Affinity network fusion and semi-supervised learning for cancer patient clustering

Methods

Volumn 145, Issue , 2018, Pages 16-24

  Ma, Tianle ^a   Zhang, Aidong ^a  

^a the State University of New York   (United States)

Author keywords

Affinity network fusion; Cancer subtype discovery; Multi omic integration; Neural network; Patient clustering; Semi supervised learning

Indexed keywords

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CANCER PATIENT; CLASSIFIER; CLUSTER ANALYSIS; INFORMATION PROCESSING; MATHEMATICAL ANALYSIS; OMICS; PRIORITY JOURNAL; SUPERVISED MACHINE LEARNING; BIOLOGY; HUMAN; NEOPLASM; PROCEDURES;

ANTINEOPLASTIC AGENT;

ANTINEOPLASTIC AGENTS; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; HUMANS; NEOPLASMS; NEURAL NETWORKS (COMPUTER); SUPERVISED MACHINE LEARNING;

EID: 85048591696     PISSN: 10462023     EISSN: 10959130     Source Type: Journal    
DOI: 10.1016/j.ymeth.2018.05.020     Document Type: Article

References (12)

1. Monti, S., Tamayo, P., Mesirov, J., Golub, T., Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn. 52:1 (2003), 91–118.
2. Bersanelli, M., Mosca, E., Remondini, D., Giampieri, E., Sala, C., Castellani, G., Milanesi, L., Methods for the integration of multi-omics data: mathematical aspects. BMC Bioinformatics, 17(2), 2016, S15, 10.1186/s12859-015-0857-9.
3. Wang, B., Mezlini, A.M., Demir, F., Fiume, M., Tu, Z., Brudno, M., Haibe-Kains, B., Goldenberg, A., Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 11:3 (2014), 333–337.
4. Shen, R., Mo, Q., Schultz, N., Seshan, V.E., Olshen, A.B., Huse, J., Ladanyi, M., Sander, C., Integrative subtype discovery in glioblastoma using icluster. PLOS ONE 7:4 (2012), 1–9, 10.1371/journal.pone.0035236.
5. Meng, C., Zeleznik, O.A., Thallinger, G.G., Kuster, B., Gholami, A.M., Culhane, A.C., Dimension reduction techniques for the integrative analysis of multi-omics data. Briefings Bioinformatics 17:4 (2016), 628–641, 10.1093/bib/bbv108.
6. Vaske, C.J., Benz, S.C., Sanborn, J.Z., Earl, D., Szeto, C., Zhu, J., Haussler, D., Stuart, J.M., Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using paradigm. Bioinformatics 26:12 (2010), i237–i245, 10.1093/bioinformatics/btq182.
7. T. Ma, A. Zhang, Integrate multi-omic data using affinity network fusion (anf) for cancer patient clustering, arXiv preprint arXiv:1708.07136.
8. Ma, T., Zhang, A., A framework for robust differential network modular structure discovery from rna-seq data. 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2016, IEEE, 288–293.
9. Luxburg, U., A tutorial on spectral clustering. Stat. Comput. 17:4 (2007), 395–416, 10.1007/s11222-007-9033-z.
10. K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
11. Hubert, L., Arabie, P., Comparing partitions. J. Classif. 2:1 (1985), 193–218, 10.1007/BF01908075.
12. H. Wang, B. Aragam, E. P. Xing, Variable selection in heterogeneous datasets: A truncated-rank sparse linear mixed model with applications to genome-wide association studies, bioRxiv doi:10.1101/228106. https://www.biorxiv.org/content/early/2017/12/03/228106.