Results 221 to 230 of about 22,118,555 (377)

CBMR: Coordinate-based meta-regression for group and covariate inference. [PDF]

Imaging Neurosci (Camb)
Yu Y, Hill-Bowen LD, Riedel MC, Bottenhorn K, Laird AR, Nichols TE.   +5 more
europepmc   +1 more source

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

Advanced 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

Candidate genes for anthracnose resistance in Senegalese sorghum: a machine learning-based exploration. [PDF]

Funct Integr Genomics
Ahn E, Baek I, Prom LK, Park S, Kim MS, Meinhardt LW, Magill C.   +6 more
europepmc   +1 more source

A bootstrap test for single index models [PDF]

, 2001
Wolfgang Karl Härdle, Enno Mammen, Isabel Proença   +2 more
openalex   +1 more source

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

Advanced 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, Tiago L. P. Galvão, José D. Gouveia, José R. B. Gomes   +3 more
wiley   +1 more source

A practice-oriented guide to statistical inference in linear modeling for non-normal or heteroskedastic error distributions. [PDF]

Behav Res Methods
Rajh-Weber H, Huber SE, Arendasy M.
europepmc   +1 more source

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Advanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source

Bootstrapping unobserved component models

, 2010
En esta tesis, proponemos el uso de técnicas bootstrap para incorporar la incertidumbre de la estimación de los parámetros en modelos de componentes inobservados expresado en un contexto de modelos de espacio de los estados. A lo largo de los capítulos usamos simulaciones Monte Carlo y datos reales para mostrar los resultados de los procedimientos ...
openaire   +2 more sources

TAN-FGBMLE: Tree-Augmented Naive Bayes Structure Learning Based on Fast Generative Bootstrap Maximum Likelihood Estimation for Continuous-Variable Classification. [PDF]

Entropy (Basel)
Wei C, Zhang T, Li C, Wang P, Ye Z.
europepmc   +1 more source

Identification of Exhaled Volatile Organic Compounds Biomarkers for Lung Cancer Under Data‐Limited Conditions Using Data Augmentation and Multi‐View Feature Selection

Advanced Intelligent Discovery, EarlyView.
This work introduces a novel framework for identifying non‐small cell lung cancer biomarkers from hundreds of volatile organic compounds in breath, analyzed via gas chromatography‐mass spectrometry. This method integrates generative data augmentation and multi‐view feature selection, providing a stable and accurate solution for biomarker discovery in ...
Guancheng Ren, Bohao Liu, Zhiyuan Qu, Xue Jiang, Heng Zhao, Danyao Qu, Ruizhi Ning, Chen Su, Taoping Liu, Guangjian Zhang, Weiwei Wu   +10 more
wiley   +1 more source