J
Jianing Xi
Researcher at University of Science and Technology of China
Publications - 30
Citations - 444
Jianing Xi is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Computer science & Cancer. The author has an hindex of 11, co-authored 24 publications receiving 259 citations. Previous affiliations of Jianing Xi include Xidian University & Northwestern Polytechnical University.
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A novel heterogeneous network-based method for drug response prediction in cancer cell lines.
TL;DR: This study proposes a novel heterogeneous network-based method, named as HNMDRP, to accurately predict cell line-drug associations through incorporating heterogeneity relationship among cell line, drug and target and can make good use of above heterogeneous information to predict drug responses.
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Inferring subgroup-specific driver genes from heterogeneous cancer samples via subspace learning with subgroup indication.
TL;DR: This work proposes a novel bioinformatics method called DriverSub, which can efficiently predict subgroup specific driver genes in the situation where the subgroup annotations are not available, and yields the best prediction of driver genes and the inference of their related subgroups.
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PTM-ssMP: A Web Server for Predicting Different Types of Post-translational Modification Sites Using Novel Site-specific Modification Profile.
TL;DR: A web server to predict PTM site, which adopts site-specific modification profile (ssMP) to efficiently extract and encode the information of both proximal PTMs and local sequence simultaneously, and suggests that ssMP consistently contributes to remarkable improvement of prediction performance.
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CNV_IFTV: An Isolation Forest and Total Variation-Based Detection of CNVs from Short-Read Sequencing Data
TL;DR: CNV_IFTV is a reliable tool for detecting CNVs from short-read sequencing data even for low-level coverage and tumor purity and is tested on both simulated and real data in comparison to several peer methods.
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A novel unsupervised learning model for detecting driver genes from pan-cancer data through matrix tri-factorization framework with pairwise similarities constraints
Jianing Xi,Ao Li,Minghui Wang +2 more
TL;DR: A novel unsupervised learning model based on matrix tri-factorization by learning the similarities from pairwise constraints to detect driver genes from pan-cancer data is proposed, which achieves better performance than those of the existing matrix factorization based methods which do not consider the pairwise similarities between cancers.