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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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UNNT: A novel Utility for comparing Neural Net and Tree-based models.

Vineeth Gutta¹, Satish Ranganathan Ganakammal², Sara Jones²

¹Department of Computer & Information Sciences, University of Delaware, Newark, Delaware, United States of America.

Plos Computational Biology

|April 29, 2024

Summary

Tree-based models like XGBoost show improved accuracy for cancer drug response prediction compared to deep learning models. An open-source library, UNNT, simplifies comparing these machine learning approaches.

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Area of Science:

Computational biology
Machine learning in oncology

Background:

Deep learning (DL) is increasingly used in cancer research, particularly for drug response prediction.
Advances in data generation, algorithms, and computing power accelerate DL adoption.
Comparing different machine learning models for complex tasks like drug response is challenging.

Purpose of the Study:

To evaluate tree-based models against deep learning models for cancer drug response prediction.
To develop an accessible tool for comparing neural network and tree-based models.
To demonstrate the utility of XGBoost over Convolutional Neural Networks (CNNs) for single drug response problems.

Main Methods:

Exploration of tree-based models, specifically XGBoost (eXtreme Gradient Boosting).
Comparison of XGBoost performance against deep learning models like CNNs.
Development of an open-source library, UNNT (A novel Utility for comparing Neural Net and Tree-based models), for model comparison.
Application to cancer drug response datasets.

Main Results:

XGBoost demonstrated superior accuracy for single drug response prediction compared to CNNs.
The UNNT library facilitates easier training and comparison of CNNs and XGBoost.
The developed approach is applicable beyond cancer research, including domains like chemistry.

Conclusions:

Tree-based models, such as XGBoost, offer advantages over deep learning models for specific cancer drug response tasks.
The UNNT library provides a valuable, open-source resource for researchers comparing different machine learning architectures.
This work highlights the potential for improved predictive modeling in drug discovery and related scientific fields.