当前位置: X-MOL 学术Bioinformatics › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Deep-learning method for data association in particle tracking.
Bioinformatics ( IF 4.4 ) Pub Date : 2020-07-06 , DOI: 10.1093/bioinformatics/btaa597
Yao Yao 1 , Ihor Smal 1, 2 , Ilya Grigoriev 3 , Anna Akhmanova 3 , Erik Meijering 1, 4
Affiliation  

Biological studies of dynamic processes in living cells often require accurate particle tracking as a first step toward quantitative analysis. Although many particle tracking methods have been developed for this purpose, they are typically based on prior assumptions about the particle dynamics, and/or they involve careful tuning of various algorithm parameters by the user for each application. This may make existing methods difficult to apply by non-expert users and to a broader range of tracking problems. Recent advances in deep-learning techniques hold great promise in eliminating these disadvantages, as they can learn how to optimally track particles from example data.

中文翻译:

粒子跟踪中数据关联的深度学习方法。

活细胞中动态过程的生物学研究通常需要精确的颗粒追踪,这是进行定量分析的第一步。尽管已经为此目的开发了许多粒子跟踪方法,但是它们通常基于关于粒子动力学的先前假设,和/或它们涉及用户针对每种应用对各种算法参数的仔细调整。这可能会使现有方法难以为非专业用户所用,并且难以解决更广泛的跟踪问题。深度学习技术的最新进展有望消除这些缺点,因为它们可以学习如何从示例数据中最佳跟踪粒子。
更新日期:2020-07-06
down
wechat
bug