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Isolation of Papillary and Reticular Fibroblasts from Human Skin by Fluorescence-activated Cell Sorting

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Transfer learning for predicting human skin sensitizers.

Chun-Wei Tung^1,2, Yi-Hui Lin³, Shan-Shan Wang³

¹Graduate Institute of Data Science, College of Management, Taipei Medical University, 172-1, Sec. 2, Keelung Rd., Taipei, 10675, Taiwan. cwtung@livemail.tw.

Archives of Toxicology

|February 27, 2019

Summary

This summary is machine-generated.

This study developed a new computational method using multitask learning to predict chemical skin sensitization risks. The approach improves prediction accuracy and coverage, aiding in safer chemical development and risk assessment.

Keywords:

Adverse outcome pathway Allergic contact dermatitis Alternative method ExtraTrees Multitask learning Skin sensitization

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Isolation and Culture of Adult Epithelial Stem Cells from Human Skin

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Published on: March 31, 2011

Area of Science:

Toxicology
Computational Chemistry
Drug Development

Background:

Computational methods are crucial for prioritizing chemicals for skin sensitization risk assessment.
Developing accurate predictive models is challenging due to limited human data for toxicological endpoints.
Small training datasets hinder model effectiveness, coverage, and accuracy.

Purpose of the Study:

To develop a robust computational method for predicting human skin sensitizers.
To improve the accuracy and coverage of chemical sensitization risk prediction models.
To leverage knowledge transfer from related tasks within the skin sensitization adverse outcome pathway (AOP).

Main Methods:

An ensemble tree-based multitask learning approach was employed.
Incorporated three relevant tasks within the skin sensitization AOP.
Focused on transferring shared knowledge to enhance the prediction of human sensitizers.

Main Results:

The developed method demonstrated significantly improved coverage and accuracy.
Performance surpassed three state-of-the-art prediction methods.
A user-friendly prediction server is now available for public use.

Conclusions:

The multitask learning method effectively enhances the prediction of chemical skin sensitization.
This approach offers a valuable tool for chemical risk assessment and safer alternative design.
The methodology is adaptable for developing prediction models for other toxicological endpoints using AOPs.