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  • DiSignAtlas: an atlas of human and mouse disease signatures . . .
    Molecular signatures, which usually encompass sets of biological molecules, are instrumental in disease research Importantly, such signatures can be derived from diverse omics data types, allowing a more comprehensive, unbiased and comparable approach to identifying relevant molecular patterns (2)
  • Proteomic signatures improve risk prediction for common and . . .
    These findings show that sparse plasma protein signatures, including both disease-specific proteins and protein predictors shared across several diseases, offer clinically useful prediction of
  • Molecular Signatures Database - GSEA-MSigDB
    immunologic signature gene sets represent cell states and perturbations within the immune system regulatory target gene sets based on gene target predictions for microRNA seed sequences and predicted transcription factor binding sites
  • Illuminating the Molecular Landscape of Disease with . . . - CBIRT
    One of the most powerful aspects of DiSignAtlas is the ability to systematically compare signatures across diseases and species: Inter-disease connections – Identify common or divergent mechanisms by analyzing DEG overlaps Disease models – Match human signatures to mouse models by comparing orthologous DEGs
  • Molecular signatures from omics data: From chaos to consensus
    Molecular signatures can be derived from a broad range of omics data types (e g DNA sequence, mRNA, and protein expression) and can be used to predict various clinical phenotypes (e g response to therapy, prognosis) for previously unseen patient specimens
  • Navigating the disease landscape: knowledge representations . . .
    Here, we review efforts to represent the context of disease-implicated genes, and we suggest that they can be divided into three broad themes, namely, pathway-centric, molecular network centric and approaches that represent biological statements as a knowledge graph (Table 1)
  • Towards a global investigation of transcriptomic signatures . . .
    We present a systematic approach that sheds light on the patterns observed in hundreds of transcriptomic datasets from over sixty indications by using pathways and molecular interactions as a template





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