Streamlining Biomarker Discovery: A Standardized Multi-Omics Pipeline for Cell Line Analysis
In precision medicine, biomarker analysis is pivotal—deciphering disease mechanisms, predicting drug responses, and laying the scientific foundation for personalized therapies. Our integrated platform combines multi-omics data with AI to:
· Identify responsive cancer types through drug sensitivity profiling
· Elucidate drug mechanisms of action (MoA)
· Pinpoint clinically relevant predictive biomarkers
· Predict drug responses for unassayed cell lines
This completes the cycle from basic research to clinical insight—all while your coffee brews.
Six-Step Analysis in Less Time Than Your Coffee Break:
1. Multi-Omics Data Hub & QC
Directly access our curated database of 2,000+ pre-processed cell line multi-omics datasets. Alternatively, upload your own drug response data for rapid integration with stored cell line omics profiles. Automated project summary generation with comprehensive QC metrics.
Example Results:
2. Cell Line Screening & Sensitivity Classification
Dual-Parameter Drug Efficacy Evaluation System
l Integrates half maximal inhibitory concentration (IC50) and area under the dose-response curve (AUC) for comprehensive drug response assessment.
l Automatically calculates correlation coefficients and statistical significance between parameters.
Cross-Cancer Pharmacodynamic Visualization
l Dynamic box plots display log10IC50/AUC distributions across different cancer types.
l Supports cancer-type-specific subgroup analysis.
Example Results:
3. Drug Correlation Analysis
This module systematically compares the target drug with approximately 2,000 reference drugs (all with clearly defined targets and pathway characteristics) for therapeutic efficacy evaluation. Utilizing both Spearman and Pearson correlation analyses of key pharmacodynamic parameters (AUC or IC50), it helps reveal potential drug mechanisms of action and identifies drugs with similar/opposing efficacy profiles.
Example Results:
4. Single Gene/Pathway Association Analysis
Gene Expression Analysis
l Differential expression gene (DEG) screening.
l Pathway enrichment (ORA/GSEA) revealing regulatory mechanisms.
l Protein-protein interaction networks identifying core targets.
Gene Mutation Analysis
l Mutation profile comparison between response and non-response groups (cancer genes + whole genome).
l Prioritization of driver mutation targets.
Copy Number Variation Analysis
l Screening of differentially amplified/deleted genes.
l Interactive oncoplots displaying variation patterns with IC50/AUC efficacy correlations.
Protein Expression Analysis
l Quantitative screening of differentially expressed proteins.
l 3D visualization of pathway-protein networks.
l Potential biomarker identification.
Pathway Activity Analysis
l Screening of differentially activated pathway .
l Heatmap/PCA clustering revealing response characteristics.
l Mechanistic pathway-target analysis.
Pan-Gene View
l Supports multi-omics data integration.
l Intelligent heatmap displaying cross-cancer patterns with IC50/AUC efficacy correlations.
Partial Results Preview:
5. AI-Driven Multi-Gene Predictive Biomarker Discovery
Using gene expression, mutations, CNV data and pathway data as inputs, we then: Apply permutation algorithms for biomarker candidate selection →Develop logistic regression predictive models →Validate performance on independent datasets and Generate interpretative visualizations (ROC curves, cluster heatmaps).
Example Results:
6. Drug Sensitivity Prediction for Un-assayed Cell Lines
Drug sensitivity predictions for characterized cell lines in the database can be performed using logistic regression models based on developed biomarkers.
Traditional analytical pipelines often require weeks to complete, while the rapid evolution of both clinical and research demands calls for more efficient solutions to dramatically shorten the "data-to-discovery" cycle. Now, results can be delivered in the time it takes to enjoy a cup of coffee.
Analysis Outputs:
Modular Output
Download individual analysis reports on demand (e.g., Gene Expression Analysis Report, Protein Expression Analysis Report).
Complete Data Package
High-resolution figures, process log files and raw analytical data (CSV format for further analysis).
One-Click Consolidated Report
Generates a comprehensive report integrating all analytical results (including an interactive HTML version)
Ready to Experience the Difference?
Schedule a Demo today!
