Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms.

Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms.
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DOI:
10.1038/s41592-021-01283-4
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发表时间:
2021-10
期刊:
影响因子:
48
通讯作者:
Sorger PK
Sorger PK
中科院分区:
生物学1区
文献类型:
--
作者:
AlQuraishi M;Sorger PK

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Deep learning using neural networks relies on a class of machine learnable models constructed using “differentiable programs.” These programs can combine mathematical equations specific to a particular domain of natural science with general-purpose machine-learnable components trained on experimental data. Such programs are having a growing impact on molecular and cellular biology. In this Perspective, we describe an emerging “differentiable biology” in which phenomena ranging from the small and specific (e.g. one experimental assay) to the broad and complex (e.g. protein folding) can be modeled effectively and efficiently, often by exploiting knowledge about basic natural phenomena to overcome the limitations of sparse, incomplete, and noisy data. By distilling differentiable biology into a small set of conceptual primitives and illustrative vignettes, we show how it can help address long-standing challenges in integrating multi-modal data from diverse experiments across biological scales. This promises to benefit fields as diverse as biophysics and functional genomics.
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