Perspectives on benchmarking foundation models for network biology  

作　　者：Christina V.Theodoris 

机构地区：[1]Gladstone Institute of Cardiovascular Disease,San Francisco,California,USA [2]Gladstone Institute of Data Science and Biotechnology,San Francisco,California,USA [3]Department of Pediatrics,University of California,San Francisco,San Francisco,California,USA

出　　处：《Quantitative Biology》2024年第4期335-338,共4页定量生物学（英文版）

摘　　要：Transfer learning has revolutionized fields including natural language understanding and computer vision by leveraging large-scale general datasets 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 understanding 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.

关 键 词：benchmarking strategy foundation models network biology transfer learning 

分 类 号：Q811.4[生物学—生物工程]