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AI-Driven Biology Is Only as Good as the Data Beneath It

Jonathan Jacobs and Patrick Boyle· 25 septembre 2026

Opinion | The biotech and pharma industries are pouring billions of dollars into AI-driven drug discovery and development. But the genomic data used to train AI models are often incomplete, contain errors, or lack contextual information — a quality issue that threatens to slow the pace of biologica…

Résumé

AI in biology basics

Artificial Intelligence (AI) is increasingly used in biology to analyze vast amounts of data, predict outcomes, and accelerate discoveries.

AI systems rely on machine learning (ML), a type of AI where algorithms learn patterns from data without explicit programming.

In biology, ML models are trained on datasets containing genetic sequences, protein structures, or clinical records to identify correlations or make predictions.

For example, AI can help predict how a patient might respond to a drug based on their genetic makeup.

However, the effectiveness of AI in biology hinges entirely on the quality and completeness of the underlying data.

If the data is biased, incomplete, or unrepresentative, the AI’s predictions may be unreliable or even harmful.

The article highlights that AI-driven biology is only as robust as the data it is built upon, emphasizing the critical role of data curation in scientific research.

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