Results 241 to 250 of about 248,790 (377)

Brain Connectivity and Information-Flow Breakdown Revealed by a Minimum Spanning Tree-Based Analysis of MRI Data in Behavioral Variant Frontotemporal Dementia. [PDF]

open access: yesFront Neurosci, 2019
Saba V   +9 more
europepmc   +1 more source

Machine Learning for Organic Fluorescent Materials

open access: yesAggregate, EarlyView.
Organic fluorescent materials (OFMs) have demonstrated significant potential in diverse applications. Conventional approaches for studying OFMs face significant limitations in fluorescence spectroscopy and computational methods. Machine learning (ML) has revolutionized materials chemistry, offering superior predictive accuracy and efficiency over ...
Jiamin Zhong   +7 more
wiley   +1 more source

AI‐Enhanced Surface‐Enhanced Raman Scattering for Accurate and Sensitive Biomedical Sensing

open access: yesAdvanced Intelligent Discovery, EarlyView.
AI‐SERS advances spectral interpretation with greater precision and speed, enhancing molecular detection, biomedical analysis, and imaging. This review explores its essential contributions to biofluid analysis, disease identification, therapeutic agent evaluation, and high‐resolution biomedical imaging, aiding diagnostic decision‐making.
Seungki Lee, Rowoon Park, Ho Sang Jung
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

MISTICA: Minimum Spanning Tree-Based Coarse Image Alignment for Microscopy Image Sequences. [PDF]

open access: yesIEEE J Biomed Health Inform, 2016
Ray N, McArdle S, Ley K, Acton ST.
europepmc   +1 more source

A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water‐Gas Shift Catalysis on MXenes

open access: yesAdvanced Intelligent Discovery, EarlyView.
Machine learning predicts activation energies for key steps in the water‐gas shift reaction on 92 MXenes. Random Forest is identified as the most accurate model. Reaction energy and reactant LogP emerge as key descriptors. The approach provides a predictive framework for catalyst design, grounded in density functional theory data and validated through ...
Kais Iben Nassar   +3 more
wiley   +1 more source

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