Associated Papers
A Machine Learning Framework Predicts the Clinical Severity of Hemophilia B Caused by Point-Mutations.
Lopes TJS et al.
Frontiers in Bioinformatics, 2022
36304295 (PubMed )
Supplement: DataSheet1.zip › Supplementary_Table_5_-_Disambiguate.xlsx › Predictions
Splicing dysregulation contributes to the pathogenicity of several F9 exonic point variants.
Katneni UK et al.
Molecular Genetics & Genomic Medicine, 2019
31257730 (PubMed )
Supplement: MGG3-7-e840-s002.xls › F9_Splicing_Data
Effects of functional CYP2C8,CYP2C9,CYP3A5,and ABCB1 genetic variants on the pharmacokinetics of insulin sensitizer pioglitazone in Chinese Han individuals.
Yin SJ et al.
Pharmacogenetics and Genomics, 2017
Identification of mutations in the F8 and F9 gene in families with haemophilia using targeted high-throughput sequencing.
Lyu C et al.
Haemophilia : the Official Journal of the World Federation of Hemophilia, 2016
The CDC Hemophilia B mutation project mutation list: a new online resource.
Li T et al.
Molecular Genetics & Genomic Medicine, 2013
24498619 (PubMed )
Supplement: mgg30001-0238-SD1.xlsx › CHBMP_Mutation_List
Analysis of F9 point mutations and their correlation to severity of haemophilia B disease.
Hamasaki-Katagiri N et al.
Haemophilia : the Official Journal of the World Federation of Hemophilia, 2012
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