Quantum processor-inspired machine learning in the biomedical sciences

• Published in: Patterns (New York, N.Y.) Vol. 2; no. 6; p. 100246
• Main Authors: Li, Richard Y., Gujja, Sharvari, Bajaj, Sweta R., Gamel, Omar E., Cilfone, Nicholas, Gulcher, Jeffrey R., Lidar, Daniel A., Chittenden, Thomas W.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 11.06.2021; Elsevier
• Subjects: cancer genomics; machine learning; The Cancer Genome Atlas; cancer genomics; DSML 2: Proof-of-Concept: Data science output has been formulated, implemented, and tested for one domain/problem; machine learning; The Cancer Genome Atlas
• ISSN: 2666-3899; 2666-3899
• DOI: 10.1016/j.patter.2021.100246

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired by recent advances in physical quantum processors, we evaluated several unconventional machine-learning (ML) strategies on actual human tumor data, namely “Ising-type” methods, whose objective function is formulated identical to simulated annealing and quantum annealing. We show the efficacy of multiple Ising-type ML algorithms for classification of multi-omics human cancer data from The Cancer Genome Atlas, comparing these classifiers to a variety of standard ML methods. Our results indicate that Ising-type ML offers superior classification performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing approaches in the biomedical sciences. •Ising-type algorithms perform better with small datasets•There is less overfitting with Ising-type algorithms•Ising-type algorithms are amenable for drug discovery•Ising-type algorithms are useful for assessment of drug efficacy Advances in sequencing technology, leading to an ever-increasing volume and variety of data, present an opportunity to probe the molecular underpinnings of disease. Inspired in part by recent developments in physical quantum processors, we evaluated several Ising-type algorithms, which are relatively unused in the biomedical sciences, on actual human tumor data from The Cancer Genome Atlas. Our results show performance competitive with conventional machine-learning algorithms in classifying human cancer types and associated molecular subtypes when training with all available data; but perhaps more strikingly, the Ising-type algorithms demonstrate superior performance with smaller training datasets. This gain in performance suggests a tantalizing application for rare diseases or other clinical applications where the number of training samples may be quite small. In addition, the features extracted from the Ising-type algorithms have biological relevance. Inspired by quantum process, Ising-type algorithms demonstrate better classification performance than standard machine learning approaches when training with small amounts of data. This suggests a potential application for rare diseases or other clinical data where the amount of training data may be limited.