.png){kind=icon}
Return
Quantum processor-inspired machine learning in the biomedical sciences
.png){kind=icon}
OA
AI
DOI：10.1016/j.patter.2021.100246.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 MLmethods. Our results indicate that Ising-typeML 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.
Keywords:
MOLECULAR CLASSIFICATION
RIDGE REGRESSION
CANCER
OPTIMIZATION
SELECTION
GENOMICS
LATTICE
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.4
Papers:
954
Citations:
3.6K
Organization
Cited Papers
ShareSaveThe cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data.png)
CANCER DISCOVERY
OAAI
CANCER DISCOVERYIF33.3
OAAIShareSaveA Fixed-Point Neural Network Architecture for Speech Applications on Resource Constrained Hardware
PREAI
ShareSaveShareSaveShareSaveShareSave
View more