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Related Experiment Video

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Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence

Published on: April 12, 2024

Rule-based classification models of molecular autofluorescence.

Bo-Han Su¹, Yi-Shu Tu, Olivia A Lin

¹Department of Computer Science and Information Engineering and ‡Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University , No. 1 Sec. 4, Roosevelt Road, Taipei, Taiwan 106.

Journal of Chemical Information and Modeling

|January 28, 2015

Summary

Autofluorescent compounds interfere with fluorescence-based high-throughput screening (HTS). This study developed in silico prediction models to identify autofluorescence, reducing HTS costs and improving accuracy.

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Autofluorescence Imaging to Evaluate Cellular Metabolism

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Published on: November 15, 2021

Area of Science:

Computational Chemistry
Biochemistry
Drug Discovery

Background:

Fluorescence-based detection is crucial for high-throughput screening (HTS) assays.
Autofluorescent compounds cause false positives, complicating HTS analysis.
Prescreening for autofluorescence is time-consuming and costly.

Purpose of the Study:

To develop in silico prediction models for autofluorescence.
To reduce time and cost associated with prescreening autofluorescent compounds.
To improve the reliability of fluorescence-based HTS assays.

Main Methods:

Developed five prediction models for specific fluorophores (Alexa Fluor 350, AFC, Alexa Fluor 488, Rhodamine, Texas Red).
Utilized C5.0 rule-based classification and PubChem 2D chemical structure fingerprints.
Applied oversampling and undersampling strategies to handle imbalanced datasets.

Main Results:

Achieved 97% average accuracy on training sets and 92% on testing sets.
Validated models using five external datasets.
Identified 14 key structural features for predicting autofluorescence.

Conclusions:

In silico models effectively predict autofluorescence in HTS assays.
The developed models can streamline HTS workflows by reducing false positives.
This approach offers a cost-effective solution for managing autofluorescent interference.