Cargando…

Emerging role of machine learning in light-matter interaction

Machine learning has provided a huge wave of innovation in multiple fields, including computer vision, medical diagnosis, life sciences, molecular design, and instrumental development. This perspective focuses on the implementation of machine learning in dealing with light-matter interaction, which...

Descripción completa

Detalles Bibliográficos
Autores principales:	Zhou, Jiajia, Huang, Bolong, Yan, Zheng, Bünzli, Jean-Claude G.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Nature Publishing Group UK 2019
Materias:	Perspective
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6804848/ https://www.ncbi.nlm.nih.gov/pubmed/31645928 http://dx.doi.org/10.1038/s41377-019-0192-4

Ejemplares similares

Designing and understanding light-harvesting devices with machine learning
por: Häse, Florian, et al.
Publicado: (2020)

Making machine learning matter to clinicians: model actionability in medical decision-making
por: Ehrmann, Daniel E., et al.
Publicado: (2023)

Synthetic data as an enabler for machine learning applications in medicine
por: Rajotte, Jean-Francois, et al.
Publicado: (2022)

Machine learning and medical education
por: Kolachalama, Vijaya B., et al.
Publicado: (2018)

Machine learning for a sustainable energy future
por: Yao, Zhenpeng, et al.
Publicado: (2022)

Emerging questions about the macromolecular machines of muscle
por: Dirksen, Robert T., et al.
Publicado: (2010)

Perspectives in machine learning for wildlife conservation
por: Tuia, Devis, et al.
Publicado: (2022)

Machine learning methods in the computational biology of cancer
por: Vidyasagar, M.
Publicado: (2014)

Machine learning in medicine: Addressing ethical challenges
por: Vayena, Effy, et al.
Publicado: (2018)

Transforming health policy through machine learning
por: Ashrafian, Hutan, et al.
Publicado: (2018)

Machine learning in population health: Opportunities and threats
por: Flaxman, Abraham D., et al.
Publicado: (2018)

Machine learning in diagnosis of coronary artery disease
por: Ling, Hao, et al.
Publicado: (2021)

Machine learning in chronic obstructive pulmonary disease
por: Zhang, Bochao, et al.
Publicado: (2023)

Machine learning approaches to predict drug efficacy and toxicity in oncology
por: Badwan, Bara A., et al.
Publicado: (2023)

Models and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level
por: Hutchinson, Lucy, et al.
Publicado: (2019)

Perspective: Multiomics and Machine Learning Help Unleash the Alternative Food Potential of Microalgae
por: Helmy, Mohamed, et al.
Publicado: (2022)

Machine learning for multi-omics data integration in cancer
por: Cai, Zhaoxiang, et al.
Publicado: (2022)

Laboratory earthquake forecasting: A machine learning competition
por: Johnson, Paul A., et al.
Publicado: (2021)

Are Machine Learning Methods the Future for Smoking Cessation Apps?
por: Abo-Tabik, Maryam, et al.
Publicado: (2021)

Advances in quantitative analysis of astrocytes using machine learning
por: Labate, Demetrio, et al.
Publicado: (2022)

Addressing racial disparities in surgical care with machine learning
por: Halamka, John, et al.
Publicado: (2022)

Promise and Provisos of Artificial Intelligence and Machine Learning in Healthcare
por: Bhardwaj, Anish
Publicado: (2022)

The path toward equal performance in medical machine learning
por: Petersen, Eike, et al.
Publicado: (2023)

Applications of machine learning in computer-aided drug discovery
por: Turzo, SM Bargeen Alam, et al.
Publicado: (2022)

A paradigm shift: emerging roles of microglia, a non-neuronal cell, in learning and memory
por: Gupta, Neelima, et al.
Publicado: (2021)

Multifaceted roles of pericytes-interorgan interactions
por: Zheng, Zhitong, et al.
Publicado: (2020)

Rethinking domain adaptation for machine learning over clinical language
por: Laparra, Egoitz, et al.
Publicado: (2020)

Second opinion needed: communicating uncertainty in medical machine learning
por: Kompa, Benjamin, et al.
Publicado: (2021)

In medicine, how do we machine learn anything real?
por: Ghassemi, Marzyeh, et al.
Publicado: (2022)

The Validity of Machine Learning Procedures in Orthodontics: What Is Still Missing?
por: Auconi, Pietro, et al.
Publicado: (2022)

Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective
por: Barrios, Joshua P., et al.
Publicado: (2022)

Starting at Go: Protein structure prediction succumbs to machine learning
por: Rothman, James E.
Publicado: (2023)

Applied machine learning as a driver for polymeric biomaterials design
por: McDonald, Samantha M., et al.
Publicado: (2023)

From Black Box to Machine Learning: A Journey through Membrane Process Modelling
por: Galinha, Claudia F., et al.
Publicado: (2021)

Artificial intelligence and machine learning in clinical development: a translational perspective
por: Shah, Pratik, et al.
Publicado: (2019)

Avoid Oversimplifications in Machine Learning: Going beyond the Class-Prediction Accuracy
por: Ho, Sung Yang, et al.
Publicado: (2020)

Can we predict T cell specificity with digital biology and machine learning?
por: Hudson, Dan, et al.
Publicado: (2023)

Making virtual learning engaging and interactive
por: Sweetman, David S.
Publicado: (2020)

Oligodendroglia are emerging players in several forms of learning and memory
por: Munyeshyaka, Maxime, et al.
Publicado: (2022)

Cavitation in soft matter
por: Barney, Christopher W., et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_d7plrecqabs050qlidr35friu7