechanism

Qiang, B., & Wang, Z. (2009) · International Conference on Bioinformatics and Biomedical Engineering · doi:10.1109/icbbe.2009.5162554 · Cited by 1

Reverse engineering of the transcriptional subnetwork in the yeast cell cycle pathway using Dynamic Bayesian Networks and evolutionary search

Salehi, M., Young, P. G., & Mousavi, P. (2008) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2008.4675766 · Cited by 1

Reconstruction of gene regulatory networks by stepwise multiple linear regression from time-series microarray data

Zhou, Y., Qureshi, R., & Saçan, A. (2012) · International Symposium on Health Informatics and Bioinformatics (HIBIT) · doi:10.1109/hibit.2012.6209046 · Cited by 1

Biological interaction networks based on non-parametric estimation

Kalantzaki, K., Bei, E. S., Garofalakis, M., & Zervakis, M. (2013) · International Journal of Biomedical Engineering and Technology · doi:10.1504/ijbet.2013.058539 · Cited by 1

Weighted gene coexpression network analysis of prostate cancer

Ma, Y., Zhang, L., Geneus, C. J., & Gong, H. (2015) · IEEE International Conference on Computational Advances in Bio and Medical Sciences · doi:10.1109/iccabs.2015.7344728 · Cited by 1

Prioritize Transcription Factor Binding Sites for Multiple Co-Expressed Gene Sets Based on Lasso Multinomial Regression Models

Hu, H., & Dai, Y. D. (2016) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-5225-0353-8.ch008 · Cited by 1

Gene grouping strategy for network modeling from a small time-series dataset: An illustrative analysis of human organogenesis

Maeda, K. (2019) · Biosystems · doi:10.1016/j.biosystems.2019.02.008 · Cited by 1

Regulatory networks of integer nonlinear programming(RNINLP): A model to analyze gene regulatory networks of breast cancer metastasis

Luo, W., Gong, L., Ren, K., & Yi, D. (2010) · World Congress on Intelligent Control and Automation · doi:10.1109/wcica.2010.5555291 · Cited by 1

Exploring Protein Regulations with Regulatory Networks for Cancer Classification

Wang, H. Q., Zhu, H. L., Yip, T. T. C., Cho, W. C. S., Ngan, R. K. C., & Law, S. C. K. (2008) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2008.205 · Cited by 1

MINING PROTEIN REGULATORY RELATIONSHIPS USING NEURAL NETWORK METHODS FOR EARLY PREDICTION OF SARS

Wang, H., Zhu, H. L., Zheng, C.-H., Yip, T. T. C., Cho, W. C. S., & Law, S. C. K. (2009) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126609005745 · Cited by 1

Inference of Gene Regulatory Network Through Adaptive Dynamic Bayesian Network Modeling

Wang, Y., Berceli, S. A., Garbey, M., & Wu, R. (2019) · ICSA book series in statistics · doi:10.1007/978-3-030-15310-6_5 · Cited by 1

Inferring dynamic gene regulatory networks with low-order conditional independencies – an evaluation of the method

Ajmal, H. B., & Madden, M. G. (2020) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2020-0051 · Cited by 1

Causality in Time-Series: A Short Review

Palshikar, G. K., Apte, M. M., Vaishampayan, S., & Shinde, A. (2023) · Soft Computing Research Society eBooks · doi:10.56155/978-81-955020-2-8-64 · Cited by 1

Gene regulatory network modelling with evolutionary algorithms -an integrative approach

Sîrbu, A. (2011) · Arrow@dit (Dublin Institute of Technology) · Cited by 1

Systems Approach for Understanding Metastasis

WOOLF, P. J., Alvarez, A. A., & Keshamouni, V. G. (2009) · Springer eBooks · doi:10.1007/978-1-4419-0772-1_17 · Cited by 1

Biological knowledge discovery through mining multiple sources of high-throughput data

Chen, Y., & Xu, D. H. (2004) · Cited by 1

Computational Inference of Biological Causal Networks – Analysis of Therapeutic Compound Effects

Yu, J., Helmlinger, G., Saulnier, M., & Georgieva, A. (2008) · doi:10.1002/9783527622818.ch4 · Cited by 1

Reverse Engineering of Biological Systems

Liu, L. (2014) · University Library - University of Saskatchewan (University of Saskatchewan) · Cited by 1

BIOINFORMATICS ORIGINAL PAPER

Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Cited by 1

Reverse Engineering of Temporal Gene Expression Data Using Dynamic Bayesian Networks And Evolutionary Search

Salehi, M. (2008) · QSpace (Queen's University Library) · Cited by 1

The dynamics of the XlnR regulon of Aspergillus niger: a systems biology approach

Omony, J. (2012) · doi:10.18174/239288 · Cited by 1

Understanding the hormonal regulation of mouse lactogenesis by transcriptomics and literature analysis

Ling, M. H. (2009) · Minerva Access (University of Melbourne) · Cited by 1

Quantitative Modelling Approaches

Castiglione, F., Mancini, E., Pedicini, M., & Jarrah, A. S. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20454-8 · Cited by 1

Gene Regulatory Models for Plant Development and Evolution

Alvarez-Buylla, E. R., Benítez, M., Aldana, M., Escalera-Santos, G., Chaos, Á., Padilla-Longoria, P., & Verduzco-Vázquez, R. (2009) · Springer eBooks · doi:10.1007/978-3-642-02301-9_1 · Cited by 1

Quantitative Modelling Approaches

Castiglione, F., Mancini, E., Pedicini, M., & Jarrah, A. S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00029-4 · Cited by 3

AN ONTOLOGY FOR COMPUTER-AIDED MODELING OF CELLULAR PROCESSES

Demir, E. (2005) · Cited by 1

CINRG: Systems Biology of Glucocorticoids in Muscle Disease

Wang, Z. (2012) · doi:10.21236/ada575824

Statistical Network Analysis for Biological Systems and Pathways

Kim, Y., Lee, J. K., Kim, H., Muthiah, A., & Davis, G. M. (2010) · Wiley eBooks · doi:10.1002/9780470567647.ch11

Improving statistical inference for gene expression profiling data by borrowing information

Qu, L. (2010) · doi:10.31274/etd-180810-4313

Biological Data Integration and Model Building

Eddy, J. A., & Price, N. D. (2013) · Springer eBooks · doi:10.1007/978-3-642-27737-5_34-3

Recovering Genetic Network from Continuous Data with Dynamic Bayesian Networks

Lelandais, G., & Lèbre, S. (2011) · doi:10.1002/9781119970606.ch12

Gene Relation Finding Through Mining Microarray Data and Literature

Wang, H., Lee, Y.-S., & Huang, T.-H. (2006) · Lecture notes in computer science · doi:10.1007/11790105_7

MiniTUBA: a Web-Based Dynamic Bayesian Network Analysis System

He, Y. O., & Xiang, Z. (2010) · Sciyo eBooks · doi:10.5772/10070

Biológiai hálózatok átfedő modularizálását végző számítógépes programok és azok alkalmazási területei

Szalay-Bekő, M. (2014) · SZTE Repository of Dissertations (University of Szeged) · doi:10.14232/phd.1963

An Optimization Model for Achieving Sparsity of Gene Regulatory Networks

Li, J., & Xia, Y. (2006)

A Hybrid Methodology for Pattern Recognition in Signaling Cervical Cancer Pathways

Escarcega, D., Ramos, F., Espinosa-García, A. M., & Berumen-Campos, J. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15992-3_32

Online spectral clustering on network streams

Huan, J., & Jia, Y. (2012) · KU ScholarWorks (The University of Kansas)

Inferring the dynamics of gene regulatory networks via optimized recurrent neural network and dynamic Bayesian network

Akutekwe, A., & Şeker, H. (2015) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2015.7300322

Transcriptional Regulatory Networks across Species : Evolution, Inference, and Refinement

Zhang, X. (2011) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-5145

Inference of Gene Regulatory Networks Using Time Sliding Comparison and Transcriptional Lagging Time from Time Series Gene Expression Profiles

Kim, S., & Kim, D. (2007) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2007.4375684

Integrative Estimation of Gene Reguratory Network by means of AdaBoost

Nabatame, S., & Iba, H. (2007) · Transactions of the Japanese Society for Artificial Intelligence · doi:10.1527/tjsai.22.508

A Hybrid Statistical Language Model Applied to the Domain Specific Information Retrieval

Wang, W., Lin, K., & Zhou, C. (2008) · International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) · doi:10.1109/fskd.2008.240

Autocatalytic closure and the evolution of cellular information processing networks

Decraene, J. (2009) · Arrow@dit (Dublin Institute of Technology)

Reverse engineering time series of gene expression data using Dynamic Bayesian networks and covariance matrix adaptation evolution strategy with explicit memory

Salehi, M., Ableson, A., & Mousavi, P. (2008) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2008.4675765

Large-scale DNA Microarray Data Analysis Reveals Glucocorticoid Receptor-mediated Breast Cancer Cell Survival Pathways

Zou, M., Wu, W. J., & Conzen, S. D. (2007) · Humana Press eBooks · doi:10.1007/978-1-59745-309-7_9

Reverse engineering of gene regulatory networks: A systems approach

Wang, Z., & Mousavi, P. (2011) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2011.5948475

NOVEL MODELING FORMALISMS AND SIMULATION TOOLS IN COMPUTATIONAL BIOSYSTEMS

Machado, C. D. (2010) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)

System Identification methods for Reverse Engineering Gene Regulatory Networks

Wang, Z. (2010) · QSpace (Queen's University Library)

Computational techniques for inferring regulatory networks

Page, C. D., & Ong, I. M. (2007)

Supplementary Material: GeneNetWeaver: In silico benchmark generation and performance profiling of network inference methods

Schaffter, T., Marbach, D., & Floreano, D. (2011)

Computational Inference of Compound-induced Anti-inflammatory Effects Across Time in an Adjuvant-induced Arthritis Rat Model.

Yu, J., Helmlinger, G., Saulnier, M., & Георгиева, A. (2006) · BIOCOMP

strategies, perspectives and challenges Reverse engineering and identification in systems biology:

Villaverde, A. F., & Banga, J. R. (2014)

Unsupervised Methods to Identify Cellular Signaling Networks from Perturbation Data

Natarajan, M. (2013) · IGI Global eBooks · doi:10.4018/978-1-4666-3604-0.ch030

Computational Hybrid Systems for Identifying Prognostic Gene Markers of Lung Cancer

Wan, Y. (2011) · doi:10.33915/etd.4812

Building and validating gene regulatory networks (GRN) with delays from multiple data sources

Liu, W. (2015) · doi:10.32657/10356/65383

Parallel Computing Algorithms for Reverse-Engineering and Analysis of Genome-Wide Gene Regulatory Networks from Gene Expression Profiles

Belcastro, V., di Bernardo, D., Gregoretti, F., & Oliva, G. (2010) · International Workshop on Parallel and Distributed Methods in Verification and International Workshop on High Performance Computational Systems Biology (PDMC-HiBi) · doi:10.1109/pdmc-hibi.2010.20

Learning Gene Regulatory Networks Computationally from Gene Expression Data Using Weighted Consensus

Fujii, C. (2015) · King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) · doi:10.25781/kaust-1a8vv

Building trajectories through clinical data to model disease progression

Li, Y. (2013) · Brunel University Research Archive (BURA) (Brunel University London)

Structure Identification for Networked Systems

Zhou, T., You, K., & Li, T. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-805311-9.00010-5

Halobacterium salinarum NRC-1: rede de regulação gênica e sua análise probabilística

Crocetti, G. M. (2018) · doi:10.11606/d.95.2018.tde-31052018-172109

Developing semantic pathway comparison methods for systems biology

Gamalielsson, J. (2009) · OpenGrey (Institut de l'Information Scientifique et Technique)

Linking the epigonome and transcriptome

Kuijpers, T. (2021) · Research Publications (Maastricht University) · doi:10.26481/dis.20211201tk

References

Chen, L., Wang, R.-S., & Zhang, X.-S. (2009) · Wiley eBooks · doi:10.1002/9780470488065.refs

Unsupervised Methods to Identify Cellular Signaling Networks from Perturbation Data

Natarajan, M. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-491-2.ch015

Correction: Improved Inference of Gene Regulatory Networks through Integrated Bayesian Clustering and Dynamic Modeling of Time-Course Expression Data

Godsey, B. (2014) · PLoS ONE · doi:10.1371/annotation/64541b73-5ebc-4f5b-acab-241ecf2b3203

References

Mallick, B. K., Gold, D. L., & Baladandayuthapani, V. (2009) · Wiley eBooks · doi:10.1002/9780470742785.refs

Prioritize Transcription Factor Binding Sites for Multiple Co-Expressed Gene Sets Based on Lasso Multinomial Regression Models

Hu, H., & Dai, Y. D. (2019) · IGI Global eBooks · doi:10.4018/978-1-5225-8903-7.ch037

Biological Data Integration and Model Building

Eddy, J. A., & Price, N. D. (2009) · Springer eBooks · doi:10.1007/978-0-387-30440-3_34

Synthesizing Time-Series Gene Expression Data to Enhance Network Inference Performance Using Autoencoder

Anh, C.-T., & Kwon, Y. (2025) · Applied Sciences · doi:10.3390/app15105768

Developing Semantic Pathway Alignment Algorithms for Systems Biology

Gamalielsson, J. (2006) · University Library of Skövde (University of Skövde)

Dynamic Bayesian Network and Nonparametric Regression for Nonlinear Modeling of Gene Networks from Time Series Gene Expression Data

Kim, S., Imoto, S., & Miyano, S. (2003) · Lecture notes in computer science · doi:10.1007/3-540-36481-1_9 · Cited by 40

Bayesian Inference of Genetic Regulatory Networks from Time Series Microarray Data Using Dynamic Bayesian Networks

Huang, Y., Wang, J., Zhang, J., Sanchez, M., & Wang, Y. (2007) · Journal of Multimedia · doi:10.4304/jmm.2.3.46-56 · Cited by 19

Inference of gene regulatory networks by means of dynamic differential Bayesian networks and nonparametric regression.

Sugimoto, N., & Iba, H. (2004) · PubMed · Cited by 26

Performance evaluation of the time-delayed dynamic Bayesian network approach to inferring gene regulatory networks from time series microarray data

Li, H., Li, P., Zhang, C., Wang, N., Gong, P., & Perkins, E. J. (2010) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/1854776.1854859 · Cited by 1

Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM) for Modeling of Gene Network from Time Series Gene Expression Data.

Zhang, Y., Deng, Z., Jiang, H., & Jia, P. (2006) · Cited by 10