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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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MORF: Multi-view oblique random forest for hepatotoxicity prediction.

Binsheng Sui¹, Qingzhuo He², Bowei Yan³

¹Department of Digital Media, Xiamen University, Xiamen 361005, China.

|February 5, 2025

Summary

This study introduces a novel Multi-View Oblique Random Forest (MORF) for accurate hepatotoxicity prediction. The MORF effectively utilizes diverse feature types, enhancing drug development reliability.

Keywords:

Bioinformatics Cancer

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Area of Science:

Computational chemistry and toxicology
Machine learning in drug discovery

Background:

Hepatotoxicity prediction is critical for safe drug development.
Existing methods may not fully leverage multi-type feature data.

Purpose of the Study:

To propose a novel Multi-View Oblique Random Forest (MORF) for enhanced hepatotoxicity prediction.
To address the challenge of integrating diverse feature types in toxicity assessment.

Main Methods:

Developed a Multi-View Oblique Random Forest (MORF) model.
Utilized Householder transformation for inclined cut hyperplanes within each feature view.
Designed two Multi-View Oblique Decision Tree (ODT) algorithms (ODT-N, ODT-R) as base learners.

Main Results:

MORF algorithms demonstrated effective utilization of information from different feature views.
Experimental comparisons validated the performance of the proposed MORF approach.

Conclusions:

The proposed MORF provides an effective and reliable method for hepatotoxicity prediction.
This approach enhances the integration of multi-view data in toxicological assessments.