2019/09/30 by Richard Y. Li, Sharvari Gujja, Sweta R. Bajaj +7 · 42 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Artificial intelligence #Computer science #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum computer #Quantum machine learning #Quantum mechanics #Quantum-Dot Cellular Automata #q-bio.GN #quant-ph
paper · pdf · doi:10.1016/j.patter.2021.100246
published in Patterns 2(6), 100246 (Elsevier BV)
openalex publication_date 2021/04/28 · arxiv created 2021/04/29 · arxiv updated 2021/12/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.