Results 151 to 160 of about 87,026 (282)

Extreme gradient boosting using conventional parameters accurately predicts insulin sensitivity in young and middle-aged Japanese persons. [PDF]

Front Endocrinol (Lausanne)
Murai N, Saito N, Nii S, Nishikawa H, Kodama E, Iida T, Imai H, Hashizume M, Tadokoro R, Sugisawa C, Iizaka T, Otsuka F, Ishibashi S, Nagasaka S.   +13 more
europepmc   +1 more source

Extreme Gradient Boosting Tuned with Metaheuristic Algorithms for Predicting Myeloid NGS Onco-Somatic Variant Pathogenicity. [PDF]

Bioengineering (Basel), 2023
Pellegrino E, Camilla C, Abbou N, Beaufils N, Pissier C, Gabert J, Nanni-Metellus I, Ouafik L.   +7 more
europepmc   +1 more source

Non‐Equilibrium Synthesis Methods to Create Metastable and High‐Entropy Nanomaterials

Advanced Science, EarlyView.
ABSTRACT Stabilizing multiple elements within a single phase enables the creation of advanced materials with exceptional properties arising from their complex composition. However, under equilibrium conditions, the Hume–Rothery rules impose strict limitations on solid‐state miscibility, restricting combinations of elements with mismatched crystal ...
Shuo Liu, Chaochao Dun, Jeffrey J. Urban, Mark T. Swihart   +3 more
wiley   +1 more source

Sequential EXtreme Gradient Boosting-Based Descriptor Reduction for Size Prediction of Zwitterionic Polymer-Based Nanoparticles. [PDF]

ACS Omega
Rezvantalab S, Mihandoost S, Pallares RM, Kiessling F.   +3 more
europepmc   +1 more source

A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh. [PDF]

PLoS One, 2023
Noorunnahar M, Chowdhury AH, Mila FA.
europepmc   +1 more source

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

Advanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan, Hengheng Zhao, Haotian Wang, Zhanjie Liu, Qian Li, Weifeng Zhang, Qingsong Zhao, Liqun Zhang, Jun Liu   +8 more
wiley   +1 more source

SGO enhanced random forest and extreme gradient boosting framework for heart disease prediction. [PDF]

Sci Rep
Naik A, Tejani GG, Mousavirad SJ.
europepmc   +1 more source

CrossFuse-XGBoost: accurate prediction of the maximum recommended daily dose through multi-feature fusion, cross-validation screening and extreme gradient boosting. [PDF]

Brief Bioinform, 2023
Li Q, He Y, Pan J.
europepmc   +1 more source

Photocatalytic Micro/Nanomotors Functioning in the Near‐Infrared Window for Biomedical Applications

Advanced Science, EarlyView.
This review focuses on near‐infrared responsive photocatalytic micro/nanomotors based on inorganic materials, with particular emphasis on minimally invasive biomedical applications. It discusses material strategies for achieving near‐infrared responsiveness, including heterostructure construction, upconversion nanoparticle integration, defect ...
Yufen Chen, João Marcos Gonçalves, Katherine Villa   +2 more
wiley   +1 more source

Gradient-enhanced neural network and extreme gradient boosting modeling for the prediction of the 3D bone mineral density distribution from 2D-DXA scans. [PDF]

Osteoporos Sarcopenia
Seo J, Quagliato L, Chung YS, Lee T.
europepmc   +1 more source