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CBX4 deletion promotes tumorigenesis under Kras^(G12D) background by inducing genomic instability 被引量:1
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作者 Fangzhen Chen Wulei Hou +13 位作者 Xiangtian Yu Jing Wu Zhengda Li Jietian Xu Zimu Deng Gaobin Chen Bo Liu Xiaoxing Yin Wei Yu Lei Zhang Guoliang Xu Hongbin Ji Chunmin Liang Zuoyun Wang 《Signal Transduction and Targeted Therapy》 SCIE CSCD 2023年第10期4877-4888,共12页
Chromobox protein homolog 4(CBX4)is a component of the Polycomb group(PcG)multiprotein Polycomb repressive complexes 1(PRC1),which is participated in several processes including growth,senescence,immunity,and tissue r... Chromobox protein homolog 4(CBX4)is a component of the Polycomb group(PcG)multiprotein Polycomb repressive complexes 1(PRC1),which is participated in several processes including growth,senescence,immunity,and tissue repair.CBX4 has been shown to have diverse,even opposite functions in different types of tissue and malignancy in previous studies. 展开更多
关键词 IMMUNITY MALIGNANCY opposite
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mvPPT:A Highly Efficient and Sensitive Pathogenicity Prediction Tool for Missense Variants
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作者 Shi-Yuan Tong Ke Fan +6 位作者 Zai-Wei Zhou Lin-Yun Liu Shu-Qing Zhang Yinghui Fu Guang-Zhong Wang Ying Zhu Yong-Chun Yu 《Genomics, Proteomics & Bioinformatics》 SCIE CAS CSCD 2023年第2期414-426,共13页
Next-generation sequencing technologies both boost the discovery of variants in the human genome and exacerbate the challenges of pathogenic variant identification.In this study,we developed Pathogenicity Prediction T... Next-generation sequencing technologies both boost the discovery of variants in the human genome and exacerbate the challenges of pathogenic variant identification.In this study,we developed Pathogenicity Prediction Tool for missense variants(mvPPT),a highly sensitive and accurate missense variant classifier based on gradient boosting.mvPPT adopts high-confidence training sets with a wide spectrum of variant profiles,and extracts three categories of features,including scores from existing prediction tools,frequencies(allele frequencies,amino acid frequencies,and genotype frequencies),and genomic context.Compared with established predictors,mvPPT achieves superior performance in all test sets,regardless of data source.In addition,our study also provides guidance for training set and feature selection strategies,as well as reveals highly relevant features,which may further provide biological insights into variant pathogenicity. 展开更多
关键词 Machine learning Missensevariant GENOMICS Computational biology Pathogenicityprediction
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