Results 41 to 50 of about 66,762 (195)

Discovery of unconventional proton-conducting inorganic solids via defect-chemistry-trained, interpretable machine learning

, 2023
High-throughput computational screening and machine learning holds significant potential for exploring diverse chemical compositions and discovering novel inorganic solids. However, the complexity of point defects, which occur in all inorganic solids and
Akihide, Kuwabara, Susumu, Fujii, Yoshihiro, Yamazaki, Yuta, Shimizu, Junji, Hyodo   +4 more
core   +1 more source

Clinical Applications of Machine Learning

Annals of Surgery Open
Objective:. This review introduces interpretable predictive machine learning approaches, natural language processing, image recognition, and reinforcement learning methodologies to familiarize end users. Background:.
Nadayca Mateussi, PhD, Michael P. Rogers, MD, Emily A. Grimsley, MD, Meagan Read, MD, Rajavi Parikh, DO, Ricardo Pietrobon, MD, PhD, Paul C. Kuo, MD   +6 more
doaj   +1 more source

Foreign direct investment and local interpretable model-agnostic explanations: a rational framework for FDI decision making [PDF]

Journal of Economics Finance and Administrative Science
Purpose – This study aims to examine foreign direct investment (FDI) factors and develops a rational framework for FDI inflow in Western European countries such as France, Germany, the Netherlands, Switzerland, Belgium and Austria.
Devesh Singh
doaj   +1 more source

Machine learning methods in chemoinformatics

, 2014
Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion.
John B. O. Mitchell, Mitchell, J.B.O.
core   +1 more source

Property valuation with interpretable machine learning [PDF]

, 2023
Property valuation is an important task for various stakeholders, including banks, local authorities, property developers, and brokers. As a result of the characteristics of the real estate market, such as the infrequency of trades, limited supply ...
Hartikainen, Kaapro
core  

SMILE: systems metabolomics using interpretable learning and evolution

BMC Bioinformatics, 2021
Background Direct link between metabolism and cell and organism phenotype in health and disease makes metabolomics, a high throughput study of small molecular metabolites, an essential methodology for understanding and diagnosing disease development and ...
Chengyuan Sha, Miroslava Cuperlovic-Culf, Ting Hu   +2 more
doaj   +1 more source

Accelerated Reactivity Mechanism and Interpretable Machine Learning Model of N-Sulfonylimines Towards Fast Multicomponent Reactions

, 2020
Predicting the outcome of chemical reactions using machine learning models has emerged as a promising research area in chemical science. However, the use of such models to prospectively test new reactions by interpreting chemical reactivity is limited ...
Yingqi, Chen, Krupal P., Jethava, Ahad, Hossain, Gaurav, Chopra, Jonathan A, Fine   +4 more
core   +1 more source

Interpretable Machine Learning of Two‐Photon Absorption

Advanced Science, 2023
Molecules with strong two‐photon absorption (TPA) are important in many advanced applications such as upconverted laser and photodynamic therapy, but their design is hampered by the high cost of experimental screening and accurate quantum chemical (QC ...
Yuming Su, Yiheng Dai, Yifan Zeng, Caiyun Wei, Yangtao Chen, Fuchun Ge, Peikun Zheng, Da Zhou, Pavlo O. Dral, Cheng Wang   +9 more
doaj   +1 more source

Conceptual challenges for interpretable machine learning [PDF]

Synthese, 2020
AbstractAs machine learning has gradually entered into ever more sectors of public and private life, there has been a growing demand for algorithmic explainability. How can we make the predictions of complex statistical models more intelligible to end users?
openaire   +3 more sources

Interpretable machine learning prediction of all-cause mortality

Communications Medicine, 2022
Qui et al. present a new approach, IMPACT, that uses explainable artificial intelligence to analyze all-cause mortality. IMPACT provides insights into the individualized mortality risk scores, while maintaining high model accuracy and the expressive ...
Wei Qiu, Hugh Chen, Ayse Berceste Dincer, Scott Lundberg, Matt Kaeberlein, Su-In Lee   +5 more
doaj   +1 more source