Identifying and ranking potential cancer drivers using representation learning on attributed network.
暂无分享,去创建一个
Jianxin Wang | Wei Peng | Wei Dai | Sichen Yi | Jianxin Wang | Wei Peng | Wei Dai | Sichen Yi
[1] F. Supek,et al. MUFFINN: cancer gene discovery via network analysis of somatic mutation data , 2016, Genome Biology.
[2] Wei Peng,et al. An Entropy-Based Method for Identifying Mutual Exclusive Driver Genes in Cancer , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[3] Eli Upfal,et al. Algorithms for Detecting Significantly Mutated Pathways in Cancer , 2010, RECOMB.
[4] Xing-Ming Zhao,et al. Identifying Disease Associated miRNAs Based on Protein Domains , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[5] Xing-Ming Zhao,et al. Integrative analysis of mutational and transcriptional profiles reveals driver mutations of metastatic breast cancers , 2016, Cell Discovery.
[6] Gary D Bader,et al. International network of cancer genome projects , 2010, Nature.
[7] Feng Xu,et al. A Brief Review of Network Embedding , 2019, Big Data Min. Anal..
[8] Alberto Montresor,et al. gat2vec: representation learning for attributed graphs , 2018, Computing.
[9] S. Gabriel,et al. Discovery and saturation analysis of cancer genes across 21 tumor types , 2014, Nature.
[10] Ao Li,et al. Discovering potential driver genes through an integrated model of somatic mutation profiles and gene functional information. , 2017, Molecular bioSystems.
[11] Hong Yan,et al. DrPOCS: Drug Repositioning Based on Projection Onto Convex Sets , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[12] Mingming Jia,et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer , 2010, Nucleic Acids Res..
[13] David Haussler,et al. Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE) , 2013, Bioinform..
[14] Lin Gao,et al. Discovering potential cancer driver genes by an integrated network-based approach. , 2016, Molecular bioSystems.
[15] Ke Zhang,et al. Network representation based on the joint learning of three feature views , 2019, Big Data Min. Anal..
[16] Joshua M. Stuart,et al. The Cancer Genome Atlas Pan-Cancer analysis project , 2013, Nature Genetics.
[17] Xing-Ming Zhao,et al. Predicting drug-disease associations with heterogeneous network embedding. , 2019, Chaos.
[18] Feng Wang,et al. A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph , 2019, BMC Bioinformatics.
[19] Christian Stolte,et al. COMPARTMENTS: unification and visualization of protein subcellular localization evidence , 2014, Database J. Biol. Databases Curation.
[20] Shi-Hua Zhang,et al. Sparse Deep Nonnegative Matrix Factorization , 2017, Big Data Min. Anal..
[21] Jure Leskovec,et al. node2vec: Scalable Feature Learning for Networks , 2016, KDD.
[22] Santhilata Kuppili Venkata,et al. The Network of Cancer Genes (NCG): a comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens , 2018, Genome Biology.
[23] Benjamin J. Raphael,et al. De novo discovery of mutated driver pathways in cancer , 2011 .
[24] Wei Peng,et al. Network Embedding the Protein–Protein Interaction Network for Human Essential Genes Identification , 2020, Genes.
[25] Steven A. Roberts,et al. Mutational heterogeneity in cancer and the search for new cancer genes , 2014 .
[26] L. Stein,et al. A human functional protein interaction network and its application to cancer data analysis , 2010, Genome Biology.
[27] Zhongming Zhao,et al. Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes , 2016, Briefings Bioinform..