首页|Perspectives on benchmarking foundation models for network biology
Perspectives on benchmarking foundation models for network biology
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维普
Transfer learning has revolutionized fields including natural language un-derstanding and computer vision by leveraging large-scale general data-sets to pretrain models with foundational knowledge that can then be transferred to improve predictions in a vast range of downstream tasks.More recently,there has been a growth in the adoption of transfer learning approaches in biological fields,where models have been pretrained on massive amounts of biological data and employed to make predictions in a broad range of biological applications.However,unlike in natural language where humans are best suited to evaluate models given a clear under-standing of the ground truth,biology presents the unique challenge of being in a setting where there are a plethora of unknowns while at the same time needing to abide by real-world physical constraints.This perspective provides a discussion of some key points we should consider as a field in designing benchmarks for foundation models in network biology.
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