AI has enormous potential in biotechnology, but its use is still incomplete because of several persistent data-related challenges. These challenges span data availability, quality, structure, and governance, and they fundamentally limit how well AI models can learn and generalize.
I have tried with the help of colleagues and others in computer science what is a structured overview of the key data challenges.
1. Limited and Biased Data Availability
🔹 Small datasets
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Many biotech experiments are expensive, slow, and low-throughput, resulting in datasets that are tiny by AI standards.
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Examples: rare disease cohorts, patient-derived organoids, animal models.
🔹 Selection bias
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Data often comes from specific populations, lab conditions, or model organisms, limiting generalizability.
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Clinical datasets may overrepresent certain ethnicities, ages, or disease severities.
Impact: AI models overfit and fail to extrapolate to new biological contexts.
2. Data Heterogeneity and Lack of Standardization
🔹 Multiple data modalities
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Genomics, transcriptomics, proteomics, metabolomics, imaging, EHRs, and lab assays all have different formats, scales, and noise profiles.
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Integrating these (“multi-omics”) remains difficult.
🔹 Inconsistent protocols
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Different labs use different experimental methods, reagents, platforms, and analysis pipelines.
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Metadata is often incomplete or missing.
Impact: Models struggle to learn consistent biological signals across datasets.
3. Noisy, Sparse, and Uncertain Data
🔹 High experimental noise
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Biological systems are inherently variable.
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Measurement errors are common (batch effects, dropouts, off-target effects).
🔹 Sparse labeling
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Many datasets have features without reliable labels (e.g., gene function, protein interactions).
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Negative results are rarely recorded.
Impact: AI learns correlations rather than causal biological mechanisms.
4. Lack of Ground Truth and Gold Standards
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In many areas (e.g., gene regulation, disease mechanisms), the true biological answer is unknown.
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Labels are often proxies (e.g., expression change does not equal functional relevance).
Impact: Model validation is weak, and performance metrics may be misleading.
5. Poor Data Integration Across Scales
Biology spans multiple scales:
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Molecular → cellular → tissue → organism → population
Most datasets:
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Capture only one scale at a time
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Are disconnected from phenotypic or clinical outcomes
Impact: AI models fail to connect molecular predictions to real-world biological or clinical effects.
6. Data Silos and Restricted Access
🔹 Proprietary datasets
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Pharmaceutical and biotech companies hold large, high-quality datasets that are not shared.
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Clinical trial data is often inaccessible.
🔹 Privacy and regulatory constraints
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Patient data is heavily regulated (HIPAA, GDPR).
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Data anonymization can remove biologically relevant signals.
Impact: Models are trained on incomplete views of biology.
7. Class Imbalance and Rare Events
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Many biologically important phenomena are rare:
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Rare mutations
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Adverse drug reactions
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Rare cell types
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Impact: AI models optimize for common patterns and miss critical rare biology.
8. Temporal and Contextual Gaps
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Most data is static snapshots, not time-series.
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Biological processes are dynamic and context-dependent (development, disease progression, treatment response).
Impact: AI cannot model causality, feedback loops, or long-term effects.
9. Reproducibility and Data Quality Issues
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Irreproducible experiments contaminate datasets.
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Publication bias favors positive results.
Impact: AI amplifies experimental artifacts rather than biological truth.
10. Misalignment Between Biological Questions and AI Data Needs
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AI thrives on:
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Massive, clean, labeled datasets
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Biology often provides:
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Small, noisy, incomplete, context-dependent data
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Impact: Even advanced models are fundamentally data-limited.
Summary Table
| Challenge | Why It Limits AI |
|---|---|
| Small & biased datasets | Poor generalization |
| Data heterogeneity | Integration failures |
| Noise & sparsity | Correlation over causation |
| No ground truth | Weak validation |
| Scale disconnect | Poor clinical translation |
| Data silos | Incomplete learning |
| Rare events | Missed critical biology |
| Static data | No dynamics or causality |
| Reproducibility issues | Amplified errors |
Why This Makes AI “Incomplete” in Biotech
AI in biotech often:
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Predicts well but explains poorly
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Works in silico but fails in vivo
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Optimizes benchmarks rather than biology
Until data becomes:
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Larger
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Cleaner
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More standardized
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More integrative
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More accessible
AI will remain a powerful assistant, not an autonomous discovery engine, in biotechnology.
Written by Lorraine Maskell for FoodWrite Ltd 17/01/2026
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