Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

How Data are Classified: Categorical Data

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Hit-to-Lead Optimization of Energy-Coupling Factor (ECF) Transporter Inhibitors as Novel Antibiotic.

Journal of medicinal chemistry·2026

Same author

CustomKinFragLib: Filtering the Kinase-Focused Fragmentation Library.

ACS omega·2026

Same author

How to predict effective drug combinations - moving beyond synergy scores.

iScience·2025

Same author

Deconstruction of Dual-Site Tankyrase Inhibitors Provides Insights into Binding Energetics and Suggests Critical Hotspots for Ligand Optimization.

Journal of medicinal chemistry·2025

Same author

Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor binding.

bioRxiv : the preprint server for biology·2025

Same author

Prospective Evaluation of Structure-Based Simulations Reveal Their Ability to Predict the Impact of Kinase Mutations on Inhibitor Binding.

The journal of physical chemistry. B·2025

Same journal

Isolation of Mesenchymal Stem Cell-Derived Extracellular Vesicles.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Modeling Melanoma Immune Surveillance by CAR-T Cells in Human Skin Organoids.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Stepwise Optimization of a Matrigel-Based In Vitro Angiogenesis Assay for Reproducible and Quantifiable 2D-Tube Formation Using HUVECs.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Quantifying Mechanical Properties of Fresh Ovarian Tissue with Optical Brillouin Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

3D Chromatin Architecture During Early Development: New Methods and New Findings.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Metabolic Plasticity in Embryogenesis Throughout the Lens of NAD<sup></sup>.

Methods in molecular biology (Clifton, N.J.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 7, 2026

Quantification of Orofacial Phenotypes in Xenopus

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

Morphological Data Analysis: From Descriptor Development to Predictive Modeling.

Floriane Odje¹, Lisa-Marie Rolli¹, Andrea Volkamer²

¹Data Driven Drug Design, Center for Bioinformatics, Saarland University, Saarbrücken, Germany.

Methods in Molecular Biology (Clifton, N.J.)

|January 1, 2026

Summary

This summary is machine-generated.

This study details using cell painting assay images to create morphological fingerprints for computational analysis. These fingerprints enable machine learning models for compound similarity searches and predicting cellular activity in drug discovery.

Keywords:

Bioactivity prediction Cell painting assay Machine learning Morphological fingerprint Similarity search Structural fingerprint

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Related Experiment Videos

Last Updated: Jan 7, 2026

Quantification of Orofacial Phenotypes in Xenopus

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Area of Science:

Computational biology
Cheminformatics
Machine learning in drug discovery

Background:

Morphological fingerprints capture cellular phenotype from image data.
These fingerprints can be used for downstream computational analysis.
Machine learning models can leverage these fingerprints for predictive tasks.

Purpose of the Study:

To explore the computational processing of morphological fingerprints.
To demonstrate their application in compound similarity search and activity prediction.
To provide a guide for leveraging these fingerprints in drug discovery.

Main Methods:

Data preparation including ingestion, standardization, and normalization.
Computation of similarity searches using morphological and structural fingerprints.
Building and evaluating a machine learning model for activity prediction.

Main Results:

Successful application of morphological fingerprints for similarity searches.
Demonstration of machine learning model for predicting estrogen receptor activity.
Insights into model tuning, testing, and interpretation were provided.

Conclusions:

Morphological fingerprints are valuable for computational analysis in drug discovery.
These fingerprints facilitate compound similarity searches and activity prediction.
The chapter provides a practical guide using Jupyter notebooks for implementation.