AI models struggle with genomic complexity beyond raw sequence data
Current AI systems lack sufficient training data and architectural sophistication to decode the full complexity of human genomics, where regulatory elements, epigenetic markers, and non-coding sequences operate in ways that static datasets cannot capture. This gap exposes a fundamental limitation in machine learning's ability to model biological systems where context and dynamism matter as much as sequence. For biotech and healthcare AI developers, the implication is clear: scaling model size alone won't bridge the genomics frontier without new data collection strategies and domain-specific inductive biases tailored to cellular biology.65
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