AIMC Topic: Dioxygenases

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TET2 gene mutation status associated with poor prognosis of transition zone prostate cancer: a retrospective cohort study based on whole exome sequencing and machine learning models.

Frontiers in endocrinology Apr 14, 2025
BACKGROUND: Prostate cancer (PCa) in the transition zone (TZ) is uncommon and often poses challenges for early diagnosis, but its genomic determinants and therapeutic vulnerabilities remain poorly characterized.
Humans Machine Learning Prognosis Retrospective Studies Aged Biomarkers, Tumor Male Middle Aged Prostatic Neoplasms Mutation DNA-Binding Proteins Exome Sequencing Proto-Oncogene Proteins Dioxygenases
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Machine Learning Guided Rational Design of a Non-Heme Iron-Based Lysine Dioxygenase Improves its Total Turnover Number.

Chembiochem : a European journal of chemical biology Dec 5, 2024
Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolutio...
Machine Learning Iron Mutation Molecular Dynamics Simulation Lysine Protein Engineering Dioxygenases
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