ModularBoost: an efficient network inference algorithm based on module decomposition

[1]  Wei Zhang,et al.  A Novel Model Integration Network Inference Algorithm with Clustering and Hub Genes Finding , 2020, Molecular informatics.

[2]  T. M. Murali,et al.  Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data , 2019, Nature Methods.

[3]  Ning Wang,et al.  Data Integration of Hybrid Microarray and Single Cell Expression Data to Enhance Gene Network Inference , 2019, Current Bioinformatics.

[4]  Ping Luo,et al.  Enhancing the prediction of disease-gene associations with multimodal deep learning , 2019, Bioinform..

[5]  Ning Wang,et al.  Hierarchical parameter estimation of GRN based on topological analysis. , 2018, IET systems biology.

[6]  Yves Moreau,et al.  GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks , 2018, Bioinform..

[7]  Ziv Bar-Joseph,et al.  Deep learning for inferring gene relationships from single-cell expression data , 2019, Proceedings of the National Academy of Sciences.

[8]  Wilberforce Zachary Ouma,et al.  Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties , 2018, PLoS Comput. Biol..

[9]  Y. Saeys,et al.  A comprehensive evaluation of module detection methods for gene expression data , 2018, Nature Communications.

[10]  Song Li,et al.  Identification of regulatory modules in genome scale transcription regulatory networks , 2017, BMC Systems Biology.

[11]  Thelma Sáfadi,et al.  Independent Component Analysis (ICA) based-clustering of temporal RNA-seq data , 2017, PloS one.

[12]  Florence Demenais,et al.  SigMod: an exact and efficient method to identify a strongly interconnected disease‐associated module in a gene network , 2017, Bioinform..

[13]  Hisanori Kiryu,et al.  SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation , 2016, bioRxiv.

[14]  Thalia E. Chan,et al.  Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures , 2016, bioRxiv.

[15]  N Baldwin,et al.  A narrow repertoire of transcriptional modules responsive to pyogenic bacteria is impaired in patients carrying loss-of-function mutations in MYD88 or IRAK4 , 2014, Nature Immunology.

[16]  Damien Chaussabel,et al.  Democratizing systems immunology with modular transcriptional repertoire analyses , 2014, Nature Reviews Immunology.

[17]  M. Dehmer,et al.  Interfacing cellular networks of S. cerevisiae and E. coli: Connecting dynamic and genetic information , 2013, BMC Genomics.

[18]  Diogo M. Camacho,et al.  Wisdom of crowds for robust gene network inference , 2012, Nature Methods.

[19]  Feng Luo,et al.  Molecular ecological network analyses , 2012, BMC Bioinformatics.

[20]  Jean-Philippe Vert,et al.  TIGRESS: Trustful Inference of Gene REgulation using Stability Selection , 2012, BMC Systems Biology.

[21]  Kim-Anh Lê Cao,et al.  Independent Principal Component Analysis for biologically meaningful dimension reduction of large biological data sets , 2012, BMC Bioinformatics.

[22]  P. Deloukas,et al.  Integrating Genome-Wide Genetic Variations and Monocyte Expression Data Reveals Trans-Regulated Gene Modules in Humans , 2011, PLoS genetics.

[23]  P. Geurts,et al.  Inferring Regulatory Networks from Expression Data Using Tree-Based Methods , 2010, PloS one.

[24]  J. Collins,et al.  Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles , 2007, PLoS biology.

[25]  Ting Wang,et al.  An improved map of conserved regulatory sites for Saccharomyces cerevisiae , 2006, BMC Bioinformatics.

[26]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .

[27]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[28]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .