Artificial Intelligence for Biotech Discovery
Machine learning and deep learning unlock patterns hidden in your biotech data. From intelligent data format conversion to multi-omic data interpretation, we build AI models that enable new research capabilities and accelerate discovery.
Let's talkWhy Custom AI for Biotech
Off-the-shelf AI models trained on generic data don't understand your biotech domain. Custom models built on your data, validated against your standards, and integrated into your workflows deliver real scientific value and publishable results.
Domain-Specific Accuracy
We don't just apply algorithms. We understand biotech physics, chemistry, and biology. Models are trained and validated on data representative of your research, ensuring predictions are accurate where it matters most.
Interpretability and Explainability
Black-box models don't work in biotech. We build interpretable models and implement explainability methods so you understand why the model makes predictions. This is essential for publications, peer review, and scientific trust.
Rigorous Validation
We validate models against your scientific standards: cross-validation, hold-out test sets, and comparison to existing methods. Performance metrics are tracked and reported transparently so you have confidence in predictions.
Seamless Integration
Models aren't useful sitting in notebooks. We integrate AI predictions into your existing data pipelines, dashboards, and lab workflows so your researchers can act on insights immediately.
Our Machine Learning Development Process
Building biotech AI isn't just data science. It's a partnership between ML engineers and domain experts. We work closely with your team to understand your data, define success metrics, and iterate until models deliver real value.
Problem Definition and Data Strategy
We start by understanding your research questions and data landscape. What patterns are you trying to find? What data do you have? What validation standards apply? Together we design the ML approach that fits your needs.
Data Preparation and Feature Engineering
Quality data determines model quality. We handle data cleaning, feature engineering, handling of missing values, and balancing of imbalanced datasets. For sequence data, we implement tokenization and embedding strategies. For images, we apply preprocessing and augmentation. For spectroscopy, we optimize normalization and binning. Domain knowledge guides feature selection to focus models on scientifically meaningful patterns.
Model Selection and Training
We evaluate multiple model architectures optimized for your data type. For sequence analysis: 1D CNNs, transformers, and recurrent networks that capture temporal dependencies. For imaging: 2D CNNs, U-Net, and vision transformers for spatial feature extraction. For tabular multi-omics: gradient boosting, random forests, and neural networks. We select and train the architecture that balances accuracy, interpretability, and deployment requirements for your use case.
Validation and Performance Assessment
Rigorous validation against your scientific standards. We use cross-validation, hold-out test sets, and comparison to baseline methods. Performance is reported transparently with confidence intervals and failure analysis.
Integration and Deployment
Models go into production as APIs, dashboards, or workflows. We handle containerization, versioning, monitoring, and retraining pipelines so your models stay accurate as new data arrives.
AI Applications Across Biotech
Every biotech domain has unique ML challenges. Here's a sampling of what's possible.
Machine learning works best when applied to specific, well-defined problems where you have enough training data and clear success metrics. We help you identify opportunities where AI can create real competitive advantage.
Instrument Result Data Conversion
Machine learning models that automatically translate data formats to enable ingestion into LIMS and other analysis software removing error and time consuming processes.
Genomic Variant Interpretation
Machine learning classifiers that assess variant pathogenicity, predict functional impact, and integrate multiple data sources (evolutionary conservation, structural effects, population frequency) for comprehensive variant prioritization.
Cell and Tissue Image Analysis
Convolutional neural networks for automated cell segmentation, morphology classification, and disease detection from microscopy images. Enable high-throughput analysis that scales to millions of images.
Metabolite Structure Elucidation
Machine learning models that predict molecular structures from mass spectrometry and NMR data, accelerating compound identification and natural product discovery.
Why Partner with Us for Biotech AI
Building AI isn't just statistics. It requires understanding your scientific domain, validation standards, and data integrity requirements. We bring machine learning expertise combined with deep biotech knowledge.
Domain Expertise
We understand proteomics, genomics, metabolomics, and other biotech disciplines. Our models are built with domain knowledge, not generic ML patterns.
Interpretability
We build models you can understand and explain. Explainability methods ensure you know why predictions are made, which is critical for scientific rigor and reproducibility.
Validation Rigor
Models are validated against your scientific standards with transparent metrics, confidence intervals, and failure analysis, Every result is defensible and reproducible.
Production Ready
Models integrate into your workflows as APIs, dashboards, or automated pipelines. We handle deployment, monitoring, and continuous improvement.
From Models to Discovery
AI is only valuable if it accelerates your research and enables new capabilities. When processing provides clean data, visualization makes it explorable, and machine learning uncovers hidden patterns, you have a complete system for discovery.
Automate Repetitive Analysis
Let models handle routine pattern recognition and classification so your researchers focus on interpretation and follow-up experiments.
Discover Hidden Patterns
Machine learning finds complex relationships in multidimensional data that human analysis might miss, revealing disease mechanisms, novel biomarkers, and unexpected connections.
Scale Analysis Across Datasets
What would take months of manual analysis can now happen in hours. Train once, predict on thousands or millions of samples to identify rare events or broad trends.
Build Competitive Advantage
Custom AI models trained on your proprietary data become strategic assets. They enable research directions your competitors can't match.
Transform Your Biotech Research with AI
Ready to unlock patterns in your data and accelerate discovery? From classification to automatic data conversion and image analysis, we build machine learning solutions tailored to your science. Let's discuss your AI opportunities.
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