Li Richard Y, Gujja Sharvari, Bajaj Sweta R, Gamel Omar E, Cilfone Nicholas, Gulcher Jeffrey R, Lidar Daniel A, Chittenden Thomas W
Department of Chemistry, University of Southern California, 920 Bloom Walk, Los Angeles, CA 90089, USA.
Computational Biology and Bioinformatics Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA.
Patterns (N Y). 2021 Apr 28;2(6):100246. doi: 10.1016/j.patter.2021.100246. eCollection 2021 Jun 11.
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.
高通量基因组技术的最新进展，再加上计算机处理能力和内存呈指数级增长，使我们能够从全基因组的角度探究人类疾病复杂的分子基础。虽然预计基因组信息将不断增加，但传统高性能计算的瓶颈正在迅速逼近。受物理量子处理器近期进展的启发，我们在实际的人类肿瘤数据上评估了几种非常规机器学习（ML）策略，即“伊辛型”方法，其目标函数的制定与模拟退火和量子退火相同。我们展示了多种伊辛型ML算法对来自癌症基因组图谱的多组学人类癌症数据进行分类的有效性，并将这些分类器与各种标准ML方法进行了比较。我们的结果表明，伊辛型ML在较小的训练数据集上具有卓越的分类性能，从而为非常规计算方法在生物医学科学中的潜在未来应用提供了令人信服的经验证据。
