Golden eagle optimized CONV-LSTM and non-negativity-constrained autoencoder to support spatial and temporal features in cancer drug response prediction

Wesam Ibrahim Hajim ^1,2, Suhaila Zainudin ², Kauthar Mohd Daud ²

⁰Department of Applied Geology, College of Sciences, University of Tikrit, Tikrit, Salah ad Din, Iraq.

Peerj. Computer Science +

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February 3, 2025

View abstract on PubMed

Summary

This study introduces a novel Non-Negativity-Constrained Auto Encoder (NNCAE) and Golden Eagle Optimization-based Convolutional Long Short-Term Memory (GEO-Conv-LSTM) network for drug response prediction. The approach effectively handles noisy, imbalanced data, achieving high accuracy in predicting drug efficacy.

Area Of Science

Computational biology
Bioinformatics
Machine learning in drug discovery

Background

Drug Response Prediction (DRP) utilizes machine learning (ML) and deep learning (DL) with genomic data.
DL models excel at feature learning but often ignore prior biological knowledge (e.g., pathway data) due to noisy, multidimensional datasets.
Noise and class imbalance in DRP datasets reduce accuracy, increase prediction time, and limit applicability.

Purpose Of The Study

To address noise and class imbalance in DRP datasets.
To enhance feature learning and extraction for improved drug response prediction.
To develop a robust hybrid deep learning model for DRP.

Main Methods

Application of Non-Negativity-Constrained Auto Encoder (NNCAE) for noise reduction and class balancing.
Integration of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) into a hybrid classifier.
Parameter optimization of the GEO-Conv-LSTM model using the Golden Eagle Optimization (GEO) algorithm.

Main Results

The NNCAE-GEO-Conv-LSTM approach achieved high prediction accuracies of 96.99% and 97.79% on two large GDSC datasets.
Demonstrated significant reduction in processing time and error rates compared to existing methods.
Successfully learned vital hidden features from pre-processed, balanced, and noise-removed data.

Conclusions

The proposed NNCAE-GEO-Conv-LSTM model offers a powerful and efficient solution for drug response prediction.
Effective handling of data challenges (noise, imbalance) is crucial for accurate DRP.
This hybrid deep learning approach shows great promise for advancing personalized medicine through accurate drug efficacy prediction.

Keywords:

Convolutional long short-term memory Deep learning Drug response prediction Golden eagle optimization Non-negativity-constrained autoencoder Spatial and temporal features