Results 121 to 130 of about 1,169,808 (377)

Machine Learning‐Enabled Polymer Discovery for Enhanced Pulmonary siRNA Delivery

Advanced Functional Materials, EarlyView.
This study provides an efficient approach to train a machine learning model by merging heterogeneous literature data to predict suitable polymers for siRNA delivery. Without the need for extensive laboratory synthesis, the machine learning enabled a virtual screening and successfully predicted a polymer that is validated for effective gene silencing in
Felix Sieber‐Schäfer, Min Jiang, Adrian Kromer, Anny Nguyen, Müge Molbay, Simone Pinto Carneiro, David Jürgens, Gerald Burgstaller, Bastian Popper, Benjamin Winkeljann, Olivia Monika Merkel   +10 more
wiley   +1 more source

CORR Synthesis: Can Decision Tree Learning Advance Orthopaedic Surgery Research? [PDF]

Clin Orthop Relat Res, 2023
Wilson A.
europepmc   +1 more source

TreeGrad: Transferring Tree Ensembles to Neural Networks

, 2019
Gradient Boosting Decision Tree (GBDT) are popular machine learning algorithms with implementations such as LightGBM and in popular machine learning toolkits like Scikit-Learn.
C Siu, DH Wolpert, F Pedregosa, JA Blackard, JH Friedman, K Nakai, L Breiman, SK Murthy   +7 more
core   +1 more source

Flux‐Regulated Crystallization of Perovskites Using Machine Learning‐Predicted Solvent Evaporation Rates for X‐Ray Detectors

Advanced Functional Materials, EarlyView.
By integrating machine learning into flux‐regulated crystallization (FRC), accurate prediction of solvent evaporation rates in real time, improving crystallization control and reducing crystal growth variability by over threefold, is achieved. This enhances the reproducibility and quality of perovskite single crystals, leading to reproducible ...
Tatiane Pretto, Sergey Dayneko, Antoine Pavesic Junior, Nicholas Sandor, Alexander Hart, Yuki Haruta, Magdalena Bazalova‐Carter, Marcos J. L. Santos, Makhsud I. Saidaminov   +8 more
wiley   +1 more source

Decision tree learning in Neo4j on homogeneous and unconnected graph nodes from biological and clinical datasets. [PDF]

BMC Med Inform Decis Mak, 2023
Mondal R, Do MD, Ahmed NU, Walke D, Micheel D, Broneske D, Saake G, Heyer R.   +7 more
europepmc   +1 more source

Bimetallic Nanoparticles as Cocatalysts for Photocatalytic Hydrogen Production

Advanced Functional Materials, EarlyView.
Recent developments have introduced bimetallic nanoparticles as effective cocatalysts for photocatalytic systems. This review explores the rapidly expanding research on bimetallic cocatalysts for photocatalytic production of hydrogen, emphasizing the creation of carrier‐selective contacts, localized surface plasmon resonance effects, methodologies for ...
Yufen Chen, Asier Agrelo‐Lestón, Ignasi Burgués‐Ceballos, Jordi Llorca, Lluís Soler   +4 more
wiley   +1 more source

Evaluation of four machine learning methods in predicting orthodontic extraction decision from clinical examination data and analysis of feature contribution

Frontiers in Bioengineering and Biotechnology
IntroductionThe study aims to predict tooth extraction decision based on four machine learning methods and analyze the feature contribution, so as to shed light on the important basis for experts of tooth extraction planning, providing reference for ...
Jialiang Huang, Jialiang Huang, Ian-Tong Chan, Zhixian Wang, Xiaoyi Ding, Ying Jin, Congchong Yang, Congchong Yang, Congchong Yang, Yichen Pan, Yichen Pan, Yichen Pan   +11 more
doaj   +1 more source

Exclusive Biosynthesis of Pullulan Using Taguchi's Approach and Decision Tree Learning Algorithm by a Novel Endophytic Aureobasidium pullulans Strain. [PDF]

Polymers (Basel), 2023
Saber WIA, Al-Askar AA, Ghoneem KM.
europepmc   +1 more source

Verifiable Reinforcement Learning via Policy Extraction

, 2019
While deep reinforcement learning has successfully solved many challenging control tasks, its real-world applicability has been limited by the inability to ensure the safety of learned policies. We propose an approach to verifiable reinforcement learning
Bastani, Osbert, Pu, Yewen, Solar-Lezama, Armando   +2 more
core  

Artificial Intelligence‐Driven Development in Rechargeable Battery Materials: Progress, Challenges, and Future Perspectives

Advanced Functional Materials, EarlyView.
AI is transforming the research paradigm of battery materials and reshaping the entire landscape of battery technology. This comprehensive review summarizes the cutting‐edge applications of AI in the advancement of battery materials, underscores the critical challenges faced in harnessing the full potential of AI, and proposes strategic guidance for ...
Qingyun Hu, Junyuan Lu, Jian Hui, Ziyuan Rao, Yang Ren, Hong Wang   +5 more
wiley   +1 more source