Quantum-classical Hybrid Machine Learning (QHML) models are recognized for their robust performance and high generalization ability even for relatively small datasets. These qualities offer unique advantages for anti-cancer drug response prediction, where the number of available samples is typically small. However, such hybrid models appear to be very sensitive to the data encoding used at the interface of a neural network and a quantum circuit, with suboptimal choices leading to stability issues. To address this problem, we propose a novel strategy that uses a normalization function based on a moderated gradient version of the $\tanh$. This method transforms the outputs of the neural networks without concentrating them at the extreme value ranges. Our idea was evaluated on a dataset of gene expression and drug response measurements for various cancer cell lines, where we compared the prediction performance of a classical deep learning model and several QHML models. These results confirmed that QHML performed better than the classical models when data was optimally normalized. This study opens up new possibilities for biomedical data analysis using quantum computers.

Researchers from the Pohang University of Science and Technology (POSTECH) in South Korea say they have successfully increased the accuracy of anti-cancer drug response predictions by using data closest to a human being's response. The team developed this machine learning technique through algorithms that learn transcriptome information from artificial organoids derived from actual patients instead of animal models. The team, led by Sanguk Kim, PhD, in the life sciences department, published its findings "Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients" in Nature Communications "Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational biomarkers from preclinical models. Here, we present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data derived from three-dimensional organoid culture models," write the investigators. "The biomarkers identified by our approach accurately predict the drug responses of 114 colorectal cancer patients treated with 5-fluorouracil and 77 bladder cancer patients treated with cisplatin.

The team developed this machine learning technique through algorithms that learn transcriptome information from artificial organoids derived from …