Classification of crystallization outcomes using deep convolutional neural networks

被引：62

作者：

Bruno, Andrew E. ^{[1
]}

Charbonneau, Patrick ^{[2
,3
]}

Newman, Janet ^{[4
]}

Snell, Edward H. ^{[5
,6
]}

So, David R. ^{[7
]}

Vanhoucke, Vincent ^{[7
]}

Watkins, Christopher J. ^{[8
]}

Williams, Shawn ^{[9
]}

Wilson, Julie ^{[10
]}

机构：

[1] SUNY Buffalo, Ctr Computat Res, Buffalo, NY USA

[2] Duke Univ, Dept Chem, Durham, NC 27706 USA

[3] Duke Univ, Dept Phys, Durham, NC 27706 USA

[4] CSIRO, Collaborat Crystallisat Ctr, Parkville, Vic, Australia

[5] Hauptman Woodward Med Res Inst, Buffalo, NY USA

[6] SUNY Buffalo, Dept Mat Design & Innovat, Buffalo, NY USA

[7] Google Inc, Google Brain, Mountain View, CA 94043 USA

[8] CSIRO, IM&T Sci Comp, Clayton, Vic, Australia

[9] GlaxoSmithKline Inc, Platform Technol & Sci, Collegeville, PA USA

[10] Univ York, Dept Math, York, N Yorkshire, England

来源：

PLOS ONE | 2018年 / 13卷 / 06期

基金：

美国国家科学基金会;

关键词：

PROTEIN-CRYSTALLIZATION; VISUAL ANALYSIS; TRAINING SET; IMAGES; TEXTURE;

D O I：

10.1371/journal.pone.0198883

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algorithms are trained and tested on different parts of this data set. We find that more than 94% of the test images can be correctly labeled, irrespective of their experimental origin. Because crystal recognition is key to high-density screening and the systematic analysis of crystallization experiments, this approach opens the door to both industrial and fundamental research applications.

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页数：16

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