Compound Classification Using Image‐Based Cellular Phenotypes

The Elements of Statistical Learning

Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works

Hierarchical Grouping to Optimize an Objective Function

Ward, J. H. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500845 · Cited by 21,341 · see citing works

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Lu, Z. Q. J. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,904 · see citing works

Applied Multivariate Statistical Analysis.

Johnson, A., & Wichern, D. W. (1988) · Biometrics · doi:10.2307/2531616 · Cited by 11,374 · see citing works

Microtubules as a target for anticancer drugs

Jordan, M. A., & Wilson, L. (2004) · Nature reviews. Cancer · doi:10.1038/nrc1317 · Cited by 4,534 · see citing works

Functional Discovery via a Compendium of Expression Profiles

Hughes, T. R., Marton, M. J., Jones, A. R., Roberts, C. J., Stoughton, R. B., Armour, C. D., Bennett, H. A., Coffey, E. M., Dai, H. A., He, Y. D., et al. (2000) · Cell · doi:10.1016/s0092-8674(00)00015-5 · Cited by 2,534 · see citing works

Small Molecule Inhibitor of Mitotic Spindle Bipolarity Identified in a Phenotype-Based Screen

Mayer, T. U., Kapoor, T. M., Haggarty, S. J., King, R. W., Schreiber, S. L., & Mitchison, T. J. (1999) · Science · doi:10.1126/science.286.5441.971 · Cited by 1,732 · see citing works

A gene expression database for the molecular pharmacology of cancer

Scherf, U., Ross, D. T., Waltham, M. C., Smith, L. H., Lee, J. K., Tanabe, L. K., Kohn, K. W., Reinhold, W. C., Myers, T. G., Andrews, D. T., et al. (2000) · Nature Genetics · doi:10.1038/73439 · Cited by 1,450 · see citing works

An Information-Intensive Approach to the Molecular Pharmacology of Cancer

Weinstein, J. N., Myers, T. G., O'Connor, P. M., Friend, S. H., Fornace, A. J., Kohn, K. W., Fojo, T., Bates, S. E., Rubinstein, L., Anderson, N. L., et al. (1997) · Science · doi:10.1126/science.275.5298.343 · Cited by 1,183 · see citing works

Multidimensional Drug Profiling By Automated Microscopy

Perlman, Z. E., Slack, M. D., Feng, Y., Mitchison, T. J., Wu, L. F., & Altschuler, S. J. (2004) · Science · doi:10.1126/science.1100709 · Cited by 660 · see citing works

Drug target validation and identification of secondary drug target effects using DNA microarrays

Marton, M. J., DeRisi, J. L., Bennett, H. A., Iyer, V. R., Meyer, M. R., Roberts, C. J., Stoughton, R. B., Burchard, J., Slade, D., Dai, H. A., et al. (1998) · Nature Medicine · doi:10.1038/3282 · Cited by 643 · see citing works

Latrunculins—novel marine macrolides that disrupt microfilament organization and affect cell growth: I. Comparison with cytochalasin D

Spector, I., Shochet, N. R., Blasberger, D., & Kashman, Y. (1989) · Cell Motility · doi:10.1002/cm.970130302 · Cited by 580 · see citing works

Applied Multivariate Statistical Analysis

(1999) · Technometrics · doi:10.1080/00401706.1999.10485612 · Cited by 555 · see citing works

Pharmaceutical innovation by the seven UK‐owned pharmaceutical companies (1964‐1985).

Prentis, R., Lis, Y., & Walker, Sr. (1988) · British Journal of Clinical Pharmacology · doi:10.1111/j.1365-2125.1988.tb03318.x · Cited by 471 · see citing works

A functional genomic analysis of cell morphology using RNA interference

Kiger, A. A., Baum, B., Jones, S. J. M., Jones, M. R., Coulson, A. R., Echeverri, C. J., & Perrimon, N. (2003) · Journal of Biology · doi:10.1186/1475-4924-2-27 · Cited by 425 · see citing works

10-nm filaments are induced to collapse in living cells microinjected with monoclonal and polyclonal antibodies against tubulin.

Blose, S. H., Meltzer, D. I., & Feramisco, J. R. (1984) · The Journal of Cell Biology · doi:10.1083/jcb.98.3.847 · Cited by 390 · see citing works

The roles of microtubule-based motor proteins in mitosis

Goshima, G., & Vale, R. D. (2003) · The Journal of Cell Biology · doi:10.1083/jcb.200303022 · Cited by 380 · see citing works

Parallel Chemical Genetic and Genome-Wide RNAi Screens Identify Cytokinesis Inhibitors and Targets

Eggert, U., Kiger, A. A., Richter, C., Perlman, Z. E., Perrimon, N., Mitchison, T. J., & Field, C. M. (2004) · PLoS Biology · doi:10.1371/journal.pbio.0020379 · Cited by 325 · see citing works

Small Molecules, Big Impact

Peterson, J. R., & Mitchison, T. J. (2002) · Chemistry & Biology · doi:10.1016/s1074-5521(02)00284-3 · Cited by 307 · see citing works

High content screening applied to large-scale cell biology

Abraham, V. (2003) · Trends in biotechnology · doi:10.1016/j.tibtech.2003.10.012 · Cited by 288 · see citing works

Model Assessment and Selection

Hastie, T., Tibshirani, R. J., & Friedman, J. H. (2008) · Springer series in statistics · doi:10.1007/978-0-387-84858-7_7 · Cited by 272 · see citing works

Informatics and Quantitative Analysis in Biological Imaging

Swedlow, J. R., Goldberg, I., Brauner, E., & Sorger, P. K. (2003) · Science · doi:10.1126/science.1082602 · Cited by 253 · see citing works

Dissecting cellular processes using small molecules: identification of colchicine-like, taxol-like and other small molecules that perturb mitosis

Haggarty, S. J., Mayer, T. U., Miyamoto, D. T., Fathi, R., King, R. W., Mitchison, T. J., & Schreiber, S. L. (2000) · Chemistry & Biology · doi:10.1016/s1074-5521(00)00101-0 · Cited by 251 · see citing works

An Unbiased Cell Morphology–Based Screen for New, Biologically Active Small Molecules

Tanaka, M., Bateman, R. L., Rauh, D., Vaisberg, E. A., Ramachandani, S., Zhang, C., Hansen, K. C., Burlingame, A. L., Trautman, J. K., Shokat, K. M., et al. (2005) · PLoS Biology · doi:10.1371/journal.pbio.0030128 · Cited by 229 · see citing works

Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro

Gunther, E. C., Stone, D. J., Gerwien, R. W., Bento, P., & Heyes, M. P. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1632587100 · Cited by 182 · see citing works

A guide to drug discovery: Making Better Drugs: Decision Gates in Non-Clinical Drug Development

Pritchard, J. F., Jurima‐Romet, M., Reimer, M. J., Mortimer, E., Rolfe, B., & Cayen, M. (2003) · Nature reviews. Drug discovery · doi:10.1038/nrd1131 · Cited by 153 · see citing works

Phenotypic Screening of Small Molecule Libraries by High Throughput Cell Imaging

Yarrow, J. C., Feng, Y., Perlman, Z. E., Kirchhausen, T. L., & Mitchison, T. J. (2003) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/138620703106298527 · Cited by 119 · see citing works

Switch-of-Function Mutants Based on Morphology Classification of Ras Superfamily Small GTPases

Heo, W. D., & Meyer, T. (2003) · Cell · doi:10.1016/s0092-8674(03)00315-5 · Cited by 110 · see citing works

Structural aspects of Golgi function

Polishchuk, R., & Mirоnоv, A. A. (2004) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-003-3353-8 · Cited by 90 · see citing works

Recruitment of cdc2 kinase by DNA topoisomerase II is coupled to chromatin remodeling

Escargueil, A. E., Plisov, S. Y., Składanowski, A., Borgne‐Sanchez, A., Meijer, L., Gorbsky, G. J., & Larsen, A. K. (2001) · The FASEB Journal · doi:10.1096/fj.00-0726fje · Cited by 26 · see citing works

The action of cytochalasin A on the in vitro polymerization of brain tubulin and muscle G‐actin

Himes, R. H., & Houston, L. L. (1976) · Journal of Supramolecular Structure · doi:10.1002/jss.400050109 · Cited by 26 · see citing works

Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes

Bray, M.-A., Singh, S., Han, H., Davis, C. T., Borgeson, B., Hartland, C., Kost-Alimova, M., Gustafsdottir, S. M., Gibson, C. C., & Carpenter, A. E. (2016) · Nature Protocols · doi:10.1038/nprot.2016.105 · Cited by 1,090 · see citing works

Data-analysis strategies for image-based cell profiling

Caicedo, J. C., Cooper, S., Heigwer, F., Warchal, S., Qiu, P., Molnar, C., Vasilevich, A. S., Barry, J. D., Bansal, H. S., Kraus, O., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4397 · Cited by 739 · see citing works

High content screening: seeing is believing

Zanella, F., Lorens, J. B., & Link, W. (2010) · Trends in biotechnology · doi:10.1016/j.tibtech.2010.02.005 · Cited by 391 · see citing works

Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning

Jones, T. R., Carpenter, A. E., Lamprecht, M. R., Moffat, J., Silver, S. J., Grenier, J. K., Castoreno, A. B., Eggert, U., Root, D. E., Golland, P., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0808843106 · Cited by 368 · see citing works

Multiplex Cytological Profiling Assay to Measure Diverse Cellular States

Gustafsdottir, S. M., Ljosa, V., Sokolnicki, K. L., Anthony Wilson, J., Walpita, D., Kemp, M. M., Petri Seiler, K., Carrel, H. A., Golub, T. R., Schreiber, S. L., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0080999 · Cited by 291 · see citing works

Increasing the Content of High-Content Screening: An Overview

Singh, S., Carpenter, A. E., & Genovesio, A. (2014) · SLAS DISCOVERY · doi:10.1177/1087057114528537 · Cited by 215 · see citing works

Comparison of Methods for Image-Based Profiling of Cellular Morphological Responses to Small-Molecule Treatment

Ljosa, V., Caie, P. D., ter Horst, R., Sokolnicki, K. L., Jenkins, E. L., Daya, S., Roberts, M. E., Jones, T. R., Singh, S., Genovesio, A., et al. (2013) · SLAS DISCOVERY · doi:10.1177/1087057113503553 · Cited by 183 · see citing works

Cell Form and Function: Interpreting and Controlling the Shape of Adherent Cells

Prasad, A., & Alizadeh, E. (2018) · Trends in biotechnology · doi:10.1016/j.tibtech.2018.09.007 · Cited by 135 · see citing works

Understanding mechanisms of toxicity: Insights from drug discovery research

Houck, K. A., & Kavlock, R. J. (2007) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2007.10.022 · Cited by 110 · see citing works

Enhanced CellClassifier: a multi-class classification tool for microscopy images

Misselwitz, B., Strittmatter, G., Periaswamy, B., Schlumberger, M. C., Samuel, R., Horváth, P., Kozak, K., & Hardt, W. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-30 · Cited by 101 · see citing works

Linking Phenotypes and Modes of Action Through High-Content Screen Fingerprints

Reisen, F., Sauty de Chalon, A., Pfeifer, M., Zhang, X., Gabriel, D., & Selzer, P. (2015) · ASSAY and Drug Development Technologies · doi:10.1089/adt.2015.656 · Cited by 76 · see citing works

Morphological profiling of small molecules

Ziegler, S., Sievers, S., & Waldmann, H. (2021) · Cell chemical biology · doi:10.1016/j.chembiol.2021.02.012 · Cited by 82 · see citing works

Aryl-phosphorus-containing flame retardants induce oxidative stress, the p53-dependent DNA damage response and mitochondrial impairment in A549 cells

Yuan, S., Han, Y., Ma, M., Rao, K., Wang, Z., Yang, R., Liu, Y., & Zhou, X. (2019) · Environmental Pollution · doi:10.1016/j.envpol.2019.03.109 · Cited by 68 · see citing works

High-Content Screening: A Decade of Evolution

Thomas, N. (2009) · SLAS DISCOVERY · doi:10.1177/1087057109353790 · Cited by 48 · see citing works

Aryl- and alkyl-phosphorus-containing flame retardants induced mitochondrial impairment and cell death in Chinese hamster ovary (CHO-k1) cells

Huang, C., Li, N., Yuan, S., Ji, X., Ma, M., Rao, K., & Wang, Z. (2017) · Environmental Pollution · doi:10.1016/j.envpol.2017.07.024 · Cited by 48 · see citing works

Reference compounds for characterizing cellular injury in high-content cellular morphology assays

Dahlin, J. L., Hua, B. K., Zucconi, B. E., Nelson, S. D., Singh, S., Carpenter, A. E., Shrimp, J. H., Lima‐Fernandes, E., Wawer, M. J., Chung, L. P. W., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36829-x · Cited by 42 · see citing works

Target identification by image analysis

Fetz, V., Prochnow, H., Brönstrup, M., & Sasse, F. (2016) · Natural Product Reports · doi:10.1039/c5np00113g · Cited by 43 · see citing works

Benchmarking of Multivariate Similarity Measures for High-Content Screening Fingerprints in Phenotypic Drug Discovery

Reisen, F., Zhang, X., Gabriel, D., & Selzer, P. M. (2013) · SLAS DISCOVERY · doi:10.1177/1087057113501390 · Cited by 41 · see citing works

Morphological Profiles of RNAi-Induced Gene Knockdown Are Highly Reproducible but Dominated by Seed Effects

Singh, S., Wu, X., Ljosa, V., Bray, M., Piccioni, F., Root, D. E., Doench, J. G., Boehm, J. S., & Carpenter, A. E. (2015) · PLoS ONE · doi:10.1371/journal.pone.0131370 · Cited by 39 · see citing works

CellDeathPred: a deep learning framework for ferroptosis and apoptosis prediction based on cell painting

Schorpp, K., Bessadok, A., Biibosunov, A., Rothenaigner, I., Strasser, S., Peng, T., & Hadian, K. (2023) · Cell Death Discovery · doi:10.1038/s41420-023-01559-y · Cited by 38 · see citing works

The opportunities of mining historical and collective data in drug discovery

Wassermann, A. M., Lounkine, E., Davies, J. W., Glick, M., & Camargo, L. M. (2015) · Drug Discovery Today · doi:10.1016/j.drudis.2014.11.004 · Cited by 34 · see citing works

Multiparametric Analysis of Screening Data: Growing Beyond the Single Dimension to Infinity and Beyond

Abraham, Y., Zhang, X., & Parker, C. N. (2014) · SLAS DISCOVERY · doi:10.1177/1087057114524987 · Cited by 32 · see citing works

Comparison of Multivariate Data Analysis Strategies for High-Content Screening

Kümmel, A., Selzer, P. M., Beibel, M., Gubler, H., Parker, C. N., & Gabriel, D. (2011) · SLAS DISCOVERY · doi:10.1177/1087057110395390 · Cited by 32 · see citing works

Increasing the Robustness and Validity of RNAi Screens

Haney, S. A. (2007) · Pharmacogenomics · doi:10.2217/14622416.8.8.1037 · Cited by 32 · see citing works

Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screenPreprint

Nir, O., Bakal, C., Perrimon, N., & Berger, B. (2010) · Genome Research · doi:10.1101/gr.100248.109 · Cited by 28 · see citing works

An Infrastructure for High‐Throughput Microscopy: Instrumentation, Informatics, and Integration

Vaisberg, E. A., Lenzi, D., Hansen, R. L., Keon, B. H., & Finer, J. T. (2006) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(06)14026-4 · Cited by 26 · see citing works

Development of a High-Content Screening Assay Panel to Accelerate Mechanism of Action Studies for Oncology Research

Towne, D. L., Nicholl, E. E., Comess, K. M., Galasinski, S. C., Hajduk, P. J., & Abraham, V. C. (2012) · SLAS DISCOVERY · doi:10.1177/1087057112450050 · Cited by 22 · see citing works

A statistical framework for high-content phenotypic profiling using cellular feature distributions

Pearson, Y. E., Kremb, S., Butterfoss, G. L., Xie, X., Fahs, H., & Gunsalus, K. C. (2022) · Communications Biology · doi:10.1038/s42003-022-04343-3 · Cited by 19 · see citing works

Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen

Boyd, J. C., Pinheiro, A., Nery, E. D., Reyal, F., & Walter, T. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz774 · Cited by 14 · see citing works

Expanding the repertoire of RNA interference screens for developing new anticancer drug targets

Haney, S. A. (2007) · Expert Opinion on Therapeutic Targets · doi:10.1517/14728222.11.11.1429 · Cited by 13 · see citing works

The Future of Computational Chemogenomics

Jacoby, E., & Brown, J. B. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8639-2_15 · Cited by 10 · see citing works

Recent advances in quantitative high throughput and high content data analysis

Moutsatsos, I. K., & Parker, C. N. (2016) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2016.1154036 · Cited by 9 · see citing works

Self-supervised deep learning uncovers the semantic landscape of drug-induced latent mitochondrial phenotypesPreprint

Natekar, P., Wang, Z., Arora, M., Hakozaki, H., & Schöneberg, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.13.557636 · Cited by 8 · see citing works

High‐Content Image‐Based Screening and Deep Learning for the Detection of Anti‐Inflammatory Drug Leads

Lau, T. A., Mair, E., Rabbitts, B. M., Lohith, A., & Lokey, R. S. (2023) · ChemBioChem · doi:10.1002/cbic.202300136 · Cited by 7 · see citing works

Drug response in the era of precision medicine: A methodological review

Okyere, D., Bravo-Merodio, L., Xu, Y., Guan, X., Parkhi, D., Gkoutos, G. V., & Acharjee, A. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.11.067 · Cited by 8 · see citing works

The Interweaving of Cheminformatics and HTS

Kümmel, A., & Parker, C. N. (2010) · Methods in molecular biology · doi:10.1007/978-1-60761-839-3_17 · Cited by 6 · see citing works

High-Content Screening Approaches That Minimize Confounding Factors in RNAi, CRISPR, and Small Molecule Screening

Haney, S. A. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7357-6_8 · Cited by 6 · see citing works

Comparative toxicity study of a novel non-ionic surfactant, vanillin ethoxylates, and nonylphenol ethoxylates in Chinese hamster ovary cells in vitro

Yuan, S., Ji, X., Ma, M., Ding, F., Rao, K., Wang, Z., Yang, R., & Liu, Y. (2019) · Journal of Environmental Sciences · doi:10.1016/j.jes.2019.02.029 · Cited by 5 · see citing works

Computational methods to support high-content screening: from compound selection and data analysis to postulating target hypotheses

Kümmel, A., Gabriel, D., Parker, C. N., & Bender, A. (2008) · Expert Opinion on Drug Discovery · doi:10.1517/17460440802586434 · Cited by 5 · see citing works

Macrophage Cytological Profiling and Anti-Inflammatory Drug Discovery

Lau, T. A., Bray, W. M., & Lokey, R. S. (2019) · Assay and Drug Development Technologies · doi:10.1089/adt.2018.894 · Cited by 4 · see citing works

Assessment of cluster analysis of elastic light scatter profiles for the identification of foodborne Bacteria

Bari, S., Zhang, Y., Patsekin, V., Robinson, J. P., Rajwa, B. P., Gehring, A., Lindsay, J. A., Kulasiri, D., & On, S. L. W. (2025) · Systematic and Applied Microbiology · doi:10.1016/j.syapm.2025.126641 · Cited by 2 · see citing works

High-content cellular screen image analysis benchmark studyPreprint

Bray, M., de Weck, A., Durand, E., Fang, J. C., Gabriel, D., Janssens, R., Moutsatsos, I. K., Spiegel, S., & Zhang, X. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.15.491989 · Cited by 2 · see citing works

Contrastive learning of dynamic processing body formation reveals undefined mechanisms of approved compounds

Shen, D., Zhu, Q., Pang, X., Pan, D., Amador, M. C. C., Zhang, M., Li, Y., Sun, Z., Cao, Z., Yang, X., et al. (2026) · iScience · doi:10.1016/j.isci.2026.114866 · Cited by 1 · see citing works

Extracting biomedically important information from large, automated imaging experiments

Carpenter, A. E. (2011) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2011.5872737 · Cited by 1 · see citing works

Morphology and Phenotype of Peripheral Erythrocytes of Fish: A Rapid Screening of Images by Using Software

Talapatra, S. N., Mitra, P., & Swarnakar, S. (2016) · International Letters of Natural Sciences · doi:10.18052/www.scipress.com/ilns.54.27 · Cited by 1 · see citing works

Reference compounds for characterizing cellular injury in high-content cellular morphology assaysPreprint

Dahlin, J. L., Hua, B. K., Zucconi, B. E., Nelson, S. D., Singh, S., Carpenter, A. E., Shrimp, J. H., Lima‐Fernandes, E., Wawer, M. J., Chung, L. P. W., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.12.499781 · Cited by 1 · see citing works

PB-scope: Contrastive learning of dynamic processing body formation reveals undefined mechanisms of approved compoundsPreprint

Shen, D., Zhu, Q., Pang, X., Yang, X., Pan, D., Zhang, M., Li, Y., Sun, Z., Fang, L., Chen, W., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.14.659731 · Cited by 1 · see citing works

comparison of Multivariate d ata Analysis Strategies for High- c ontent Screening

Kü, A., & SeLzer, L. (2011)

MEASUREMENT OF HAEMOCYTES OF GASTROPODS THROUGH AN AUTOMATED IMAGE SCREENING APPROACH BY USING IMAGE PROCESSING TOOL

Ghosh, A., & Chatterjee, T. K. (2022) · Journal of Advanced Scientific Research · doi:10.55218/jasr.202213214

Phenotypic Screening

Macolino-Kane, C. M., Ciallella, J. R., Lipinski, C. A., & Reaume, A. G. (2017) · CRC Press eBooks · doi:10.4324/9781315373669-7

Deep learning for computational phenotyping in cell-based assays

Boyd, J. (2020) · theses.fr (ABES) · doi:10.70675/140b4351zf0f1z4c9az9d06z54438ee98410 · Cited by 1 · see citing works

Robust Classification of Small-Molecule Mechanism of Action Using a Minimalist High-Content Microscopy Screen and Multidimensional Phenotypic Trajectory Analysis

Twarog, N. R., Low, J. A., Currier, D. G., Miller, G., Chen, T., & Shelat, A. A. (2016) · PLOS ONE · doi:10.1371/journal.pone.0149439 · Cited by 16 · see citing works

Multiplex Cytological Profiling Assay to Measure Diverse Cellular States

Gustafsdottir, S. M., Ljosa, V., Sokolnicki, K. L., Anthony Wilson, J., Walpita, D., Kemp, M. M., Petri Seiler, K., Carrel, H. A., Golub, T. R., Schreiber, S. L., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0080999 · Cited by 291 · see citing works

The Cell Painting Assay as a Screening Tool for the Discovery of Bioactivities in New Chemical Matter

Pahl, A., & Sievers, S. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8891-4_6 · Cited by 37 · see citing works

Large-scale cytological profiling for functional analysis of bioactive compounds

Woehrmann, M. H., Bray, W. M., Durbin, J. K., Nisam, S. C., Michael, A. K., Glassey, E., Stuart, J. M., & Lokey, R. S. (2013) · Molecular BioSystems · doi:10.1039/c3mb70245f · Cited by 62 · see citing works

An integrated one-step system to extract, analyze and annotate all relevant information from image-based cell screening of chemical libraries

Rabal, O., Link, W., Serelde, B. G., Bischoff, J. R., & Oyarzábal, J. (2010) · Molecular BioSystems · doi:10.1039/b919830j · Cited by 13 · see citing works