Skip to content


Our experts in bioinformatics can offer you the analysis of differentially expressed genes/proteins and pathways affected by gene dysfunction/disease/treatment.

Standard analysis

The standard bioinformatic analysis includes sc/snRNA sequencing data consolidation, counting and quantification, quality control, normalization,  batch effect correction, cell clustering, cell type annotation, differential gene expression in major cell types, and data visualization.​

Customized bioinformatic analysis

For specific biological questions, our services include additional cell sub-clustering, differential gene expression in distinct cell types, Gene Ontology analysis, single-cell trajectory inference, co-expression network analysis, cell-cell interaction analysis, machine, and deep learning-based analysis and predictions.​​

AI-driven omics analysis

Our team leverages the power of explainable machine learning (ML) models to integrate extensive biological datasets. Among many applications, our ML models can:

  • Uncover molecular mechanisms linked to drug efficacy;
  • Predict treatment toxicities;
  • Predict drug efficacy and combination synergies;
  • Predict repurposing of existing drugs for new therapeutic uses;
  • Predict patient-specific responses to therapies;
  • Identify disease-related signatures;
  • Identify drug development targets and diagnostic biomarkers.
Back To Top