Edit and expand networks based on the molecular relationships most relevant to the project. Understand toxicity responses by exploring connections between drugs or targets and related genes or chemicals.

Identify regulatory events that lead from signaling events to transcriptional effects. These analyses are used to determine the most significantly affected pathways.Р”В¬omparison Analysis determines theР’В most significant pathways, upstream regulators, diseases, biological functions, and more, across time points, dose, or other conditions.Р”В«uild and explore transcriptional networks, miRNAРІР‚вЂњmRNA target networks, phosphorylation cascades, and proteinРІР‚вЂњprotein or proteinРІР‚вЂњDNA interaction networks. Pathway Analysis, Canonical Pathways, Overlapping Pathways, Pathway Import, and scoring Gene expression results are used to identify whether significant downstream biological processes are increased or decreased. Mechanistic Networks automatically generate plausible signaling cascades, describing potential mechanisms of action that lead to observed gene expression changes. This analysis predicts upstream molecules, including miRNA and transcription factors,Р’В that may be causing observed gene expression changes. Identify genes known to be causally relevant as potential targets or identify targets of toxicity, associated known drugs, biomarkers, and pathways. Prioritize by score, hierarchical cluster, or trend.Р”Р„Р’В disease or phenotype can be rapidly profiled by understanding its associated genes and compounds. Focus on the networks that are of highest relevance by scoring the resulting causal networks against molecules, diseases, or functions of interest.Р”В¬omparison Analysis provides quick visualization of canonical pathway score trends across dose, time, or other factors using theР’В Comparison AnalysisР’В heat map. Expanding beyond direct or single-hop relationships between the upstream regulator and the target molecules in the dataset, Causal Network AnalysisР’В uncovers networks of regulators that connect to the dataset targets. These matches can help confirm your interpretation of the results or provide unexpected insights into underlying shared biological mechanisms among ostensibly unrelated datasets.Р”В¬ausal Network Analysis comprehensively identifies upstream molecules that control the expression of the genes in datasets. Analysis Match automatically helps you discover other IPA Core Analyses with similar (or opposite) biological results as compared to yours.

Broaden the scope of your discovery when you evaluate your dataset in the context of more than 50,000 IPA analyses.

Long path tool alternative