کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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5914784 | 1162754 | 2011 | 5 صفحه PDF | دانلود رایگان |
Many cyro-EM datasets are heterogeneous stemming from molecules undergoing conformational changes. The need to characterize each of the substrates with sufficient resolution entails a large increase in the data flow and motivates the development of more effective automated particle selection algorithms. Concepts and procedures from the machine-learning field are increasingly employed toward this end. However, a review of recent literature has revealed a discrepancy in terminology of the performance scores used to compare particle selection algorithms, and this has subsequently led to ambiguities in the meaning of claimed performance. In an attempt to curtail the perpetuation of this confusion and to disentangle past mistakes, we review the performance of published particle selection efforts with a set of explicitly defined performance scores using the terminology established and accepted within the field of machine learning.
Journal: Journal of Structural Biology - Volume 175, Issue 3, September 2011, Pages 348-352