Crop phenomics: current status and perspectives
10.3389/fpls.2019.00714 · doi-reference
Perceptual image quality assessment: a survey
10.1007/s11432-019-2757-1 · doi-reference
10.1016/j.tibtech.2015.12.013
10.1016/j.tibtech.2015.12.013 · doi-reference
10.1038/ncb2764
10.1038/ncb2764 · doi-reference
Unveiling nonessential gene deletions that confer significant morphological phenotypes beyond natural yeast strains
10.1186/1471-2164-15-932 · doi-reference
Multi-domain translation between single-cell imaging and sequencing data using autoencoders
10.1038/s41467-020-20249-2 · doi-reference
10.1002/1873-3468.14067
10.1002/1873-3468.14067 · doi-reference
10.1038/nmeth.4326
10.1038/nmeth.4326 · doi-reference
The FAIR guiding principles for scientific data management and stewardship
10.1038/sdata.2016.18 · doi-reference
10.1111/jmi.12184
10.1111/jmi.12184 · doi-reference
Zebra finches have style: nest morphology is repeatable and associated with experience
10.1016/j.isci.2023.108194 · doi-reference
10.1016/j.cels.2022.10.001
10.1016/j.cels.2022.10.001 · doi-reference
10.1146/annurev.genet.34.1.593
10.1146/annurev.genet.34.1.593 · doi-reference
10.1177/2472555218820805
10.1177/2472555218820805 · doi-reference
10.1042/bst20180391
10.1042/bst20180391 · doi-reference
High-content screening for quantitative cell biology
10.1016/j.tcb.2016.03.008 · doi-reference
In silico-labeled ghost cytometry
10.7554/elife.67660 · doi-reference
10.1039/d1cb00069a
10.1039/d1cb00069a · doi-reference
Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics
10.1371/journal.pcbi.1009061 · doi-reference
A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis
10.1038/s42003-022-03218-x · doi-reference
High-dimensional phenotyping to define the genetic basis of cellular morphology
10.1038/s41467-023-44045-w · doi-reference
Genetic interactions involving five or more genes contribute to a complex trait in yeast
10.1371/journal.pgen.1004324 · doi-reference
10.3390/cells11020285
10.3390/cells11020285 · doi-reference
Global study of holistic morphological effectors in the budding yeast Saccharomyces cerevisiae
10.1186/s12864-018-4526-z · doi-reference
10.1002/cyto.a.23973
10.1002/cyto.a.23973 · doi-reference
10.1038/s41592-020-01018-x
10.1038/s41592-020-01018-x · doi-reference
CellProfiler 4: improvements in speed, utility and usability
10.1186/s12859-021-04344-9 · doi-reference
Generalized additive models for location scale and shape (GAMLSS) in R
10.18637/jss.v023.i07 · doi-reference
Deep neural network models for computational histopathology: A survey
10.1016/j.media.2020.101813 · doi-reference
10.1073/pnas.0807038105
10.1073/pnas.0807038105 · doi-reference
Patterns of basal signaling heterogeneity can distinguish cellular populations with different drug sensitivities
10.1038/msb.2010.22 · doi-reference
Repurposing high-throughput image assays enables biological activity prediction for drug discovery
10.1016/j.chembiol.2018.01.015 · doi-reference
Interactive notebooks: Sharing the code
10.1038/515151a · doi-reference
mclust 5: clustering, classification and density estimation using Gaussian finite mixture models
10.32614/rj-2016-021 · doi-reference
10.1126/science.abj3013
10.1126/science.abj3013 · doi-reference
Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review
10.1016/j.ejca.2021.10.007 · doi-reference
Cross-talk between Rho and Rac GTPases drives deterministic exploration of cellular shape space and morphological heterogeneity
10.1098/rsob.130132 · doi-reference
A review of feature selection techniques in bioinformatics
10.1093/bioinformatics/btm344 · doi-reference
Scientific community image forum: a discussion forum for scientific image software
10.1371/journal.pbio.3000340 · doi-reference
Capturing single-cell heterogeneity via data fusion improves image-based profiling
10.1038/s41467-019-10154-8 · doi-reference
