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Pattern discovery and cancer gene identification in integrated cancer genomic data
Open accessAbstract
Large-scale integrated cancer genome characterization efforts including the cancer genome atlas and the cancer cell line encyclopedia have created unprecedented opportunities to study cancer biology in the context of knowing the entire catalog of genetic alterations. A clinically important challenge is to discover cancer subtypes and their molecular drivers in a comprehensive genetic context. Curtis et al. [ Nature (2012) 486(7403):346–352] has recently shown that integrative clustering of copy number and gene expression in 2,000 breast tumors reveals novel subgroups beyond the classic expression subtypes that show distinct clinical outcomes. To extend the scope of integrative analysis for the inclusion of somatic mutation data by massively parallel sequencing, we propose a framework for joint modeling of discrete and continuous variables that arise from integrated genomic, epigenomic, and transcriptomic profiling. The core idea is motivated by the hypothesis that diverse molecular phenotypes can be predicted by a set of orthogonal latent variables that represent distinct molecular drivers, and thus can reveal tumor subgroups of biological and clinical importance. Using the cancer cell line encyclopedia dataset, we demonstrate our method can accurately group cell lines by their cell-of-origin for several cancer types, and precisely pinpoint their known and potential cancer driver genes. Our integrative analysis also demonstrates the power for revealing subgroups that are not lineage-dependent, but consist of different cancer types driven by a common genetic alteration. Application of the cancer genome atlas colorectal cancer data reveals distinct integrated tumor subtypes, suggesting different genetic pathways in colon cancer progression.
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“Along with the introduction of high-throughput technologies and the availability of multi-omics data derived from a number of samples, a large set of multi-omics data integrative tools and methods have been reported for the discovery of biomarkers.”
From Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer (2024) · cites this paper as [98]
In the citing paper
“These methods can be divided into categories, such as Bayesian integration (e.g., 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 (MOGSA) [103], Cytoscape”
From Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer (2024) · cites this paper as [98]
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InfluentialComprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas
Abeshouse, A., Adebamowo, C., Adebamowo, S. N., Akbani, R., Akeredolu, T., Ally, A., Anderson, M. L., Anur, P., Appelbaum, E. L., Armenia, J., et al. (2017) · Cell · doi:10.1016/j.cell.2017.10.014 · Cited by 1,069
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“The “iClusterPlus” R package (76) was used to perform integrative unsupervised clustering of 94 CCAs based on 4 genomic data types: (i) somatic point mutations in 404 targeted genes (gene by sample matrix of binary values), (ii) sCNAs defined as copy-number segments identified by ASCAT v2.”
MethodUsing machine learning approaches for multi-omics data analysis: A review
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More Is Better: Recent Progress in Multi-Omics Data Integration Methods
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“H is the loading factor (coefficient), the imposed sparsity with different types of penalty functions for various data types. iCluster uses E to represent the error/noise term, and the underlying decomposition equation is: (Mo et al., 2013).”
BackgroundIntegration strategies of multi-omics data for machine learning analysis
Picard, M., Scott-Boyer, M.-P., Bodein, A., Périn, O., & Droit, A. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.06.030 · Cited by 696
Targeting the STAT3 signaling pathway in cancer: Role of synthetic and natural inhibitors
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Multi-omic and multi-view clustering algorithms: review and cancer benchmark
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“An extension called iCluster+ (64) includes different models for numeric, categorical and count data, but maintains the idea that data originate from a low dimension matrix Z.”
BackgroundThe IBD interactome: an integrated view of aetiology, pathogenesis and therapy
de Souza, H. S. P., Fiocchi, C., & Iliopoulos, D. (2017) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/nrgastro.2017.110 · Cited by 447
MOLI: multi-omics late integration with deep neural networks for drug response prediction
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“Multi-omics data have been exploited for different problems such as driver gene identification (Dimitrakopoulos et al., 2018; Mo et al., 2013; Shrestha et al., 2017; Singh et al., 2019), patient stratification (Khakabimamaghani and Ester, 2016), survival prediction (Chaudhary et al.”
MethodMenyhárt, O., & Győrffy, B. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.01.009 · Cited by 406
“iCluster+ can identify genomic features that contribute most to the biological variation with a lasso regression [20].”
BackgroundMethodMachine learning and integrative analysis of biomedical big data
Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P. (2019) · Genes · doi:10.3390/genes10020087 · Cited by 380
“Recently, iCluster was upgraded to iCluster+ to incorporate diverse data modalities including, binary, categorical and continuous values such that somatic mutation, CNA and gene expression were integrated and distinct tumor sub-groups were identified [75].”
MethodInfluentialA genomic and epigenomic atlas of prostate cancer in Asian populations
Li, J., Xu, C., Lee, H. J., Ren, S., Zi, X., Zhang, Z., Wang, H., Yu, Y., Yang, C., Gao, X., et al. (2020) · Nature · doi:10.1038/s41586-020-2135-x · Cited by 353
Self-citationMachine learning for multi-omics data integration in cancer
Cai, Z., Poulos, R. C., Liu, J., & Zhong, Q. (2022) · iScience · doi:10.1016/j.isci.2022.103798 · Cited by 366
“Based on iCluster, iClusterPlus (Mo et al., 2013) focuses on modeling different statistical distributions for discrete data types. iClusterBayes (Mo et al., 2018) is the latest version in the series and implements a fully Bayesian inference algorithm, which runs six times faster than iClusterPlus.”
MethodYang, Z., & Michailidis, G. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btv544 · Cited by 295
Ross‐Adams, H., Lamb, A. D. G., Dunning, M., Halim, S., Lindberg, J., Massie, C. E., Egevad, L., Russell, R. B., Ramos‐Montoya, A., Vowler, S. L., et al. (2015) · EBioMedicine · doi:10.1016/j.ebiom.2015.07.017 · Cited by 319
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“For example, Nguyen and Gelfand (2011) exploit functional covariance models for timecourse data to determine overall and time-specific clusters.”
BackgroundProteogenomic Characterization of Human Early-Onset Gastric Cancer
Mun, D.-G., Bhin, J., Kim, S., Kim, H., Jung, J. H., Jung, Y., Jang, Y. E., Park, J. M., Kim, H., Jung, Y., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2018.12.003 · Cited by 294
Statistical and integrative system-level analysis of DNA methylation data
Teschendorff, A. E., & Relton, C. L. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.86 · Cited by 297
Integrative multi-omics approaches in cancer research: from biological networks to clinical subtypes
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“Since the runtime of iCluster increases with the number of features, iCluster+, providing full Bayesian regularization for clustering, has recently been proposed (Mo et al., 2013).”
BackgroundNEMO: cancer subtyping by integration of partial multi-omic data
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A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data
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“…variables are estimated using the expectation-maximisation method [76], and clusters are obtained by applying K-means to matrix F . iClusterPlus [77] extends iClus-ter by modelling different statistical distributions to handle discrete omics datasets but it was criticized for its computational…”
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“Very few studies integrate somatic mutation data with mRNA and methylation data, due to the difficulty in integrating binary with continuous data types [137].”
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NADPH Oxidases: A Perspective on Reactive Oxygen Species Production in Tumor Biology
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“In the context of cancer biology, where prohibitive cost may preclude many laboratories from pursuing in vivo studies, bioinformatics tools provide access to gene expression profiles and patient-specific tumor data that would otherwise be unavailable (49, 93, 119, 137).”
BackgroundA guide to multi-omics data collection and integration for translational medicine
Athieniti, E., & Spyrou, G. M. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.050 · Cited by 185
A novel approach for data integration and disease subtypingPreprint
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“, 2012), iClusterPlus (Mo et al., 2013), and iCluster (Shen et al.”
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Vahabi, N., & Michailidis, G. C. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.854752 · Cited by 171
Subtype-GAN: a deep learning approach for integrative cancer subtyping of multi-omics data
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Methods, Tools and Current Perspectives in Proteogenomics
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“amples in this category include iCluster (132), LRAcluster”
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Tini, G., Marchetti, L., Priami, C., & Scott‐Boyer, M. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx167 · Cited by 157
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Self-citationMultiplatform-based molecular subtypes of non-small-cell lung cancer
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moCluster: Identifying Joint Patterns Across Multiple Omics Data Sets
Meng, C., Helm, D., Frejno, M., & Küster, B. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00824 · Cited by 147
PINSPlus: a tool for tumor subtype discovery in integrated genomic data
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Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology
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Evaluation and comparison of multi-omics data integration methods for cancer subtyping
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“For the mRNA expression and the DNA methylation, we adopted the median absolute deviation (MAD) to select the top 2,000 most-variable features as iClusterPlus [43] did.”
MethodRecent human adaptation: genomic approaches, interpretation and insights
Scheinfeldt, L., & Tishkoff, S. A. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3604 · Cited by 118
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“Three integrative tools were used, including iClusterBayes from iClusterPlus 1.30.0 package [61], intNMF from IntNMF 1.2.0 package [49], and SNF from SNFtool 2.3.1 package [48].”
MethodInfluentialiOmicsPASS: 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 128
Integrating Clinical and Multiple Omics Data for Prognostic Assessment across Human Cancers
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Self-citationMulti-omics strategies for biomarker discovery and application in personalized oncology
Jiang, Z., Zhang, H., Gao, Y., & Sun, Y. (2025) · Molecular Biomedicine · doi:10.1186/s43556-025-00340-0 · Cited by 143
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Systematic analysis of somatic mutations impacting gene expression in 12 tumour types
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“lized to facilitate unsupervised integration such as iCluster programs [222,223], JIVE [224], CNAmet [225], and PARADIGM [226].”
MethodStatistical 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 101
“While iCluster was originally formulated for clustering continuous data types, iCluster+ (Mo et al. 2013) extends the framework to cope with both discrete and continuous data, replacing the linear formulation in (1) by a generalized linear one.”
MethodBae, J. M., Kim, J. H., & Kang, G. H. (2016) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2015-0310-ra · Cited by 100
“002 Distant metastasis 81 (18) 25 (14) 3 (7) 11 (36) 2 (11) .”
Evaluation of colorectal cancer subtypes and cell lines using deep learning
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“the same using MOFA (24) and iCluster+ (14), other published methods for multiomics integration by dimensionality reduction.”
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“In the second article [26], the authors compared iCluster [38], MoCluster [39], JIVE [56], multiple dataset integration [58], integrative NMF from [59] and Bayesian consensus clustering [60].”
BackgroundMethodAdvances 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 111
Angle-based joint and individual variation explained
Feng, Q., Jiang, M., Hannig, J., & Marron, J. S. (2018) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2018.03.008 · Cited by 93
“[24] performed integrative clustering on multiple sources to reveal novel and consistent cancer subtypes based on understanding of joint and individual variation.”
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Pattern fusion analysis by adaptive alignment of multiple heterogeneous omics data
Shi, Q., Zhang, C., Peng, M., Yu, X., Zeng, T., Liu, J., & Chen, L. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx176 · Cited by 92
Wang, D. Y. Q., Pham, N., Tong, J., Sakashita, S., Allo, G., Kim, L., Yanagawa, N., Raghavan, V., Wei, Y., To, C., et al. (2016) · International Journal of Cancer · doi:10.1002/ijc.30472 · Cited by 89
“Mutations (somatic, wildtype) and copy number alterations (gain, loss, diploid) for each pathway were used as input features for iClusterPlus.(38) The model that maximized Bayesian information criterion was selected for each pathway and the proportion of variation explained was reported.”
MethodMachine 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 91
Comprehensive molecular profiling of lung adenocarcinoma
Lander, E. S. (2014) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 84
A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression
Liu, Y., Gu, Q., Hou, J. P., Han, J., & Ma, J. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-37 · Cited by 84
“In [13,14], an integrated approach was developed to”
MethodIntegrative clustering methods of multi‐omics data for molecule‐based cancer classifications
Wang, D., & Gu, J. (2016) · Quantitative Biology · doi:10.1007/s40484-016-0063-4 · Cited by 81
“iCluster+ Latent factor analysis [13]”
BackgroundMethodInfluentialMatsuoka, T., & Yashiro, M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25115880 · Cited by 83
“…such as Bayesian integration (e.g., 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],…”
African-specific molecular taxonomy of prostate cancer
Jaratlerdsiri, W., Jiang, J., Gong, T., Patrick, S. M., Willet, C. E., Chew, T., Lyons, R. J., Haynes, A., Pasqualim, G., Louw, M., et al. (2022) · Nature · doi:10.1038/s41586-022-05154-6 · Cited by 75
Integration of Online Omics-Data Resources for Cancer Research
Das, T., Andrieux, G., Ahmed, M., & Chakraborty, S. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.578345 · Cited by 77
EL‐Manzalawy, Y., Hsieh, T.-Y., Shivakumar, M., Kim, D., & Honavar, V. (2018) · BMC Medical Genomics · doi:10.1186/s12920-018-0388-0 · Cited by 70
Sathyanarayanan, A., Gupta, R., Thompson, E. W., Nyholt, D. R., Bauer, D. C., & Nagaraj, S. H. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz121 · Cited by 70
Comprehensive analysis of oncogenic determinants across tumor types via multi-omics integration
Ubaid, S., Kushwaha, R., Kashif, M., & Singh, V. K. (2025) · Cancer Genetics · doi:10.1016/j.cancergen.2025.08.010 · Cited by 74
Yang, Z., Xu, J., Li, L., Li, R., Wang, Y., Tian, Y., Guo, W., Wang, Z., Tan, F., Ying, J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18702-3 · Cited by 68
Lu, X., Meng, J., Su, L., Jiang, L., Wang, H., Zhu, J., Huang, M., Cheng, W., Xu, L., Ruan, X., et al. (2021) · Clinical and Translational Medicine · doi:10.1002/ctm2.601 · Cited by 64
A phenotypic and genomics approach in a multi-ethnic cohort to subtype systemic lupus erythematosus
Lanata, C. M., Paranjpe, I., Nititham, J., Taylor, K. E., Gianfrancesco, M. A., Paranjpe, M. D., Andrews, S. V., Chung, S. A., Rhead, B. L., Barcellos, L. F., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11845-y · Cited by 65
Kong, R., Song, Z., Meng, H., Zhang, X., Shen, G., & Yu, R. (2013) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2013.11.041 · Cited by 64
Deep latent space fusion for adaptive representation of heterogeneous multi-omics data
Zhang, C., Chen, Y., Zeng, T., Zhang, C., & Chen, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab600 · Cited by 63
Systematic exploration of cell morphological phenotypes associated with a transcriptomic query
Nassiri, I., & McCall, M. N. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky626 · Cited by 66
“The LASSO fits a sparse model and consequently focuses on the most significant transcriptomic features (20).”
BackgroundThe Discovery of Mutated Driver Pathways in Cancer: Models and Algorithms
Zhang, J., & Zhang, S. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2640963 · Cited by 62
“Several integrative methods to decipher coherent patterns have also been developed [79], [80], [81], [82], [83], [84], [85], which may help to reveal clinically relevant characteristics.”
MethodFrom 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 63
Xu, A., Chen, J., Peng, H., Han, G., & Cai, H. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00236 · Cited by 59
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 63
Machine learning analysis of TCGA cancer data
Liñares-Blanco, J., Pazos, A., & Fernandez-Lozano, C. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.584 · Cited by 63
“On other hand, an extended version (iClusterPlus) was also developed (Mo et al., 2013).”
BackgroundMachine 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
Zhao, J. Y., Zhao, B., Song, X., Lyu, C., Chen, W., Xiong, Y., & Wei, D. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad025 · Cited by 56
“Statistical methods (such as NMF [12], MCCA [13], iCluster [14], iCluster+ [15] and iClusterBayes [16]) model the distribution of each data type and then maximize the likelihood of multiomics data based on joint latent variables.”
MethodSin, T. K., Mokhtar, N. M., Hassan, N. Z. A., Sagap, I., Rose, I. M., Harun, R., & Jamal, R. A. (2015) · Oncology Reports · doi:10.3892/or.2015.3993 · Cited by 53
“To address this issue, epigenomic and genomic data from microarray analyses have been used to obtain a more comprehensive insight of the molecular mechanisms involved in CRC (17,23-25).”
MethodApproximate Graph Laplacians for Multimodal Data Clustering
Khan, A., & Maji, P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2945574 · Cited by 51
“On the other hand, some of the direct integrative approaches, like super k -means [11], iCluster [12], iCluster+ [13], LRAcluster [14], joint and individual variance explained (JIVE) [15], and angle-based JIVE (A-JIVE) [16], proceed by concatenating the individual modalities to get the integrated…”
BackgroundSudhakar, P., Verstockt, B., Cremer, J., Verstockt, S., Sabino, J., Ferrante, M., & Vermeire, S. (2021) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaa281 · Cited by 49
Multi-omics Data Integration Model Based on UMAP Embedding and Convolutional Neural Network
Elkarami, B., Alkhateeb, A., Qattous, H., Alshomali, L., & Shahrrava, B. (2022) · Cancer Informatics · doi:10.1177/11769351221124205 · Cited by 51
“The adjacency matrix of undirected weighted graph B can be computed as: B A A A A T T = + − ° (4)”
MethodOmics for the future in asthma
Abdel‐Aziz, M. I., Neerincx, A. H., Vijverberg, S. J. H., Kraneveld, A. D., & der Zee, A. H. M. (2020) · Seminars in Immunopathology · doi:10.1007/s00281-019-00776-x · Cited by 54
“On the other hand, unsupervised approaches, such as similarity network fusion [109], iClusterPlus [110], PINSPlus [111], and Spectrum [112], cluster the patients based on their similarity in the multi-omics profiles.”
MethodResults of the Seventh Scientific Workshop of ECCO: Precision Medicine in IBD—What, Why, and How
Fiocchi, C., Dragoni, G., Iliopoulos, D., Katsanos, K. H., Hernández, V., Suzuki, K., Verstockt, B., Fiocchi, C., Torres, J., & Scharl, M. M. (2021) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjab051 · Cited by 54
NSD1- and NSD2-damaging mutations define a subset of laryngeal tumors with favorable prognosis
Peri, S., Izumchenko, E., Schubert, A. D., Slifker, M. J., Ruth, K. J., Serebriiskii, I. G., Guo, T. W., Burtness, B. A., Mehra, R., Ross, E. A., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01877-7 · Cited by 53
Preethi, S., Schomay, T. E., Aiello, K. A., & Alter, O. (2015) · PLoS ONE · doi:10.1371/journal.pone.0121396 · Cited by 46
“Unlike recent approaches to the integrative modeling of different types of large-scale molecular biological profiles from the same set of patients, notably clustering [60, 61], our comparative modeling was not limited to tumor profiles, and included also patient- and platformmatched normal DNA copy-number profiles.”
BackgroundMulti-omics data integration approaches for precision oncology
Aguila, R. C., Alonso-Pupo, N., & Hernández‐Rodríguez, E. W. (2022) · Molecular Omics · doi:10.1039/d1mo00411e · Cited by 52
Metadata
- Authors
- Mo, Q., Wang, S., Seshan, V. E., Olshen, A. B., Schultz, N., Sander, C., Powers, R. S., Ladanyi, M., & Shen, R.
- Affiliations
- Baylor College of Medicine (US)
- University of Wisconsin–Madison (US)
- Department of Epidemiology and Biostatistics,
- University of California, San Francisco (US)
- Computational Biology Program, and
- Cancer Genome Center, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11797
- Memorial Sloan Kettering Cancer Center (US)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2013-02-21
- Volume
- 110
- Issue
- 11
- Pages
- 4245-4250
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3600490
- OpenAlex
- W2023461603
- Cited by
- 474
- References
- 36
- Citations by year
- 2026: 29 · 2025: 33 · 2024: 34 · 2023: 54 · 2022: 40 · 2021: 74 · 2020: 40 · 2019: 48 · 2018: 27 · 2017: 27 · 2016: 25 · 2015: 23 · 2014: 13 · 2013: 7
- FWCI
- 7.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Cancer Institute (U24CA143840, CA143840, U01CA168409, P30CA008748)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- tumor subtypes, cancer driver genes, integrative clustering, somatic mutations, copy number alterations, gene expression, Cancer Cell Line Encyclopedia, TCGA, colorectal cancer, breast cancer, cancer subtyping, cell of origin
- MeSH terms
- Breast Neoplasms, Female, Gene Expression Regulation, Humans, Male, Colorectal Neoplasms, Genomics, Databases, Genetic, Genes, Neoplasm, Epigenomics
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