AI can help analyze experimental biology, but predictions and patterns must be tested against independent biological evidence.
Research context: educational synthesis. Verify primary studies, dates, methods and independent replications before attributing a specific breakthrough to this reporting week.
Sequencing, microscopy and assays generate observations affected by sampling, instruments and experimental conditions.
Check missing values, batch effects, mislabeled samples and contamination before fitting a model.
Split by biological specimen, patient or experimental batch when necessary to prevent data leakage.
A model may predict a phenotype without identifying the causal molecular mechanism.
Use appropriate controls, orthogonal assays and replicates to challenge model-generated hypotheses.
Record dataset versions, preprocessing steps, model parameters and code needed to reproduce results.
Report uncertainty and evaluate whether results transfer to new laboratories, instruments and populations.
Build an auditable chain from raw data to prediction, experiment, result and independent review.