Machine learning and AI are reshaping biology and biomedical research - but making them work on your data isn't easy.

We help researchers apply proven models like XGBoost, SVM, or CNNs to datasets like RNA-seq, single-cell, histology, and clinical records - properly, efficiently, and with biological clarity.

🔍 Where Most Researchers Get Stuck:

• ❌ Data quantity too low, complexity too high - biological datasets often break naive assumptions
• ❌ Published models don’t just run - broken code, odd formats, and missing assumptions abound
• ❌ Preprocessing isn't standardized - one wrong choice can derail everything
• ❌ Multi-omics integration creates spurious correlations if not handled properly
• ❌ Validation leaks and test set misuse - a recipe for irreproducibility
• ❌ Deep model outputs are hard to interpret and hard to publish
• ❌ Deadlines loom - trial-and-error wastes valuable time

✅ How We Help with Machine Learning and AI

We don’t invent new ML methods - we help apply the right ones, the right way, for your data and goals.

• - Model selection: SVM, random forest, logistic regression, XGBoost, CNNs
• - Deep learning for sequence data, histology images, and spatial transcriptomics
• - Clustering and embedding (UMAP, autoencoders, k-means) for stratification
• - Proper train/test splits, cross-validation, and leakage prevention
• - Integration with SHAP, metadata, and biological interpretation tools
• - Survival models, treatment response modeling, trajectory inference
• - Figures, methods, and grant supplements - all handled

Whether your focus is histology, patient response, or multi-modal single-cell data, we help you get results that make sense - and meet your timeline.

📚 Learn More From Our AI/ML Blog Series

• → AI Models in Bioinformatics: Some General Tips
• → 10 Real-World AI Applications in Biology and Biomedicine You’ve Read About - But Might Need Help Implementing

These are the same practical challenges our clients face - and the same ones we help solve every day.

📈 Explore the 10 Reasons Why Researchers Choose Us

Reason #1: 150 years of combined experience in bioinformatics
Reason #2: Deep understanding of both the biology and the computer
Reason #3: Top-level expertise possessed by our bioinformaticians
Reason #4: Wide range of bioinformatics and data modeling solutions
Reason #5: Researchers from 80 organizations can't all be wrong!
Reason #6: We are cost-effective
Reason #7: We guarantee to do it "right"!
Reason #8: We will assist you in paper reviews
Reason #9: We will assist you in grant writing
Reason #10: See how researchers have evaluated us

Find out more about our company, or check out our FAQ page!

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