From Data to Value in Hours
Our AI-powered analysis automates each step, rapidly delivering results as an interactive web application that can be explored and shared across all stakeholders.
- Identify Relevant Clinical Variables
Clinical information (e.g., age, sex, BMI, drug use) is used to identify the most relevant molecular changes. We compare AI-based predictions with supplied metadata to detect and correct labeling errors and select the minimal set of relevant clinical variables to preserve statistical power in subsequent steps. - Assess Quality
We perform quality control (QC) on each sample to identify technical errors and use a data-driven approach to detect samples that differ substantially from others in the experiment, ensuring more reliable downstream insights. - Normalize Across Samples
To reveal true biological variation, we detect and reduce subtle technical noise that can arise during sample processing. We normalize measurements, filter out erroneous proteins, impute missing values where appropriate and adjust for known batch effects. - Differentially Abundant Proteins
Depending on your experimental design and research question, we select the most appropriate, statistically rigorous approach. We account for technical and biological variability and automatically fit an appropriate linear regression model to each protein. - Pinpoint Affected Pathways
We map proteins to genes and perform gene set enrichment analysis (GSEA) across all major pathway databases, clustering similar pathways to explore how groups of genes act together, even when individual genes are not statistically significant. - Build Diagnostic Classifiers
We identify the minimal set of biomarkers that best differentiate sample groups. By evaluating multiple machine learning algorithms and cross-validation strategies, we assess classifier accuracy for potential diagnostic, prognostic, or treatment applications.
Additional Value Out-of-the-Box
Proteomic data contains a wealth of valuable information that often goes unexplored. To ensure you do not miss key insights, these analyses are included as part of our standard workflow.
- Phospho-Proteomics
Phospho-Proteomics focuses on proteins that have undergone phosphorylation. When possible, we quantify Kinase-Substrate interactions, build signalomes that elucidate physical and functional protein interactions. - Investigate Drugs for Repurposing
Automated analysis of drug connectivity databases identifies compounds that may reverse the observed phenotype, uncovering new drug repurposing opportunities and highlighting candidates that could counteract disease signatures. - Protein-Protein Interactions
We identify the full set of proteins perturbed by a specific disease within the human interactome. Instead of simply retrieving known “hub” proteins that interact with everything, our method focuses on proteins that are specific and critical to the local disease-related cluster.
Get in touch to see how our intelligent analysis platform can redefine your multi-omics results.
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