Literature DB >> 28942588

The emergence of dynamic phenotyping.

Daniel Ruderman1.   

Abstract

Mesh：

Year:  2017        PMID： 28942588     DOI： 10.1007/s10565-017-9413-x

Source DB:  PubMed          Journal:  Cell Biol Toxicol        ISSN： 0742-2091            Impact factor:   6.691


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  3 in total

1.  Trajectories, bifurcations, and pseudo-time in large clinical datasets: applications to myocardial infarction and diabetes data.

Authors:  Sergey E Golovenkin; Jonathan Bac; Alexander Chervov; Evgeny M Mirkes; Yuliya V Orlova; Emmanuel Barillot; Alexander N Gorban; Andrei Zinovyev
Journal:  Gigascience       Date:  2020-11-25       Impact factor: 6.524

Review 2.  Emerging machine learning approaches to phenotyping cellular motility and morphodynamics.

Authors:  Hee June Choi; Chuangqi Wang; Xiang Pan; Junbong Jang; Mengzhi Cao; Joseph A Brazzo; Yongho Bae; Kwonmoo Lee
Journal:  Phys Biol       Date:  2021-06-17       Impact factor: 2.959

Review 3.  Toward dynamic phenotypes and the scalable measurement of human behavior.

Authors:  Laura Germine; Roger W Strong; Shifali Singh; Martin J Sliwinski
Journal:  Neuropsychopharmacology       Date:  2020-07-06       Impact factor: 8.294
  3 in total

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