Patient-specific data fusion defines prognostic cancer subtypes
Open accessAbstract
Different data types can offer complementary perspectives on the same biological phenomenon. In cancer studies, for example, data on copy number alterations indicate losses and amplifications of genomic regions in tumours, while transcriptomic data point to the impact of genomic and environmental events on the internal wiring of the cell. Fusing different data provides a more comprehensive model of the cancer cell than that offered by any single type. However, biological signals in different patients exhibit diverse degrees of concordance due to cancer heterogeneity and inherent noise in the measurements. This is a particularly important issue in cancer subtype discovery, where personalised strategies to guide therapy are of vital importance. We present a nonparametric Bayesian model for discovering prognostic cancer subtypes by integrating gene expression and copy number variation data. Our model is constructed from a hierarchy of Dirichlet Processes and addresses three key challenges in data fusion: (i) To separate concordant from discordant signals, (ii) to select informative features, (iii) to estimate the number of disease subtypes. Concordance of signals is assessed individually for each patient, giving us an additional level of insight into the underlying disease structure. We exemplify the power of our model in prostate cancer and breast cancer and show that it outperforms competing methods. In the prostate cancer data, we identify an entirely new subtype with extremely poor survival outcome and show how other analyses fail to detect it. In the breast cancer data, we find subtypes with superior prognostic value by using the concordant results. These discoveries were crucially dependent on our model's ability to distinguish concordant and discordant signals within each patient sample, and would otherwise have been missed. We therefore demonstrate the importance of taking a patient-specific approach, using highly-flexible nonparametric Bayesian methods.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
PSDF is a non-parametric Bayesian model for clustering prognostic cancer subtypes by combining gene expression and copy number variation data.
“PSDF (Patient-Specific Data Fusion) (Yuan et al., 2011) is a non-parametric Bayesian model for clustering prognostic cancer subtypes by combining gene expression and copy number variation data.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Yuan et al., 2011
PSDF uses a two-step process and limits integration to only two datatypes.
“It uses a two-step process and limits the integration to only two datatypes.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Yuan et al., 2011
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling
Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., et al. (2000) · Nature · doi:10.1038/35000501 · Cited by 9,925 · see citing works
KEGG for linking genomes to life and the environment
Kanehisa, M. I., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., Tokimatsu, T., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm882 · Cited by 6,828 · see citing works
limma: Linear Models for Microarray Data
Smyth, G. K. (2005) · Statistics for Biology and Health · doi:10.1007/0-387-29362-0_23 · Cited by 5,796 · see citing works
Repeated observation of breast tumor subtypes in independent gene expression data sets
Sørlie, T., Tibshirani, R. J., Parker, J. S., Hastie, T. J., Marron, J. S., Nobel, A. B., Deng, S., Johnsen, H., Pesich, R., Geisler, S. B., et al. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0932692100 · Cited by 5,428 · see citing works
A Bayesian Analysis of Some Nonparametric Problems
Ferguson, T. S. (1973) · The Annals of Statistics · doi:10.1214/aos/1176342360 · Cited by 4,814 · see citing works
Integrative Genomic Profiling of Human Prostate Cancer
Taylor, B. S., Schultz, N. D., Hieronymus, H., Gopalan, A., Xiao, Y., Carver, B. S., Arora, V. K., Kaushik, P., Cerami, E. G., Reva, B. A., et al. (2010) · Cancer Cell · doi:10.1016/j.ccr.2010.05.026 · Cited by 3,653 · see citing works
Bioinformatics and Computational Biology Solutions Using R and Bioconductor
Gentleman, R. C., Carey, V. J., Huber, W. K., Irizarry, R. A., & Dudoit, S. (2005) · Statistics in the health sciences · doi:10.1007/0-387-29362-0 · Cited by 3,470 · see citing works
Human Protein Reference Database--2009 update
Prasad, T. S. K., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., Telikicherla, D., Raju, R., Shafreen, R. B., Venugopal, A., et al. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn892 · Cited by 3,205 · see citing works
Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation
Merico, D., Isserlin, R., Stueker, O., Emili, A., & Bader, G. D. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013984 · Cited by 2,235 · see citing works
Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems
Antoniak, C. E. (1974) · The Annals of Statistics · doi:10.1214/aos/1176342871 · Cited by 1,976 · see citing works
Genomic and transcriptional aberrations linked to breast cancer pathophysiologies
Chin, K., DeVries, S., Fridlyand, J., Spellman, P. T., Roydasgupta, R., Kuo, W.-L., Lapuk, A. V., Neve, R. M., Qian, Z., Ryder, T., et al. (2006) · Cancer Cell · doi:10.1016/j.ccr.2006.10.009 · Cited by 1,288 · see citing works
A Biologic Definition of Burkitt's Lymphoma from Transcriptional and Genomic Profiling
Hummel, M., Bentink, S., Berger, H., Klapper, W., Weßendorf, S., Barth, T. F. E., Bernd, H., Cogliatti, S., Dierlamm, J., Feller, A. C., et al. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa055351 · Cited by 1,002 · see citing works
The Infinite Gaussian Mixture Model
Rasmussen, C. E. (1999) · Cited by 985 · see citing works
Deregulated proteolysis by the F-box proteins SKP2 and β-TrCP: tipping the scales of cancer
Frescas, D., & Pagano, M. (2008) · Nature reviews. Cancer · doi:10.1038/nrc2396 · Cited by 886 · see citing works
Shen, R., Olshen, A. B., & Ladanyi, M. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp543 · Cited by 849 · see citing works
A module map showing conditional activity of expression modules in cancer
Segal, E., Friedman, N., Koller, D., & Regev, A. (2004) · Nature Genetics · doi:10.1038/ng1434 · Cited by 685 · see citing works
Shen, R., Olshen, A. B., & Ladanyi, M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp659 · Cited by 566 · see citing works
Chin, S., Teschendorff, A. E., Marioni, J. C., Wang, Y., Barbosa‐Morais, N. L., Thorne, N., Costa, J. L., Pinder, S. E., van de Wiel, M. A., Green, A. R., et al. (2007) · Genome biology · doi:10.1186/gb-2007-8-10-r215 · Cited by 320 · see citing works
Model-Based Clustering for Expression Data via a Dirichlet Process Mixture Model
Dahl, D. B. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511584589.011 · Cited by 294 · see citing works
TP53mutation status and gene expression profiles are powerful prognostic markers of breast cancer
Langerød, A., Zhao, H., Borgan, Ø., Nesland, J. M., Bukholm, I. R., Ikdahl, T., Kåresen, R., Børresen‐Dale, A., & Jeffrey, S. S. (2007) · Breast Cancer Research · doi:10.1186/bcr1675 · Cited by 291 · see citing works
Bayesian infinite mixture model based clustering of gene expression profiles
Medvedovic, M., & Sivaganesan, S. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.9.1194 · Cited by 285 · see citing works
CGHcall: calling aberrations for array CGH tumor profiles
van de Wiel, M. A., Kim, K. I., Vosse, S. J., van Wieringen, W. N., Wilting, S. M., & Ylstra, B. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm030 · Cited by 240 · see citing works
Systems Biology and Genomics of Breast CancerPreprint
Perou, C. M., & Børresen‐Dale, A. (2010) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a003293 · Cited by 240 · see citing works
BioNet: an R-Package for the functional analysis of biological networks
Beißer, D., Klau, G. W., Dandekar, T., Müller, T., & Dittrich, M. T. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq089 · Cited by 238 · see citing works
Warnat, P., Eils, R., & Brors, B. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-265 · Cited by 228 · see citing works
Bayesian mixture model based clustering of replicated microarray data
Medvedovic, M., Yeung, K. Y., & Bumgarner, R. E. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth068 · Cited by 214 · see citing works
Pathway-specific differences between tumor cell lines and normal and tumor tissue cells
Ertel, A., Verghese, A. I., Byers, S. W., Ochs, M. F., & Tözeren, A. (2006) · Molecular Cancer · doi:10.1186/1476-4598-5-55 · Cited by 213 · see citing works
Endocytosis of titanium dioxide nanoparticles in prostate cancer PC-3M cells
Thurn, K. T., Arora, H., Paunesku, T., Wu, A., Brown, E. M. B., Doty, C. B., Kremer, J. C., & Woloschak, G. E. (2010) · Nanomedicine Nanotechnology Biology and Medicine · doi:10.1016/j.nano.2010.09.004 · Cited by 158 · see citing works
HTSanalyzeR: an R/Bioconductor package for integrated network analysis of high-throughput screens
Wang, X., Terfve, C., Rose, J. C., & Markowetz, F. M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr028 · Cited by 130 · see citing works
Self-citationClustering microarray gene expression data using weighted Chinese restaurant process
Qin, Z. S. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl284 · Cited by 130 · see citing works
Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge
Geier, F. M., Timmer, J., & Fleck, C. (2007) · BMC Systems Biology · doi:10.1186/1752-0509-1-11 · Cited by 120 · see citing works
Cluster stability scores for microarray data in cancer studies
Smolkin, M., & Ghosh, D. (2003) · BMC Bioinformatics · doi:10.1186/1471-2105-4-36 · Cited by 115 · see citing works
Polo, S., Pece, S., & Fiore, P. P. D. (2004) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2004.02.003 · Cited by 107 · see citing works
Furge, K. A., Lucas, K. A., Takahashi, M., Sugimura, J., Kort, E. J., Kanayama, H.-O., Kagawa, S., Hoekstra, P., Curry, J., Yang, X. J., et al. (2004) · Cancer Research · doi:10.1158/0008-5472.can-04-0534 · Cited by 107 · see citing works
Bayesian Inference for Gene Expression and Proteomics
Vannucci, M. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511584589 · Cited by 94 · see citing works
CGHregions: dimension reduction for array CGH data with minimal information loss.
van de Wiel, M. A., & van Wieringen, W. N. (2007) · PubMed Central · Cited by 91 · see citing works
Sieuwerts, A. M., Look, M. P., Gelder, M. E. M., Timmermans, M. A. M., Trapman-Jansen, A., Garcia, R. R., Arnold, M., Goedheer, A. J. W., de Weerd, V., Portengen, H., et al. (2006) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-06-0225 · Cited by 90 · see citing works
CGHregions: Dimension Reduction for Array CGH Data with Minimal Information Loss
van de Wiel, M. A., & van Wieringen, W. N. (2007) · Cancer Informatics · doi:10.1177/117693510700300031 · Cited by 81 · see citing works
Chemokine signaling in cancer: One hump or two?
Rubin, J. B. (2008) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2008.10.001 · Cited by 80 · see citing works
Chemokine signaling in cancer: Implications on the tumor microenvironment and therapeutic targeting.
Hembruff, S. L., & Cheng, N. (2009) · PubMed · Cited by 71 · see citing works
Discovering transcriptional modules by Bayesian data integration
Savage, R. S., Zoubin Ghahramani, A., Griffin, J. E., de la Cruz, B. J., & Wild, D. L. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq210 · Cited by 65 · see citing works
Self-citationZheng, C., Ren, Z., Wang, H., Zhang, W., Kalvakolanu, D. V., Tian, Z., & Xiao, W. (2009) · Cancer Research · doi:10.1158/0008-5472.can-08-4113 · Cited by 58 · see citing works
Jointly Analyzing Gene Expression and Copy Number Data in Breast Cancer Using Data Reduction Models
Berger, J. A., Hautaniemi, S., Mitra, S. K., & Astola, J. T. (2006) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2006.10 · Cited by 50 · see citing works
Modeling and Visualizing Uncertainty in Gene Expression Clusters Using Dirichlet Process Mixtures
Rasmussen, C. E., de la Cruz, B. J., Zoubin Ghahramani, A., & Wild, D. L. (2009) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2007.70269 · Cited by 41 · see citing works
Miecznikowski, J. C., Wang, D., Liu, S., Sucheston, L., & Gold, D. L. (2010) · BMC Cancer · doi:10.1186/1471-2407-10-573 · Cited by 30 · see citing works
Bicciato, S., Spinelli, R., Zampieri, M., Mangano, E., Ferrari, F., Beltrame, L., Cifola, I., Peano, C., Solari, A., & Battaglia, C. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp520 · Cited by 30 · see citing works
Liu, X., Sivaganesan, S., Yeung, K. Y., Guo, J., Bumgarner, R. E., & Medvedovic, M. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl184 · Cited by 27 · see citing works
Combining Sequence and Time Series Expression Data to Learn Transcriptional Modules
Kundaje, A. B., Middendorf, M., Gao, F., Wiggins, C. H., & Leslie, C. S. (2005) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2005.34 · Cited by 25 · see citing works
Jiang, M. H., Li, M., Fu, X., Huang, Y., Qian, H., Sun, R., Mao, Y., Xie, Y., & Li, Y. (2008) · The Prostate · doi:10.1002/pros.20756 · Cited by 22 · see citing works
A Bayesian approach to modelling uncertainty in gene expression clusters
Wild, D., Rasmussen, C. E., Zoubin Ghahramani, A., Cregg, J. M., de la Cruz, B., Kan, C., & Scanlon, K. J. (2002) · Cambridge University Engineering Department Publications Database · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Multi-omics Data Integration, Interpretation, and Its Application
Subramanian, I., Verma, S., Kumar, S., Jere, A., & Anamika, K. (2020) · Bioinformatics and Biology Insights · doi:10.1177/1177932219899051 · Cited by 1,609 · see citing works
Using machine learning approaches for multi-omics data analysis: A review
Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107739 · Cited by 903 · see citing works
More Is Better: Recent Progress in Multi-Omics Data Integration Methods
Huang, S., Chaudhary, K., & Garmire, L. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00084 · Cited by 637 · see citing works
Zitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A., & Hoffman, M. M. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.012 · Cited by 675 · see citing works
Integrated omics: tools, advances and future approaches
Misra, B. B., Langefeld, C., Olivier, M., & Cox, L. A. (2019) · Journal of Molecular Endocrinology · doi:10.1530/jme-18-0055 · Cited by 686 · see citing works
Multi-omic and multi-view clustering algorithms: review and cancer benchmark
Rappoport, N., & Ron Shamir, A. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky889 · Cited by 549 · see citing works
Applications of multi‐omics analysis in human diseases
Chen, C., Wang, J., Pan, D., Wang, X., Xu, Y., Yan, J., Wang, L., Yang, X., Yang, M., & Liu, G. (2023) · MedComm · doi:10.1002/mco2.315 · Cited by 545 · see citing works
Pattern discovery and cancer gene identification in integrated cancer genomic data
Mo, Q., Wang, S., Seshan, V. E., Olshen, A. B., Schultz, N., Sander, C., Powers, R. S., Ladanyi, M., & Shen, R. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1208949110 · Cited by 474 · see citing works
Methods for the integration of multi-omics data: mathematical aspects
Bersanelli, M., Mosca, E., Remondini, D., Giampieri, E., Sala, C., Castellani, G., & Milanesi, L. (2016) · BMC Bioinformatics · doi:10.1186/s12859-015-0857-9 · Cited by 449 · see citing works
Vatansever, S., Schlessinger, A., Wacker, D., Kaniskan, H. Ü., Jin, J., Zhou, M., & Zhang, B. (2021) · Medicinal Research Reviews · doi:10.1002/med.21764 · Cited by 429 · see citing works
Principles and methods of integrative genomic analyses in cancer
Kristensen, V. N., Lingjærde, O. C., Russnes, H. G., Vollan, H. K. M., Frigessi, A., & Børresen‐Dale, A. (2014) · Nature reviews. Cancer · doi:10.1038/nrc3721 · Cited by 368 · see citing works
Lock, E. F., & Dunson, D. B. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt425 · Cited by 308 · see citing works
Integrative multi-omics approaches in cancer research: from biological networks to clinical subtypes
Heo, Y. J., Hwa, C., Lee, G.-H., Park, J.-M., & An, J.-Y. (2021) · Molecules and Cells · doi:10.14348/molcells.2021.0042 · Cited by 294 · see citing works
Methods for biological data integration: perspectives and challenges
Gligorijević, V., & Pržulj, N. (2015) · Journal of The Royal Society Interface · doi:10.1098/rsif.2015.0571 · Cited by 274 · see citing works
Wu, D., Wang, D., Zhang, M. Q., & Gu, J. (2015) · BMC Genomics · doi:10.1186/s12864-015-2223-8 · Cited by 229 · see citing works
Bayesian correlated clustering to integrate multiple datasets
Kirk, P., Griffin, J. E., Savage, R. S., Zoubin Ghahramani, A., & Wild, D. L. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts595 · Cited by 228 · see citing works
Self-citationIntegrative methods for analyzing big data in precision medicine
Gligorijević, V., Malod‐Dognin, N., & Pržulj, N. (2015) · PROTEOMICS · doi:10.1002/pmic.201500396 · Cited by 216 · see citing works
Multi-omic approaches for host-microbiome data integration
Chetty, A., & Blekhman, R. (2024) · Gut Microbes · doi:10.1080/19490976.2023.2297860 · Cited by 183 · see citing works
Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer
Jendoubi, T. (2021) · Metabolites · doi:10.3390/metabo11030184 · Cited by 177 · see citing works
Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review
Vahabi, N., & Michailidis, G. C. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.854752 · Cited by 159 · see citing works
Variational autoencoders for cancer data integration: design principles and computational practice
Simidjievski, N., Bodnar, C., Tariq, I., Scherer, P., Andres Terre, H., Shams, Z., Jamnik, M., & Liò, P. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01205 · Cited by 145 · see citing works
Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology
Gönen, M., & Margolin, A. A. (2014) · neural information processing systems · Cited by 138 · see citing works
Evaluation and comparison of multi-omics data integration methods for cancer subtyping
Duan, R., Gao, L., Gao, Y., Hu, Y., Xu, H., Huang, M., Song, K., Wang, H., Dong, Y., Jiang, C., et al. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009224 · Cited by 122 · see citing works
Moving towards a molecular taxonomy of autoimmune rheumatic diseases
Barturen, G., Beretta, L., Cervera, R., van Vollenhoven, R., & Alarcón-Riquelme, M. E. (2018) · Nature Reviews Rheumatology · doi:10.1038/nrrheum.2017.220 · Cited by 125 · see citing works
iOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery
Koh, H. W. L., Fermin, D., Vogel, C., Choi, K. P., Ewing, R. M., & Choi, H. (2019) · npj Systems Biology and Applications · doi:10.1038/s41540-019-0099-y · Cited by 113 · see citing works
Memarian, N., Kim, S., Dewar, S. R., Engel, J., & Staba, R. J. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.06.008 · Cited by 109 · see citing works
Rahnenführer, J., de Bin, R., Benner, A., Ambrogi, F., Lusa, L., Boulesteix, A., Migliavacca, E., Binder, H., Michiels, S., Sauerbrei, W., et al. (2023) · BMC Medicine · doi:10.1186/s12916-023-02858-y · Cited by 103 · see citing works
Statistical Methods in Integrative Genomics
Richardson, S. T., Tseng, G. C., & Sun, W. (2016) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-041715-033506 · Cited by 97 · see citing works
Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine
Li, Y., Ma, L., Wu, D., & Chen, G. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab024 · Cited by 101 · see citing works
Ding, J., Blencowe, M., Nghiem, T. X., Ha, S. M., Chen, Y.-W., Li, G., & Yang, X. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab405 · Cited by 96 · see citing works
Machine learning: its challenges and opportunities in plant system biology
Hesami, M., Alizadeh, M., Jones, A. M. P., & Torkamaneh, D. (2022) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-022-11963-6 · Cited by 87 · see citing works
Evaluation of integrative clustering methods for the analysis of multi-omics data
Chauvel, C., Novoloaca, A., Veyre, P., Reynier, F., & Becker, J. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz015 · Cited by 84 · see citing works
A pathway-based data integration framework for prediction of disease progression
Seoane, J. A., Day, I. N. M., Gaunt, T. R., & Campbell, C. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt610 · Cited by 80 · see citing works
Matsuoka, T., & Yashiro, M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25115880 · Cited by 83 · see citing works
“…Pathway Recognition Algorithm using Data Integration on Genomic Models (PARADIGM) [97], iClusterPlus [98], Patient-specific data fusion (PSDF) [99]), network and pathway integration (e.g., Similarity network fusion (SNF) [100], PaintOmics [101], MultiGSEA [102], multi-omics gene-set analysis…”
MethodMulti-omics research strategies in ischemic stroke: A multidimensional perspective
Li, W., Shao, C., Zhou, H., Du, H., Chen, H., Wan, H., & He, Y. T. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101730 · Cited by 72 · see citing works
Lemsara, A., Ouadfel, S., & Fröhlich, H. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3465-2 · Cited by 64 · see citing works
Wani, N., & Raza, K. (2019) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2019.107120 · Cited by 60 · see citing works
From systems biology to P4 medicine: applications in respiratory medicine
Noell, G. G., Faner, R., & Agustí, A. G. N. (2018) · European Respiratory Review · doi:10.1183/16000617.0110-2017 · Cited by 60 · see citing works
Advance computational tools for multiomics data learning
Mansoor, S., Hamid, S., Tuan, T. T., Park, J.-E., & Chung, Y. S. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108447 · Cited by 60 · see citing works
Integrative Analysis of Omics Big Data
Yu, X., & Zeng, T. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7717-8_7 · Cited by 59 · see citing works
Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations
Oh, M., Park, S., Kim, S., & Chae, H. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa032 · Cited by 55 · see citing works
Breast cancer patient stratification using a molecular regularized consensus clustering method
Wang, C., Machiraju, R., & Huang, K. (2014) · Methods · doi:10.1016/j.ymeth.2014.03.005 · Cited by 47 · see citing works
Mallik, S., & Zhao, Z. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bby120 · Cited by 46 · see citing works
Chen, P., Huhtinen, K., Kaipio, K., Mikkonen, P., Aittomäki, V., Lindell, R., Hynninen, J., Auranen, A., Grénman, S., Lehtonen, R., et al. (2015) · Cancer Research · doi:10.1158/0008-5472.can-14-3242 · Cited by 37 · see citing works
A computational framework for complex disease stratification from multiple large-scale datasets
Meulder, B. D., Lefaudeux, D., Bansal, A. T., Mazein, A., Chaiboonchoe, A., Ahmed, H., Balaur, I., Saqi, M. A. S., Pellet, J., Ballereau, S. J., et al. (2018) · BMC Systems Biology · doi:10.1186/s12918-018-0556-z · Cited by 37 · see citing works
Labory, J., Fierville, M., Ait-El-Mkadem, S., Bannwarth, S., Paquis‐Flucklinger, V., & Bottini, S. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.590842 · Cited by 38 · see citing works
Patient-centric knowledge graphs: a survey of current methods, challenges, and applications
Khatib, H. S. A., Neupane, S., Manchukonda, H. K., Golilarz, N. A., Mittal, S., Amirlatifi, A., & Rahimi, S. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1388479 · Cited by 36 · see citing works
Integrative cancer patient stratification via subspace merging
Ding, H., Sharpnack, M. F., Wang, C., Huang, K., & Machiraju, R. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty866 · Cited by 34 · see citing works
Vlachavas, E., Bohn, J. R., Ückert, F., & Nürnberg, S. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22062822 · Cited by 36 · see citing works
ICM: a web server for integrated clustering of multi-dimensional biomedical data
He, S., He, H., Xu, W., Huang, X., Jiang, S., Li, F., He, F., & Bo, X. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw378 · Cited by 32 · see citing works
On fusion methods for knowledge discovery from multi-omics datasets
Baldwin, E., Han, J., Luo, W., Zhou, J. J., An, L., Liu, J., Zhang, H. H., & Li, H. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.02.011 · Cited by 32 · see citing works
Wu, M.-J., Gao, Y.-L., Liu, J., Zheng, C.-H., & Wang, J. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2948456 · Cited by 31 · see citing works
Simultaneous discovery of cancer subtypes and subtype features by molecular data integration
Van, T. L., van Leeuwen, M., Fierro, A. C., Maeyer, D. D., den Eynden, J. V., Verbeke, L. P. C., Raedt, L. D., Kathleen Marchal, D., & Nijssen, S. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw434 · Cited by 29 · see citing works
Ahmad, A., & Fröhlich, H. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx464 · Cited by 29 · see citing works
Integrative clustering methods for multi‐omics data
Zhang, X., Zhou, Z., Xu, H., & Liu, C. (2021) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1553 · Cited by 28 · see citing works
Multi-Omics Integration in Nephrology: Advances, Challenges, and Future Directions
Saliba, A., Du, Y., Feng, T., & Garmire, L. X. (2024) · Seminars in Nephrology · doi:10.1016/j.semnephrol.2025.151584 · Cited by 30 · see citing works
Translational biomarkers in the era of precision medicine
Bravo-Merodio, L., Acharjee, A., Russ, D., Bisht, V., Williams, J. A., Tsaprouni, L., & Gkoutos, G. V. (2020) · Advances in clinical chemistry · doi:10.1016/bs.acc.2020.08.002 · Cited by 28 · see citing works
DeFusion: a denoised network regularization framework for multi-omics integration
Wang, W., Zhang, X., & Dai, D. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab057 · Cited by 27 · see citing works
MONET: Multi-omic module discovery by omic selection
Rappoport, N., Safra, R., & Shamir, R. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008182 · Cited by 26 · see citing works
“PSDF extended this framework to support feature selection [16].”
BackgroundKondylakis, H., Axenie, C., Bastola, D. K., Katehakis, D. G., Kouroubali, A., Kurz, D., Larburu, N., Macía, I., Maguire, R., Maramis, C., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/22034 · Cited by 23 · see citing works
Predicting chemoinsensitivity in breast cancer with ’omics/digital pathology data fusion
Savage, R. S., & Yuan, Y. (2016) · Royal Society Open Science · doi:10.1098/rsos.140501 · Cited by 21 · see citing works
Self-citationToward Integrative Bayesian Analysis in Molecular Biology
Ickstadt, K., Schäfer, M. K.-H., & Zucknick, M. (2017) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-031017-100438 · Cited by 19 · see citing works
Bismeijer, T., Canisius, S., & Wessels, L. F. A. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006520 · Cited by 19 · see citing works
Selective Matrix Factorization for Multi-relational Data Fusion
Wang, Y., Yu, G., Domeniconi, C., Wang, J., Zhang, X., & Guo, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-18576-3_19 · Cited by 17 · see citing works
Clustering of cancer data based on Stiefel manifold for multiple views
Tian, J., Zhao, J., & Zheng, C.-H. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04195-4 · Cited by 13 · see citing works
INTEGRATIVE MACHINE LEARNING APPROACHES FOR MULTI-OMICS DATA ANALYSIS IN CANCER RESEARCH
Shoaib, A. S. M., Nishat, N., Raasetti, M., & Arif, I. (2024) · GLOBAL MAINSTREAM JOURNAL · doi:10.62304/ijhm.v1i2.149 · Cited by 11 · see citing works
SMRT: Randomized Data Transformation for Cancer Subtyping and Big Data Analysis
Nguyen, H., Tran, D., Tran, B., Roy, M., Cassell, A., Dascalu, S. M., Drăghici, S., & Nguyen, T. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.725133 · Cited by 11 · see citing works
CEPICS: A Comparison and Evaluation Platform for Integration Methods in Cancer Subtyping
Duan, R., Gao, L., Xu, H., Song, K., Hu, Y., Wang, H., Dong, Y., Zhang, C., & Jia, S. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00966 · Cited by 8 · see citing works
Koh, H. W. L., Fermin, D., Choi, K. P., Ewing, R. M., & Choi, H. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/374520 · Cited by 8 · see citing works
A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways
Niranjan, V., Uttarkar, A., Kaul, A., & Varghese, M. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2617-7_19 · Cited by 6 · see citing works
MDI-GPU: accelerating integrative modelling for genomic-scale data using GP-GPU computing
Mason, S. A., Sayyid, F., Kirk, P., Starr, C., & Wild, D. L. (2016) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2015-0055 · Cited by 6 · see citing works
A graph theoretical approach to data fusion
Žurauskienė, J., Kirk, P., & Stumpf, M. P. H. (2016) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2016-0016 · Cited by 5 · see citing works
A Comprehensive Protocol and Step-by-Step Guide for Multi-Omics Integration in Biological Research
Adam, E., Zanoaga, M. D., Rota, R., & Cominetti, O. (2025) · Journal of Visualized Experiments · doi:10.3791/66995 · Cited by 4 · see citing works
Multi-Omics Data Mining Techniques: Algorithms and Software
Tang, M., Liu, Y., & Gong, X. (2023) · Translational bioinformatics · doi:10.1007/978-981-19-8210-1_4 · Cited by 4 · see citing works
MoNETA: MultiOmics Network Embedding for SubType Analysis
Scala, G., Ferraro, L., Brandi, A., Guo, Y. P., Majello, B., & Ceccarelli, M. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae141 · Cited by 4 · see citing works
A Drug Repurposing Pipeline Based on Bladder Cancer Integrated Proteotranscriptomics Signatures
Mokou, M., Narayanasamy, S., Stroggilos, R., Balaur, I., Vlahou, A., Mischak, H., & Frantzi, M. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3291-8_4 · Cited by 4 · see citing works
Dall’Olio, D., Sträng, E., Turki, A. T., Tettero, J. M., Barbus, M., Schulze‐Rath, R., Elicegui, J. M. M., Matteuzzi, T., Merlotti, A., Carota, L., et al. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011299 · Cited by 4 · see citing works
Survival regression by data fusion
Žitnik, M., & Zupan, B. (2014) · Systems Biomedicine · doi:10.1080/21628130.2015.1016702 · Cited by 3 · see citing works
Disease Dynamics: Insights from Microbiome and Multi-Omics Analysis
Mishra, T., Tiwari, P. B., Rezaei, A. R., Mallik, B., Kanchan, S., & Kesheri, M. (2025) · Springer eBooks · doi:10.1007/978-981-96-7067-3_3 · Cited by 2 · see citing works
Approaches to uncovering cancer diagnostic and prognostic molecular signatures
Hong, S., Huang, Y., Cao, Y., Chen, X., & Han, J. J. (2014) · Molecular & Cellular Oncology · doi:10.4161/23723548.2014.957981 · Cited by 2 · see citing works
Choudhuri, S., Mallik, S., Ghosh, B., Si, T., Bhadra, T., Maulik, U., & Li, A. (2022) · Wiley eBooks · doi:10.1002/9781119743187.ch5 · Cited by 2 · see citing works
Neher, A., Stamenos, A., Fiecas, M. J. A., Safo, S. E., & Chekouo, T. (2025) · Data Science in Science · doi:10.1080/26941899.2025.2600125 · Cited by 1 · see citing works
Multiple Omics Data Integration
Qin, G., Liu, Z., & Xie, L. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.11508-9 · Cited by 1 · see citing works
"Improving Diagnosis of Alzheimer’s Disease by Data Fusion"
Zhang, Z. (2023) · Biomedical Journal of Scientific & Technical Research · doi:10.26717/bjstr.2023.49.007866 · Cited by 1 · see citing works
Biomedical data integration in cancer genomics
Louhimo, R. (2015) · Työväentutkimus Vuosikirja
paraSNF: An Parallel Approach for Large-Scale Similarity Network Fusion
Shen, X., He, S., Fang, M., Wen, Y., Bo, X., & Dou, Y. (2018) · Communications in computer and information science · doi:10.1007/978-981-13-2423-9_12
Sandia National Laboratories (SNL-CA), L., Chen, M., Administration, U. A. S. F. H. R. A., Sandia National Laboratories (SNL-NM), A., Darling, M., & Stracuzzi, D. J. (2018) · doi:10.2172/1475256
A data-fusion approach to identifying developmental dyslexia from multi-omics datasetsPreprint
Carrion, J., Nandakumar, R., Shi, X., Gu, H., Kim, Y., Raskind, W. H., Peter, B., & Dinu, V. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.27.530280
Integrating omics atlas in health informatics system design-an opinion article
Zeng, I. S. L. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1374359 · Cited by 1 · see citing works
Multi-omics and drug development
Hamid, S., Bhat, A. A., Bhat, M. R., Assad, A., & Macha, M. A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-13595-8.00007-6
Bayesian learning of clinically meaningful sepsis phenotypes in northern Tanzania
Dombowsky, A., Dunson, D. B., Madut, D. B., Rubach, M. P., & Herring, A. H. (2025) · The Annals of Applied Statistics · doi:10.1214/25-aoas2045
Lemsara, A., Ouadfel, S., & Fröhlich, H. (2020) · Figshare · doi:10.6084/m9.figshare.12144297
Lemsara, A., Ouadfel, S., & Fröhlich, H. (2020) · Figshare · doi:10.6084/m9.figshare.12144303
Artificial intelligence and brain–computer interface in cardiology and neurology—Introduction
Victor, S. P., Paul, W., & Sharma, C. P. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40441-2.00004-4
Immunotherapeutics in Combination with Chemotherapy in Clinical Trials
Shaik, R., Malik, M. S., Al‐Rooqi, M. M., Azeeza, S., Mallikarjun, B., Mounika, V., & Ahmed, S. A. (2026) · Reviews on Recent Clinical Trials · doi:10.2174/0115748871418784251208113309
Prevalence of disability-free survival at 5 years after surgery: a prospective observational study
Naito, Y., Ida, M., Ogata, S., Yabuno, Y., Inoue, S., & Kawaguchi, M. (2026) · Journal of Anesthesia · doi:10.1007/s00540-026-03758-0
- Authors
- Yuan, Y., Savage, R. S., & Markowetz, F.
- Affiliations
- University of Cambridge (GB)
- University of Warwick (GB)
- Corresponding author
- Yuan, Yinyin
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-10-20
- Volume
- 7
- Issue
- 10
- Pages
- e1002227
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC3197649
- OpenAlex
- W2103923312
- Cited by
- 97
- References
- 50
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 9 · 2023: 6 · 2022: 5 · 2021: 11 · 2020: 12 · 2019: 9 · 2018: 12 · 2017: 3 · 2016: 7 · 2015: 6 · 2014: 5 · 2013: 3 · 2012: 1
- FWCI
- 1.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Cancer Research UK
- University of Cambridge
- Hutchison Whampoa Limited
- Medical Research Council
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- data fusion, gene expression, copy number variation, nonparametric Bayesian models, hierarchical Dirichlet process, patient-specific analysis, feature selection, prognostic subtypes, prostate cancer, breast cancer, tumor heterogeneity
- MeSH terms
- Bayes Theorem, Breast Neoplasms, Female, Humans, Male, Models, Biological, Prognosis, Prostatic Neoplasms, Models, Statistical, Signal Transduction, Survival Analysis, Statistics, Nonparametric, Gene Expression Profiling, DNA Copy Number Variations
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review