Zhonghong Teng, Shukai Cai
Application of Computer-Aided Instruction (CAI) in the Physical Education: Survey Analysis of Chinese Universities
Journal of healthcare engineering
rare oncogenic driver events particularly affecting expression splicing driver genes suspected substantially contribute large heterogeneity hematologic malignancies however identification remains challenging address issue generated largest dataset date matched whole genome sequencing total rna sequencing hematologic malignancies 3760 patients spanning 24 disease entities taking advantage dataset size focused discovering rare regulatory aberrations therefore called expression splicing outliers using extension workflow drop detection rna outliers pipeline absplice variant effect predictor identifies genetic variants causing aberrant splicing next trained machine learning model integrating results prioritize new candidate diseasespecific driver genes found median seven expression outlier genes two splicing outlier genes two rare spliceaffecting variants per sample category showed significant enrichment already wellcharacterized driver genes odds ratios exceeding three among genes called five samples heldout data integrative modeling significantly outperformed modeling based solely genomic data revealed promising novel candidate driver genes remarkably found truncated form low density lipoprotein receptor lrp1b transcript aberrantly overexpressed half hairy cell leukemia variant hclv samples lesser extent closely related bcell neoplasms observation confirmed independent cohort suggests lrp1b novel marker hclv subclass yet unreported functional role lrp1b within rare entities altogether census expression splicing outliers 24 hematologic malignancy entities companion computational workflow constitute unique resources deepen understanding rare oncogenic events hematologic cancers