Results 221 to 230 of about 207,453 (329)
BDDN: bayesian dynamic differential network analysis in cancer proteomics. [PDF]
Kim J, Lee D, Park J, Jin IH, Ha MJ.
europepmc +1 more source
Abstract Context‐centric proactive information delivery (PID) is a relatively underexplored domain within recommender systems (RS) aimed at enhancing Knowledge Workers' productivity by proactively providing relevant information during digital tasks.
Mahta Bakhshizadeh +4 more
wiley +1 more source
ABSTRACT A recent “hierarchical” reinterpretation of the neurological basis of autism suggests that in autism with early language delay, perceptual processing may be favored over the integration of transmodal information. This model is largely based on neuroimaging findings relating to visual processing, but predicts a corresponding reorganization in ...
Luodi Yu +3 more
wiley +1 more source
Bayesian neural network-based policy effect prediction for green transformation of power business environment. [PDF]
Shen Y +5 more
europepmc +1 more source
Abstract Aim Tacrolimus dosing in the early post‐kidney transplant period is challenging due to a narrow therapeutic index and substantial interindividual pharmacokinetic (PK) variability. This study aimed to develop and validate mechanism‐informed machine learning (ML) models to support individualized tacrolimus dosing during this critical period ...
Hui Yu +4 more
wiley +1 more source
Letter to the Editor: integrating Bayesian network-based personalized prediction models to enhance manual acupuncture outcomes for chronic spontaneous urticaria. [PDF]
Ruan C, Ruan C, Wang L.
europepmc +1 more source
Transfer Learning Approaches in Bioprocess Engineering: Opportunities and Challenges
ABSTRACT Transfer learning (TL) has recently emerged as a promising approach to overcoming one of the key limitations of bioprocess engineering: data scarcity. By leveraging knowledge from one bioprocess to another, TL allows existing models and data sets to be reused efficiently, accelerating process development, improving prediction accuracy, and ...
Daniel Barón Díaz +3 more
wiley +1 more source
Bayesian networks for predicting clinical outcomes in COVID-19 patients: A retrospective study in a resource-limited setting. [PDF]
Filamant TC +2 more
europepmc +1 more source
ABSTRACT The growing demand for biopharmaceutical products reflects their effectiveness in medical treatments. However, developing new biopharmaceuticals remains a major bottleneck, often taking up to a decade before market approval. Machine learning (ML) models have the potential to accelerate this process, but their success depends on access to large
Mohammad Golzarijalal +2 more
wiley +1 more source
Network-Driven Insights into Plant Immunity: Integrating Transcriptomic and Proteomic Approaches in Plant-Pathogen Interactions. [PDF]
Lv Y, Fan G.
europepmc +1 more source

