Results 191 to 200 of about 113,736 (262)

From information to evidence in a Bayesian network

, 2019
Ali Ben Mrad, Véronique Delcroix, Sylvain Piechowiak (7183376), Philip A. Leicester   +3 more
openalex   +1 more source

Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics

Advanced Intelligent Discovery, EarlyView.
A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha, Jesus Gil, Pedro J. Ballester   +2 more
wiley   +1 more source

Exploring stroke risk factors in different genders using Bayesian networks: a cross-sectional study involving a population of 134,382. [PDF]

BMC Public Health
Linghu L, Huang Y, Qiu L, Wang X, Zhang J, Ma L, Li C, Wang L.   +7 more
europepmc   +1 more source

A hybrid factored frontier algorithm for dynamic Bayesian network models of biopathways

, 2011
Sucheendra K. Palaniappan, S. Akshay, Blaise Genest, Thiagarajan, P.S.   +3 more
openalex   +1 more source

Code: Stochastic Weight Sharing for Bayesian Neural Networks

Moule Lin, Goetz Botterweck
openalex   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Using Bayesian Networks to Predict Urgent Care Visits in Patients Receiving Systemic Therapy for Non-Small Cell Lung Cancer. [PDF]

JCO Clin Cancer Inform
Gonzalez BD, Li X, Gudenkauf LM, Pullukkara JJ, Oswald LB, Hoogland AI, Le T, El Naqa I, Saltos AN, Haura EB, Luo Y.   +10 more
europepmc   +1 more source

Generative and Experimental Validation of High Refractive Index Polymers via Domain Knowledge Approach with Small Data

Advanced Intelligent Discovery, EarlyView.
This research demonstrates that the combination of domain knowledge–based multiple regression, multi‐objective Bayesian optimization, and generative models is a suitable prediction tool for candidates of high refractive index polymers, even with the constraints in the model trained on limited data. The experimental validation can reproduce the proposed
Takuya Yokoo, Seigo Watanabe, Hana Matsunaga, Kenichi Oyaizu   +3 more
wiley   +1 more source

Predicting Atrial Fibrillation Relapse Using Bayesian Networks: Explainable AI Approach. [PDF]

JMIR Cardio
Alves JM, Matos D, Martins T, Cavaco D, Carmo P, Galvão P, Costa FM, Morgado F, Ferreira AM, Freitas P, Dias CC, Rodrigues PP, Adragão P.   +12 more
europepmc   +1 more source

Bayesian-neural-network-based strain estimation approach for optical coherence elastography

Yulei Bai, Kangyang Zhang, Rui Mo, Zihao Ni, Zhaoshui He, Shengli Xie, Bo Dong   +6 more
openalex   +1 more source