1 |
Hsu YHH, Churchhouse C, Pers TH, et al. (2019). PAIRUP-MS: Pathway analysis and imputation to relate unknowns in profiles from mass spectrometry-based metabolite data, PLoS Computational Biology, 15, e1006734.
DOI
|
2 |
Johnson J, Thijssen B, McDermott U, Garnett M,Wessels LFA, and Bernards R (2016). Targeting the RB-E2F pathway in breast cancer, Oncogene, 35, 4829.
DOI
|
3 |
Kaenel P, Mosimann M, and Andres AC (2012). The multifaceted roles of Eph/ephrin signaling in breast cancer, Cell Adhesion & Migration, 6, 138-147.
DOI
|
4 |
Koksal AS, Beck K, Cronin DR, et al. (2018). Synthesizing signaling pathways from temporal phosphoproteomic data, Cell Reports, 24, 3607-3618.
DOI
|
5 |
Krause RW, Huisman M, and Snijders TA (2018). Multiple imputation for longitudinal network data, Italian Journal of Applied Statistics, 30, 33-58.
|
6 |
Kramer N, Schafer J, and Boulesteix AL (2009). Regularized estimation of large-scale gene association networks using graphical Gaussian models, BMC Bioinformatics, 10, 384.
DOI
|
7 |
Liu F (2011). Inhibition of Smad3 activity by cyclin D-CDK4 and cyclin E-CDK2 in breast cancer cells, Cell Cycle, 10, 190-191.
|
8 |
Ma J, Lyu H, Huang J, and Liu B (2014). Targeting of erbB3 receptor to overcome resistance in cancer treatment, Molecular Cancer, 13, 105.
DOI
|
9 |
Mazumder R, Hastie T, and Tibshirani R (2010). Spectral regularization algorithms for learning large incomplete matrices, Journal of Machine Learning Research, 11, 2287-2322.
|
10 |
Nevins JR (2001). The Rb/E2F pathway and cancer, Human Molecular Genetics, 10, 699-703.
DOI
|
11 |
Schulz H, Ruppert AK, Herms S, et al. (2017). Genome-wide mapping of genetic determinants influencing DNA methylation and gene expression in human hippocampus, Nature Communications, 8, 1511.
DOI
|
12 |
Pasquale EB (2010). Eph receptors and ephrins in cancer: bidirectional signalling and beyond, Nature Reviews Cancer, 10, 165.
DOI
|
13 |
Sales G, Calura E, and Romualdi C (2018). graphite: GRAPH Interaction from pathway Topological Environment, R package version 1.26.1.
|
14 |
Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, and Buetow KH (2008). PID: the pathway interaction database, Nucleic Acids Research, 37, D674-D679.
DOI
|
15 |
Shen R, Olshen AB, and Ladanyi M (2009). Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis, Bioinformatics, 25, 2906-2912.
DOI
|
16 |
Shojaie A and Michailidis G (2009). Analysis of gene sets based on the underlying regulatory network, Journal of Computational Biology, 16, 407-426.
DOI
|
17 |
Tang YN, Ding WQ, Guo XJ, Yuan XW, Wang DM, and Song JG (2015). Epigenetic regulation of Smad2 and Smad3 by profilin-2 promotes lung cancer growth and metastasis, Nature Communications, 6, 8230.
DOI
|
18 |
Shojaie A and Michailidis G (2010). Network enrichment analysis in complex experiments, Statistical Applications in Genetics and Molecular Biology, 9, 22.
DOI
|
19 |
Sommer S and Fuqua SA (2001). Estrogen receptor and breast cancer, Seminars in Cancer Biology, 11, 339-352.
DOI
|
20 |
Subramanian A, Tamayo P, Mootha VK, et al. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. In Proceedings of the National Academy of Sciences, 102, 15545-15550.
DOI
|
21 |
Tarasewicz E, Rivas L, Hamdan R, et al. (2014). Inhibition of CDK-mediated phosphorylation of Smad3 results in decreased oncogenesis in triple negative breast cancer cells, Cell Cycle, 13, 3191-3201.
DOI
|
22 |
Thomas AL, Lind H, Hong A, et al. (2017). Inhibition of CDK-mediated Smad3 phosphorylation reduces the Pin1-Smad3 interaction and aggressiveness of triple negative breast cancer cells, Cell Cycle, 16, 1453-1464.
DOI
|
23 |
Tomczak K, Czerwinska P, and Wiznerowicz M (2015). The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge, Contemporary Oncology, 19, A68.
|
24 |
Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, and Altman RB (2001). Missing value estimation methods for DNA microarrays, Bioinformatics, 17, 520-525.
DOI
|
25 |
Tsuchiya T, Fujii M, Matsuda N, Kunida K, Uda S, Kubota H, Konishi K, and Kuroda S (2017). System identification of signaling dependent gene expression with different time-scale data, PLoS Computational Biology, 13, e1005913.
DOI
|
26 |
Bochkis IM, Schug J, Diana ZY, Kurinna S, Stratton SA, Barton MC, and Kaestner KH (2012). Genome-wide location analysis reveals distinct transcriptional circuitry by paralogous regulators Foxa1 and Foxa2, PLoS Genetics, 8, e1002770.
DOI
|
27 |
Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein MY, Geiger T, Mann M, and Cox J (2016). The Perseus computational platform for comprehensive analysis of (prote) omics data, Nature Methods, 13, 731.
DOI
|
28 |
Vaske CJ, Benz SC, Sanborn JZ, Earl D, Szeto C, Zhu J, Haussler D, and Stuart JM (2010). Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM, Bioinformatics, 26, i237-i245.
DOI
|
29 |
Almende BV, Thieurmel B, and Robert T (2018). visNetwork: Network Visualization using vis.js Library, R package version 2.0.4, https://CRAN.R-project.org/package=visNetwork
|
30 |
Benjamini Y and Hochberg Y (1995). Controlling the false discovery rate a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B (Methodological), 57, 289-300.
DOI
|
31 |
Brown KA, Pietenpol JA, and Moses HL (2007). A tale of two proteins: Differential roles and regulation of Smad2 and Smad3 in TGF-beta signaling, Journal of Cellular Biochemistry, 101, 9-33.
DOI
|
32 |
Cai T, Cai TT, and Zhang A (2016). Structured matrix completion with applications to genomic data integration, Journal of the American Statistical Association, 111, 621-633.
DOI
|
33 |
Candes EJ and Tao T (2010). The power of convex relaxation: Near-optimal matrix completion, IEEE Transactions on Information Theory, 56, 2053-2080.
DOI
|
34 |
Zhang Y, Linder MH, Shojaie A, Ouyang Z, Shen R, Baggerly KA, Baladandayuthapani V, and Zhao H (2017a). Dissecting pathway disturbances using network topology and multi-platform genomics data, Statistics in Biosciences, 10, 1-21.
|
35 |
Wei L, Jin Z, Yang S, Xu Y, Zhu Y, and Ji Y (2017). TCGA-assembler 2: software pipeline for retrieval and processing of TCGA/CPTAC data, Bioinformatics, 34, 1615-1617.
|
36 |
Wu D, Lim E, Vaillant F, Asselin-Labat ML, Visvader JE, and Smyth GK (2010). ROAST: rotation gene set tests for complex microarray experiments, Bioinformatics, 26, 2176-2182.
DOI
|
37 |
Zelivianski S, Cooley A, Kall R, and Jeruss JS (2010). Cyclin-dependent kinase 4-mediated phosphorylation inhibits Smad3 activity in cyclin D-overexpressing breast cancer Cells, Molecular Cancer Research, 8, 1375-1387.
DOI
|
38 |
Zhang Y, Ouyang Z, and Zhao H (2017b). A statistical framework for data integration through graphical models with application to cancer genomics, The Annals of Applied Statistics, 11, 161-184.
DOI
|
39 |
Zhao Y, Hoang TH, Joshi P, Hong SH, Giardina C, and Shin DG (2017). A route-based pathway analysis framework integrating mutation information and gene expression data, Methods, 124, 3-12.
DOI
|
40 |
Zhou X, Carbonetto P, and Stephens M (2013). Polygenic modeling with Bayesian sparse linear mixed models, PLoS Genetics, 9, e1003264.
DOI
|
41 |
Csardi G and Nepusz T (2006). The igraph software package for complex network research, Inter-Journal, Complex Systems, 1695.
|
42 |
Chang W, Cheng J, Allaire JJ, Xie Y, and McPherson J (2018). shiny: Web Application Framework for R, R package version 1.2.0, https://CRAN.R-project.org/package=shiny
|
43 |
Cheang MCU, Chia SK, Voduc D, et al. (2009). Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer, JNCI: Journal of the National Cancer Institute, 101, 736-750.
DOI
|
44 |
Chudasama P, Mughal SS, Sanders MA, et al. (2018). Integrative genomic and transcriptomic analysis of leiomyosarcoma, Nature Communications, 9, 144.
DOI
|
45 |
Dai X, Li T, Bai Z, Yang Y, Liu X, Zhan J, and Shi B (2015). Breast cancer intrinsic subtype classification, clinical use and future trends, American Journal of Cancer Research, 5, 2929.
|
46 |
Danielsen SA, Eide PW, Nesbakken A, Guren T, Leithe E, and Lothe RA (2015). Portrait of the PI3K/AKT pathway in colorectal cancer, Biochimica et Biophysica Acta (BBA)-Reviews on Cancer, 1855, 104-121.
DOI
|
47 |
Driver KE, Song H, Lesueur F, et al. (2008). Association of single-nucleotide polymorphisms in the cell cycle genes with breast cancer in the British population, Carcinogenesis, 29, 333-341.
DOI
|
48 |
Franzin A, Sambo F, and di Camillo B (2017). bnstruct: an R package for Bayesian Network structure learning in the presence of missing data, Bioinformatics, 33, 1250-1252.
|
49 |
Zhu Y, Qiu P, and Ji Y (2014). TCGA-assembler: open-source software for retrieving and processing TCGA data, Nature Methods, 11, 599.
DOI
|
50 |
Fryett JJ, Inshaw J, Morris AP, and Cordell HJ (2018). Comparison of methods for transcriptome imputation through application to two common complex diseases, European Journal of Human Genetics, 26, 1658-1667.
DOI
|
51 |
Gamazon ER, Wheeler HE, Shah KP, et al. (2015). A gene-based association method for mapping traits using reference transcriptome data, Nature Genetics, 47, 1091.
DOI
|
52 |
Grossman RL, Heath AP, Ferretti V, Varmus HE, Lowy DR, Kibbe WA, and Staudt LM (2016). Toward a shared vision for cancer genomic data, New England Journal of Medicine, 375, 1109-1112.
DOI
|
53 |
Gusev A, Ko A, Shi H, et al. (2016). Integrative approaches for large-scale transcriptome-wide association studies, Nature Genetics, 48, 245.
DOI
|
54 |
Howie BN, Donnelly P, and Marchini J (2009). PLoS Genetics, A flexible and accurate genotype imputation method for the next generation of genome-wide association studies, 5, e1000529.
DOI
|