L2,1-GRMF: an improved graph regularized matrix factorization method to predict drug-target interactions

被引：0
|
作者：
Zhen Cui
Ying-Lian Gao
Jin-Xing Liu
Ling-Yun Dai
Sha-Sha Yuan
机构：
[1] Qufu Normal University,School of Information Science and Engineering
[2] Qufu Normal University,Library of Qufu Normal University
[3] Anhui University,Co
来源：
BMC Bioinformatics | / 20卷
关键词：
Drug-target interaction prediction; Graph regularization; L; -norm; Matrix factorization; Manifold learning;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [21] Regularized Semi-Nonnegative Matrix Factorization Using L2,1-Norm for Data Compression
    Rhodes, Anthony
    Jiang, Bin
    2021 DATA COMPRESSION CONFERENCE (DCC 2021), 2021, : 365 - 365
  • [22] Predicting drug-target interactions by dual-network integrated logistic matrix factorization
    Ming Hao
    Stephen H. Bryant
    Yanli Wang
    Scientific Reports, 7
  • [23] Predicting drug-target interactions by dual-network integrated logistic matrix factorization
    Hao, Ming
    Bryant, Stephen
    Wang, Yanli
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2017, 254
  • [24] Graph regularized non-negative matrix factorization with prior knowledge consistency constraint for drug–target interactions prediction
    Junjun Zhang
    Minzhu Xie
    BMC Bioinformatics, 23
  • [25] Microbe-disease associations prediction by graph regularized non-negative matrix factorization with L2,1 norm regularization terms
    Chen, Ziwei
    Zhang, Liangzhe
    Li, Jingyi
    Chen, Hang
    JOURNAL OF CELLULAR AND MOLECULAR MEDICINE, 2024, 28 (17)
  • [26] DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches
    Olayan, Rawan S.
    Ashoor, Haitham
    Bajic, Vladimir B.
    BIOINFORMATICS, 2018, 34 (07) : 1164 - 1173
  • [27] l2,1 Norm and Hessian Regularized Non-Negative Matrix Factorization with Discriminability for Data Representation
    Luo, Peng
    Peng, Jinye
    Fan, Jianping
    APPLIED SCIENCES-BASEL, 2017, 7 (10):
  • [28] A unified solution for different scenarios of predicting drug-target interactions via triple matrix factorization
    Shi, Jian-Yu
    Zhang, An-Qi
    Zhang, Shao-Wu
    Mao, Kui-Tao
    Yiu, Siu-Ming
    BMC SYSTEMS BIOLOGY, 2018, 12
  • [29] Identification of drug-target interactions via multi-view graph regularized link propagation model
    Ding, Yijie
    Tang, Jijun
    Guo, Fei
    NEUROCOMPUTING, 2021, 461 : 618 - 631
  • [30] Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization
    Gonen, Mehmet
    BIOINFORMATICS, 2012, 28 (18) : 2304 - 2310
12345