“The book covers a variety of computational and mathematical aspects related to the inference of gene regulatory networks. The style of the chapters seamlessly binds the comprehensive overview that recommended the book to junior researchers and the thorough description of topics, highlighting new direction of research, that would appeal to post-graduates and established researchers.” (Irina Ioana Mohorianu, zbMATH 1417.92005, 2019)

• Gene Regulatory Network Inference: An Introductory Survey.- Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks.- Overview and Evaluation of Recent Methods for Statistical Inference of Gene Regulatory Networks from Time Series Data.- Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic Data.- Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks.- A Multiattribute Gaussian Graphical Model for Inferring Multiscale Regulatory Networks: An Application in Breast Cancer.- Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks.- Unsupervised Gene Network Inference with Decision Trees and Random Forests.- Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3.- Network Inference from Single-Cell Transcriptomic Data.- Inferring Gene Regulatory Networks from Multiple Datasets.- Unsupervised GRN Ensemble.- Learning Differential Module Networks across Multiple Experimental Conditions.- Stability in GRN Inference.- Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling.- Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes.

“The book covers a variety of computational and mathematical aspects related to the inference of gene regulatory networks. The style of the chapters seamlessly binds the comprehensive overview that recommended the book to junior researchers and the thorough description of topics, highlighting new direction of research, that would appeal to post-graduates and established researchers.” (Irina Ioana Mohorianu, zbMATH 1417.92005, 2019)

• Gene Regulatory Network Inference: An Introductory Survey.- Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks.- Overview and Evaluation of Recent Methods for Statistical Inference of Gene Regulatory Networks from Time Series Data.- Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic Data.- Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks.- A Multiattribute Gaussian Graphical Model for Inferring Multiscale Regulatory Networks: An Application in Breast Cancer.- Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks.- Unsupervised Gene Network Inference with Decision Trees and Random Forests.- Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3.- Network Inference from Single-Cell Transcriptomic Data.- Inferring Gene Regulatory Networks from Multiple Datasets.- Unsupervised GRN Ensemble.- Learning Differential Module Networks across Multiple Experimental Conditions.- Stability in GRN Inference.- Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling.- Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes.

“The book covers a variety of computational and mathematical aspects related to the inference of gene regulatory networks. The style of the chapters seamlessly binds the comprehensive overview that recommended the book to junior researchers and the thorough description of topics, highlighting new direction of research, that would appeal to post-graduates and established researchers.” (Irina Ioana Mohorianu, zbMATH 1417.92005, 2019)

• Gene Regulatory Network Inference: An Introductory Survey.- Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks.- Overview and Evaluation of Recent Methods for Statistical Inference of Gene Regulatory Networks from Time Series Data.- Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic Data.- Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks.- A Multiattribute Gaussian Graphical Model for Inferring Multiscale Regulatory Networks: An Application in Breast Cancer.- Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks.- Unsupervised Gene Network Inference with Decision Trees and Random Forests.- Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3.- Network Inference from Single-Cell Transcriptomic Data.- Inferring Gene Regulatory Networks from Multiple Datasets.- Unsupervised GRN Ensemble.- Learning Differential Module Networks across Multiple Experimental Conditions.- Stability in GRN Inference.- Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling.- Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes.