A comprehensive database for integrated analysis of omics data in autoimmune diseases
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J. Dopazo | J. Saez-Rodriguez | F. Al-Shahrour | G. Gómez-López | K. Troulé | M. Peña-Chilet | G. Barturen | P. Carmona-Sáez | Jordi Martorell-Marugán | R. López-Domínguez | J. A. Villatoro-García | V. González-Rumayor | A. Martín-Gómez | A. García-Moreno | D. Toro-Domínguez | M. Alarcón-Riquelme | J. Villatoro-García | Adrián García-Moreno
[1] J. Dopazo,et al. Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models , 2019, bioRxiv.
[2] Panuwat Trairatphisan,et al. From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL , 2019, npj Systems Biology and Applications.
[3] Daniel Toro-Domínguez,et al. ImaGEO: integrative gene expression meta-analysis from GEO database , 2018, Bioinform..
[4] Alexander Lachmann,et al. Mining data and metadata from the gene expression omnibus , 2018, Biophysical Reviews.
[5] L. Rönnblom,et al. An update on the role of type I interferons in systemic lupus erythematosus and Sjögren's syndrome , 2018, Current opinion in rheumatology.
[6] Marta R. Hidalgo,et al. Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape. , 2018, Cancer research.
[7] J. Sáez-Rodríguez,et al. Benchmark and integration of resources for the estimation of human transcription factor activities , 2018, bioRxiv.
[8] N. Pavlos,et al. Rheumatoid arthritis: pathological mechanisms and modern pharmacologic therapies , 2018, Bone Research.
[9] Jongho Kim,et al. An integrated clinical and genomic information system for cancer precision medicine , 2018, BMC Medical Genomics.
[10] M. Alarcón‐Riquelme,et al. Moving towards a molecular taxonomy of autoimmune rheumatic diseases , 2018, Nature Reviews Rheumatology.
[11] P. López,et al. Heterogeneity of the Type I Interferon Signature in Rheumatoid Arthritis: A Potential Limitation for Its Use As a Clinical Biomarker , 2018, Front. Immunol..
[12] J. Sáez-Rodríguez,et al. Perturbation-response genes reveal signaling footprints in cancer gene expression , 2016, Nature Communications.
[13] Kathleen M Jagodnik,et al. Massive mining of publicly available RNA-seq data from human and mouse , 2017, Nature Communications.
[14] Tom Heskes,et al. RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets , 2017, Bioinform..
[15] M. Alarcón‐Riquelme,et al. Omics studies: their use in diagnosis and reclassification of SLE and other systemic autoimmune diseases. , 2016, Rheumatology.
[16] Jinwei Chen,et al. Application of omics in predicting anti-TNF efficacy in rheumatoid arthritis , 2017, Clinical Rheumatology.
[17] Francisco Salavert,et al. High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes , 2016, bioRxiv.
[18] A. El-Osta,et al. Gene name errors are widespread in the scientific literature , 2016, Genome Biology.
[19] M. Kleinewietfeld,et al. Environmental factors in autoimmune diseases and their role in multiple sclerosis , 2016, Cellular and Molecular Life Sciences.
[20] Virginia Pascual,et al. Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients , 2016, Cell.
[21] K. Kalunian,et al. Sifalimumab, an anti-interferon-α monoclonal antibody, in moderate to severe systemic lupus erythematosus: a randomised, double-blind, placebo-controlled study , 2016, Annals of the rheumatic diseases.
[22] Francisco Salavert,et al. Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity , 2015, Scientific Reports.
[23] A. Conesa,et al. Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package , 2015, Nucleic acids research.
[24] M. Peinado,et al. Wanderer, an interactive viewer to explore DNA methylation and gene expression data in human cancer , 2015, Epigenetics & Chromatin.
[25] Matthew E. Ritchie,et al. limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.
[26] Robert Petryszak,et al. ArrayExpress update—simplifying data submissions , 2014, Nucleic Acids Res..
[27] Daniel Toro-Domínguez,et al. Shared signatures between rheumatoid arthritis, systemic lupus erythematosus and Sjögren’s syndrome uncovered through gene expression meta-analysis , 2014, Arthritis Research & Therapy.
[28] W. Huber,et al. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.
[29] M. Crow. Type I Interferon in the Pathogenesis of Lupus , 2014, The Journal of Immunology.
[30] Rafael A. Irizarry,et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays , 2014, Bioinform..
[31] Hae-Rim Kim,et al. Advances in Systems Biology Approaches for Autoimmune Diseases , 2014, Immune network.
[32] Oliver S. Burren,et al. A Type I Interferon Transcriptional Signature Precedes Autoimmunity in Children Genetically at Risk for Type 1 Diabetes , 2014, Diabetes.
[33] C. Mohan,et al. Systemic lupus erythematosus diagnostics in the 'omics' era. , 2013, International journal of clinical rheumatology.
[34] K. Morris,et al. Interferon-γ and systemic autoimmunity. , 2013, Discovery medicine.
[35] Joshua M. Stuart,et al. The Cancer Genome Atlas Pan-Cancer analysis project , 2013, Nature Genetics.
[36] Ruth Pidsley,et al. A data-driven approach to preprocessing Illumina 450K methylation array data , 2013, BMC Genomics.
[37] Ellen T. Gelfand,et al. The Genotype-Tissue Expression (GTEx) project , 2013, Nature Genetics.
[38] A. Peck,et al. The Interferon-Signature of Sjögren’s Syndrome: How Unique Biomarkers Can Identify Underlying Inflammatory and Immunopathological Mechanisms of Specific Diseases , 2013, Front. Immunol..
[39] L. Rönnblom,et al. The interferon signature in autoimmune diseases , 2013, Current opinion in rheumatology.
[40] R. Weksberg,et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray , 2013, Epigenetics.
[41] Simon Yu,et al. INTERFEROME v2.0: an updated database of annotated interferon-regulated genes , 2012, Nucleic Acids Res..
[42] Francesco Marabita,et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data , 2012, Bioinform..
[43] Thomas R. Gingeras,et al. STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..
[44] Benjamin E. Gross,et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. , 2012, Cancer discovery.
[45] Colin N. Dewey,et al. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome , 2011, BMC Bioinformatics.
[46] Wei Shi,et al. Optimizing the noise versus bias trade-off for Illumina whole genome expression BeadChips , 2010, Nucleic acids research.
[47] M. Robinson,et al. A scaling normalization method for differential expression analysis of RNA-seq data , 2010, Genome Biology.
[48] Hadley Wickham,et al. ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .
[49] E. Birney,et al. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt , 2009, Nature Protocols.
[50] Virginia Pascual,et al. A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus. , 2008, Immunity.
[51] Pan Du,et al. lumi: a pipeline for processing Illumina microarray , 2008, Bioinform..
[52] A. Bird,et al. DNA methylation landscapes: provocative insights from epigenomics , 2008, Nature Reviews Genetics.
[53] Sean R. Davis,et al. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor , 2007, Bioinform..
[54] Bart De Moor,et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis , 2005, Bioinform..
[55] Benjamin M. Bolstad,et al. affy - analysis of Affymetrix GeneChip data at the probe level , 2004, Bioinform..
[56] G. Cooper,et al. The epidemiology of autoimmune diseases. , 2003, Autoimmunity reviews.
[57] Rafael A Irizarry,et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.
[58] M. R. Salaman. A Two-step Hypothesis for the Appearance of Autoimmune Disease , 2003, Autoimmunity.
[59] Alex E. Lash,et al. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..
[60] Susumu Goto,et al. KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..
[61] Hiroyuki Ogata,et al. KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..