Cancer classification with a network of chemical oscillators
Literature Information
Konrad Gizynski, Jerzy Gorecki
We discuss chemical information processing considering dataset classifiers formed with a network of interacting droplets. Our arguments are based on computer simulations of droplets in which a photosensitive variant of the Belousov–Zhabotinsky (BZ) reaction proceeds. By applying optical control we can adjust the time evolution of individual droplets and prepare the network to perform a specific computational task. We demonstrate that chemical classifiers made of droplets can be designed in computer simulations based on evolutionary algorithms. The mutual information between the dataset and the observed time evolution of droplets in the network is taken as the fitness function in the optimization process. We show that a classifier of the Wisconsin Breast Cancer Dataset made of a relatively small number of droplets can distinguish between malignant and benign forms of cancer with an accuracy exceeding 97%. The reliability of the optimized chemical classifiers of this dataset as a function of optimization time, number of droplets involved in data processing and the method of extracting the output information is discussed.
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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.











methanone structure [4-(Hydroxymethyl)phenyl](phenyl)methanone structure](https://static.chemtradehub.com/structs/814/81449-01-6-786d.webp)

![4-(4-{4-[4-Fluoro-3-(trifluoromethyl)phenyl]-1-methyl-1H-imidazol-2-yl}-1-piperidinyl)-1H-pyrazolo[3,4-d]pyrimidine 4-methylbenzenesulfonate (1:1) structure 4-(4-{4-[4-Fluoro-3-(trifluoromethyl)phenyl]-1-methyl-1H-imidazol-2-yl}-1-piperidinyl)-1H-pyrazolo[3,4-d]pyrimidine 4-methylbenzenesulfonate (1:1) structure](https://static.chemtradehub.com/structs/108/1082949-68-5-00b6.webp)
