INTERPRETABLE MACHINE LEARNING Mining for informative signals in biological sequences

被引：0

作者：

Alaa, Ahmed M. ^{[1
,2
]}

机构：

[1] Broad Inst MIT & Harvard, Merkin Bldg, Cambridge, MA 02142 USA

[2] MIT, Cambridge, MA 02139 USA

来源：

NATURE MACHINE INTELLIGENCE | 2022年 / 4卷 / 08期

关键词：

PREDICTION;

D O I：

10.1038/s42256-022-00524-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Deep learning models for sequential data can be trained to make accurate predictions from large biological datasets. New tools from computer vision and natural language processing can help us make these models interpretable to biologists.

引用

页码：665 / 666

页数：2

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