Active Learning Strategies for Phenotypic Profiling of High-Content Screens.
about
High content screening in chemical biology: overview and main challengesAccelerating glioblastoma drug discovery: Convergence of patient-derived models, genome editing and phenotypic screeningFindFoci: a focus detection algorithm with automated parameter training that closely matches human assignments, reduces human inconsistencies and increases speed of analysisDevelopment of the Theta Comparative Cell Scoring Method to Quantify Diverse Phenotypic Responses Between Distinct Cell Types.Machine learning applications in cell image analysis.Next-generation phenotypic screening.Knowledge from Small-Molecule Screening and Profiling Data.Environmental properties of cells improve machine learning-based phenotype recognition accuracy.
P2860
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P2860
Active Learning Strategies for Phenotypic Profiling of High-Content Screens.
description
2014 nî lūn-bûn
@nan
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
2014年论文
@zh
2014年论文
@zh-cn
name
Active Learning Strategies for Phenotypic Profiling of High-Content Screens.
@en
Active Learning Strategies for Phenotypic Profiling of High-Content Screens.
@nl
type
label
Active Learning Strategies for Phenotypic Profiling of High-Content Screens.
@en
Active Learning Strategies for Phenotypic Profiling of High-Content Screens.
@nl
prefLabel
Active Learning Strategies for Phenotypic Profiling of High-Content Screens.
@en
Active Learning Strategies for Phenotypic Profiling of High-Content Screens.
@nl
P2860
P356
P1476
Active Learning Strategies for Phenotypic Profiling of High-Content Screens.
@en
P2093
Kevin Smith
Peter Horvath
P2860
P304
P356
10.1177/1087057114527313
P577
2014-03-18T00:00:00Z