Chemical communications (Cambridge, England)
Apr 22, 2025
Nature, especially plants, can inspire scientists and engineers in the development of bioinspired machines able to adapt and interact with complex unstructured environments. Advances in manufacturing techniques, such as 3D printing, have expanded the...
Chemical communications (Cambridge, England)
Nov 30, 2023
Materials informatics (MI) has immense potential to accelerate the pace of innovation and new product development in biotechnology. Close collaborations between skilled physical and life scientists with data scientists are being established in pursui...
Chemical communications (Cambridge, England)
Aug 10, 2023
Precision diagnosis and classification of autoimmune diseases (ADs) is challenging due to the obscure symptoms and pathological causes. Biofluid metabolic analysis has the potential for disease screening, in which high throughput, rapid analysis and ...
Chemical communications (Cambridge, England)
Jun 8, 2023
ITIC-series nonfullerene organic photovoltaics (NF OPVs) have realized the simultaneous increases of the short-circuit current density () and open-circuit voltage (), called the positive correlation between and , which could improve the power conver...
Chemical communications (Cambridge, England)
May 30, 2023
We combined a library of medium-sized molecules with iterative screening using multiple machine learning algorithms that were ligand-based, which resulted in a large increase of the hit rate against a protein-protein interaction target. This was demo...
Chemical communications (Cambridge, England)
May 9, 2023
Based on label-free SERS technology, the relationship between the Raman signals of pathogenic microorganisms and purine metabolites was analyzed in detail. A deep learning CNN model was successfully developed, achieving a high accuracy rate of 99.7%...
Chemical communications (Cambridge, England)
Sep 13, 2022
In this study, we evaluate different apoproaches to unsupervised classification of cyclic voltammetric data, including Principal Component Analysis (PCA), t-distributed Stochastic Neighbour Embedding (t-SNE), Uniform Manifold Approximation and Projec...
Chemical communications (Cambridge, England)
Jun 9, 2022
Data representation forms a feature space where forms data distribution that is one of the key factors determining the prediction accuracy of machine learning (ML). In particular, the data representation is crucial to handle small and biased training...
Chemical communications (Cambridge, England)
May 30, 2022
Breath odor sensing-based individual authentication was conducted for the first time using an artificial olfactory sensor system. Using a 16-channel chemiresistive sensor array and machine learning, a mean accuracy of >97% was successfully achieved. ...
Chemical communications (Cambridge, England)
May 5, 2022
This work showcases the remarkable ability of sigma profiles to function as molecular descriptors in deep learning. The sigma profiles of 1432 compounds are used to train convolutional neural networks that accurately correlate and predict a wide rang...