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Zhourun Wu et al.
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Inference of gene regulatory networks based on nonlinear ordinary differential equations
Baoshan Ma et al.
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Reconstructing gene regulatory networks in single-cell transcriptomic data analysis
Hao Dai et al.
ZOOLOGICAL RESEARCH (2020)
Eleven grand challenges in single-cell data science
David Laehnemann et al.
GENOME BIOLOGY (2020)
Integration of Multi-Omics Data for Gene Regulatory Network Inference and Application to Breast Cancer
Lin Yuan et al.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2019)
Evaluating measures of association for single-cell transcriptomics
Michael A. Skinnider et al.
NATURE METHODS (2019)
WASABI: a dynamic iterative framework for gene regulatory network inference
Arnaud Bonnaffoux et al.
BMC BIOINFORMATICS (2019)
Deep learning for inferring gene relationships from single-cell expression data
Ye Yuan et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)
RNDEtree: Regulatory Network With Differential Equation Based on Flexible Neural Tree With Novel Criterion Function
Bin Yang et al.
IEEE ACCESS (2019)
Development of stock correlation networks using mutual information and financial big data
Xue Gun et al.
PLOS ONE (2018)
HSCVFNT: Inference of Time-Delayed Gene Regulatory Network Based on Complex-Valued Flexible Neural Tree Model
Bin Yang et al.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2018)
Inference of Large-scale Time-delayed Gene Regulatory Network with Parallel MapReduce Cloud Platform
Bin Yang et al.
SCIENTIFIC REPORTS (2018)
Discovery of Relationships Between Long Non-Coding RNAs and Genes in Human Diseases Based on Tensor Completion
Chen Peng et al.
IEEE ACCESS (2018)
LEAP: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering
Alicia T. Specht et al.
BIOINFORMATICS (2017)
Output Regulation of Boolean Control Networks
Haitao Li et al.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2017)
Model-free inference of direct network interactions from nonlinear collective dynamics
Jose Casadiego et al.
NATURE COMMUNICATIONS (2017)
Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures
Thalia E. Chan et al.
CELL SYSTEMS (2017)
Quantifying Gene Regulatory Relationships with Association Measures: A Comparative Study
Zhi-Ping Liu
FRONTIERS IN GENETICS (2017)
Reverse-engineering of gene networks for regulating early blood development from single-cell measurements
Jiangyong Wei et al.
BMC MEDICAL GENOMICS (2017)
SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation
Hirotaka Matsumoto et al.
BIOINFORMATICS (2017)
SCOUP: a probabilistic model based on the Ornstein-Uhlenbeck process to analyze single-cell expression data during differentiation
Hirotaka Matsumoto et al.
BMC BIOINFORMATICS (2016)
An integrated approach to infer dynamic protein-gene interactions - A case study of the human P53 protein
Junbai Wang et al.
METHODS (2016)
Part mutual information for quantifying direct associations in networks
Juan Zhao et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2016)
System-wide Clinical Proteomics of Breast Cancer Reveals Global Remodeling of Tissue Homeostasis
Yair Pozniak et al.
CELL SYSTEMS (2016)
Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data
Andrea Ocone et al.
BIOINFORMATICS (2015)
The Proteomic Landscape of Triple-Negative Breast Cancer
Robert T. Lawrence et al.
CELL REPORTS (2015)
Causal Inference Using Graphical Models with theRPackagepcalg
Markus Kalisch et al.
Journal of Statistical Software (2015)
Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks
Xiujun Zhang et al.
NUCLEIC ACIDS RESEARCH (2015)
Supervised, semi-supervised and unsupervised inference of gene regulatory networks
Stefan R. Maetschke et al.
BRIEFINGS IN BIOINFORMATICS (2014)
Regression Analysis of Combined Gene Expression Regulation in Acute Myeloid Leukemia
Yue Li et al.
PLOS COMPUTATIONAL BIOLOGY (2014)
Learning restricted Boolean network model by time-series data
Hongjia Ouyang et al.
EURASIP JOURNAL ON BIOINFORMATICS AND SYSTEMS BIOLOGY (2014)
QUANTIFYING CAUSAL INFLUENCES
Dominik Janzing et al.
ANNALS OF STATISTICS (2013)
Genomic and Epigenomic Landscapes of Adult De Novo Acute Myeloid Leukemia
Timothy J. Ley et al.
NEW ENGLAND JOURNAL OF MEDICINE (2013)
Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information
Xiujun Zhang et al.
BIOINFORMATICS (2012)
Identifying dysregulated pathways in cancers from pathway interaction networks
Ke-Qin Liu et al.
BMC BIOINFORMATICS (2012)
Wisdom of crowds for robust gene network inference
Daniel Marbach et al.
NATURE METHODS (2012)
Insights into the regulation of protein abundance from proteomic and transcriptomic analyses
Christine Vogel et al.
NATURE REVIEWS GENETICS (2012)
Mathematical Modelling of the MAP Kinase Pathway Using Proteomic Datasets
Tianhai Tian et al.
PLOS ONE (2012)
Activation and Function of the MAPKs and Their Substrates, the MAPK-Activated Protein Kinases
Marie Cargnello et al.
MICROBIOLOGY AND MOLECULAR BIOLOGY REVIEWS (2011)
Advantages and limitations of current network inference methods
Riet De Smet et al.
NATURE REVIEWS MICROBIOLOGY (2010)
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods
Van Anh Huynh-Thu et al.
PLOS ONE (2010)
DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models
Alex Greenfield et al.
PLOS ONE (2010)
Revealing strengths and weaknesses of methods for gene network inference
Daniel Marbach et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2010)
A Yeast Synthetic Network for In Vivo Assessment of Reverse-Engineering and Modeling Approaches
Irene Cantone et al.
CELL (2009)
Comprehensive genomic characterization defines human glioblastoma genes and core pathways
L. Chin et al.
NATURE (2008)
Modelling and analysis of gene regulatory networks
Guy Karlebach et al.
NATURE REVIEWS MOLECULAR CELL BIOLOGY (2008)
Information-Theoretic Inference of Large Transcriptional Regulatory Networks
Patrick E. Meyer et al.
EURASIP JOURNAL ON BIOINFORMATICS AND SYSTEMS BIOLOGY (2007)
Reverse engineering of regulatory networks in human B cells
K Basso et al.
NATURE GENETICS (2005)
The KEGG resource for deciphering the genome
M Kanehisa et al.
NUCLEIC ACIDS RESEARCH (2004)
A gene-coexpression network for global discovery of conserved genetic modules
JM Stuart et al.
SCIENCE (2003)
Assigning numbers to the arrows: Parameterizing a gene regulation network by using accurate expression kinetics
M Ronen et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2002)