Results 221 to 230 of about 242,378 (342)

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Detection of Differentially Methylated Regions Using Bayes Factor for Ordinal Group Responses. [PDF]

Genes (Basel), 2019
Dunbar F, Xu H, Ryu D, Ghosh S, Shi H, George V.   +5 more
europepmc   +1 more source

Relationships Between Environmental Physicochemical Factors, Zooplankton and Jellyfish Blooms in Chabahar and Pozm Bays (Makran (Oman) Sea) [PDF]

, 2017
Fatemeh Pourjomeh, Mohammadreza Shokri, Hamid Rezai, Hassan Rajabi Maham, Elham Maghsoudlou   +4 more
openalex   +1 more source

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

Reply to Held: When is a harmonic mean <i>p</i>-value a Bayes factor? [PDF]

Proc Natl Acad Sci U S A, 2019
Wilson DJ.
europepmc   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Bayesian Design of Non-Inferiority Clinical Trials via the Bayes Factor. [PDF]

Stat Biosci, 2018
Li W, Chen MH, Wangy X, Dey DK.
europepmc   +1 more source

Building an Ensemble from a Single Naive Bayes Classifier in the Analysis of Key Risk Factors for Polish State Fire Service [PDF]

, 2014
Stefan Nikolić, Marko Knežević, Vladimir G. Ivancevic, Ivan Luković   +3 more
openalex   +1 more source

Distribution of Bayes’ factor

Physical Review D
Luca Amendola, Vrund Patel, Ziad Sakr, Elena Sellentin, Kevin Wolz   +4 more
openaire   +2 more sources

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source