Results 201 to 210 of about 520,634 (314)

Polymerase Chain Reaction. Perturbation Theory and Machine Learning Artificial Intelligence‐Experimental Microbiome Analysis: Applications to Ancient DNA and Tree Soil Metagenomics Cases of Study

Advanced Intelligent Systems, EarlyView.
The polymerase chain reaction (PCR).Perturbation Theory and Machine Learning framework integrates perturbation theory and machine learning to classify genetic sequences, distinguishing ancient DNA from modern controls and predicting tree health from soil metagenomic data.
Jose L. Rodriguez, Estefania Ascencio, Jose M. Ageitos, Lucia Feijoo, Shan He, Amirreza Daghighi, Gerardo M. Casanola‐Martin, Cristian R. Munteanu, Santiago Rodriguez Yañez, Alejandro Pazos, Harbil Bediaga, Brenda Durand Ordiales, Raquel Esteban, Ana‐Maria Heres, Jorge Curiel Yuste, Lur Epelde, Bakhtiyor Rasulev, Tomas Gonzalez, Sonia Arrasate, Humberto González‐Diaz   +19 more
wiley   +1 more source

Bayesian network-GIS-based livability assessment of rural settlements in the Upper Min River Basin, China. [PDF]

Sci Rep
Li J, Meng J, Wang Y, Mei H, Deng L, Guo C.   +5 more
europepmc   +1 more source

Data Sheet 7_Efficacy and safety of Chinese medicine injection combined with concurrent chemoradiotherapy in the treatment of esophageal cancer: a Bayesian network meta-analysis.docx

Jiacheng Wang (1359669), Hongbin Xi (20305953), Xuewei Chen (224652), Yingqian Xin (22425478), Fengqin Wei (6103142)   +4 more
openalex   +1 more source

Predicting Postresection Colorectal Liver Metastases Recurrence Using Advanced Graph Neural Networks with Explainability and Causal Inference

Advanced Intelligent Systems, EarlyView.
This study introduces a framework that combines graph neural networks with causal inference to forecast recurrence and uncover the clinical and pathological factors driving it. It further provides interpretability, validates risk factors via counterfactual and interventional analyses, and offers evidence‐based insights for treatment planning ...
Jubair Ahmed, Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Sami Azam   +3 more
wiley   +1 more source

Machine learning and bayesian network based on fuzzy AHP framework for risk assessment in process units. [PDF]

Sci Rep
Mandali H, Keighobadi E, Ebrahimi H, Hanifi SM, Ayat SM, Ghashghaei M.   +5 more
europepmc   +1 more source

Approximation Strategies for Structure Learning in Bayesian Networks

, 2015
Teppo Niinimäki
openalex   +1 more source

Table 4_Comparative effectiveness of multiple different non-pharmacologic interventions for post-stroke constipation: a Bayesian network meta-analysis.docx

Sisi Feng (401706), Xinhui Wu (1512367), Xuemei Dai (631920), Zhihao Liu (419349), Yi Luo (143206), Fei Wang (19534)   +5 more
openalex   +1 more source

Cross‐Modal Characterization of Thin‐Film MoS2 Using Generative Models

Advanced Intelligent Systems, EarlyView.
Cross‐modal learning is evaluated using atomic force microscopy (AFM), Raman spectroscopy, and photoluminescence spectroscopy (PL) through unsupervised learning, regression, and autoencoder models. Autoencoder models are used to generate spectroscopy data from the microscopy images.
Isaiah A. Moses, Chen Chen, Joan M. Redwing, Wesley F. Reinhart   +3 more
wiley   +1 more source

Antibody-drug conjugates outperform chemotherapy in EGFR-TKI-resistant NSCLC: a Bayesian network meta-analysis. [PDF]

Ther Adv Med Oncol
Chen Y, Du Y, Ni J, Wang D, Zhan P, Song Y, Han H, Lv T.   +7 more
europepmc   +1 more source

Diagnostic Bayesian network in building energy systems: Current insights, practical challenges, and future trends

Chujie Lu, Ziao Wang, Martín Mosteiro-Romero, Laure Itard   +3 more
openalex   +1 more source