Results 221 to 230 of about 22,043,161 (356)

Characterization of the Cubamyces Menziesii Terpenome

open access: yesChemBioChem, EarlyView.
The genome of Cubamyces menziesii reveals 18 putative sesquiterpene cyclase genes. These genes are cloned and expressed in Escherichia coli, yielding 10 active enzymes. Using farnesyl diphosphate as substrate, the enzymes are analyzed, and the products characterized after bioconversion, uncovering diverse sesquiterpene structures. This study highlights
Létitia Leydet   +10 more
wiley   +1 more source

Bootstrap sample partition data model and distributed ensemble learning

open access: yes大数据
A sequential implementation of Bootstrap sampling and Bagging ensemble learning is computationally inefficient and not scalable to build large Bagging ensemble models with a large number of component models.Inspired by distributed big data computing, a ...
Kaijing LUO   +3 more
doaj  

Exploring the Stability and Substrate Profile of Transaminase from Silicibacter pomeroyi with Ancestral Sequence Reconstruction

open access: yesChemBioChem, EarlyView.
This study describes the successful identification of ancestral sequences N50 and N49, which have a longer half‐life of approximately four and two times the wild‐type Sp‐amine transaminase, respectively.. This approach effectively enhances the thermal stability of the transaminase.
Luyao Zhao   +4 more
wiley   +1 more source

Clade III Synthases Add Cyclic and Linear Terpenoids to the Psilocybe Metabolome

open access: yesChemBioChem, EarlyView.
The Psilocybe cubensis terpene synthases, CubB‐CubE, were investigated. Product formation assays identified CubB as a (3R,6E)‐(‐)‐nerolidol or linalool synthase, whereas CubC is a multiproduct synthase catalyzing the formation of β‐caryophyllene and other linear and cyclic sesquiterpenes/‐terpenoids.
Nick Zschoche   +7 more
wiley   +1 more source

Protocol for an Integrative Meta‐Analysis of the Application of Machine Learning Algorithms in the Prediction of Chronic Disease Risks and Outcomes

open access: yesChronic Diseases and Translational Medicine, EarlyView.
ABSTRACT Background Precise risk prediction of chronic diseases is essential for effective preventive care and management. Machine learning (ML) is a promising avenue to enhance chronic disease risk prediction; however, a comprehensive assessment of ML performance across various chronic diseases, populations, and health settings is needed. Methods This
Ebenezer Afrifa‐Yamoah   +4 more
wiley   +1 more source

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