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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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THPep: A machine learning-based approach for predicting tumor homing peptides.

Watshara Shoombuatong¹, Nalini Schaduangrat¹, Reny Pratiwi²

¹Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.

Computational Biology and Chemistry

|June 1, 2019

Summary

Researchers developed THPep, a computational tool to predict tumor homing peptides. This method enhances anti-cancer drug specificity, offering a promising advancement in cancer therapy development.

Keywords:

Classification Machine learning Random forest Therapeutic peptide Tumor homing peptide

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Area of Science:

Biotechnology
Computational Biology
Oncology

Background:

Current anti-cancer drugs often lack specificity for tumor cells, leading to side effects.
Tumor homing peptides are emerging as targeted therapeutic agents for cancer treatment.
The increasing number of therapeutic peptides necessitates efficient computational prediction tools.

Purpose of the Study:

To develop an effective and interpretable computational model for predicting tumor homing peptides.
To analyze tumor homing peptides using sequence-based features and machine learning.
To provide a user-friendly web server for experimental scientists.

Main Methods:

A sequence-based approach named THPep was developed.
An interpretable random forest classifier was employed.
Amino acid composition, dipeptide composition, and pseudo amino acid composition were utilized as features.

Main Results:

THPep achieved an overall accuracy of 90.13% on an independent test set.
A Matthews correlation coefficient of 0.76 was obtained.
The method demonstrated superior performance compared to existing approaches.

Conclusions:

THPep is a highly effective and interpretable tool for predicting tumor homing peptides.
The developed model holds significant potential for advancing cancer therapy.
A publicly accessible web server (http://codes.bio/thpep/) is available for researchers.