Pattern discovery and cancer gene identification in integrated cancer genomic data

Regression shrinkage and selection via the lasso

Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works

Regularization Paths for Generalized Linear Models via Coordinate Descent

Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927 · see citing works

Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn923 · Cited by 14,108 · see citing works

Comprehensive molecular portraits of human breast tumours

The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11412 · Cited by 12,129 · see citing works

Comprehensive molecular characterization of human colon and rectal cancer

The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11252 · Cited by 8,499 · see citing works

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950 · see citing works

Integrated genomic analyses of ovarian carcinoma

Perou, C. M. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/hvp3-wg08 · Cited by 7,927 · see citing works

Comprehensive genomic characterization defines human glioblastoma genes and core pathways

The Cancer Genome Atlas Research Network (2008) · Nature · doi:10.1038/nature07385 · Cited by 7,735 · see citing works

Atomic Decomposition by Basis Pursuit

Chen, S. S., Donoho, D. L., & Saunders, M. A. (1998) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827596304010 · Cited by 6,734 · see citing works

The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups

METABRIC Group, Curtis, C., Shah, S. P., Chin, S.-F., Turashvili, G., Rueda, O. M., Dunning, M. J., Speed, D., Lynch, A. G., Samarajiwa, S., et al. (2012) · Nature · doi:10.1038/nature10983 · Cited by 6,097 · see citing works

Comprehensive genomic characterization of squamous cell lung cancers

The Cancer Genome Atlas Research Network (2012) · Nature · doi:10.1038/nature11404 · Cited by 3,994 · see citing works

A census of human cancer genes

Futreal, P. A., Coin, L., Marshall, M., Down, T., Hubbard, T., Wooster, R., Rahman, N., & Stratton, M. R. (2004) · Nature Reviews Cancer · doi:10.1038/nrc1299 · Cited by 3,184 · see citing works

Systematic identification of genomic markers of drug sensitivity in cancer cells

Garnett, M. J., Edelman, E. J., Heidorn, S. J., Greenman, C. D., Dastur, A., Lau, K. W., Greninger, P., Thompson, I. R., Luo, X., Soares, J., et al. (2012) · Nature · doi:10.1038/nature11005 · Cited by 2,456 · see citing works

Absolute quantification of somatic DNA alterations in human cancer

Carter, S. L., Cibulskis, K., Helman, E., McKenna, A., Shen, H., Zack, T. I., Laird, P. W., Onofrio, R. C., Winckler, W., Weir, B. A., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2203 · Cited by 2,003 · see citing works

Integrative genomic analyses identify MITF as a lineage survival oncogene amplified in malignant melanoma

Garraway, L. A., Widlund, H. R., Rubin, M. A., Getz, G., Berger, A. J., Ramaswamy, S., Beroukhim, R., Milner, D. A., Granter, S. R., Du, J., et al. (2005) · Nature · doi:10.1038/nature03664 · Cited by 1,548 · see citing works

High-throughput oncogene mutation profiling in human cancer

Thomas, R. K., Baker, A. C., Debiasi, R. M., Winckler, W., LaFramboise, T., Lin, W. M., Wang, M., Feng, W., Zander, T., MacConaill, L. E., et al. (2007) · Nature Genetics · doi:10.1038/ng1975 · Cited by 978 · see citing works

Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis

Shen, R., Olshen, A. B., & Ladanyi, M. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp543 · Cited by 849 · see citing works

Maximum Likelihood Algorithms for Generalized Linear Mixed Models

McCulloch, C. E. (1997) · Journal of the American Statistical Association · doi:10.1080/01621459.1997.10473613 · Cited by 762 · see citing works

The nuclear deubiquitinase BAP1 is commonly inactivated by somatic mutations and 3p21.1 losses in malignant pleural mesothelioma

Bott, M., Brevet, M., Taylor, B. S., Shimizu, S., Ito, T., Wang, L., Creaney, J., Lake, R. A., Zakowski, M. F., Reva, B., et al. (2011) · Nature Genetics · doi:10.1038/ng.855 · Cited by 717 · see citing works

Mutual exclusivity analysis identifies oncogenic network modulesPreprint

Ciriello, G., Cerami, E. G., Sander, C., & Schultz, N. D. (2011) · Genome Research · doi:10.1101/gr.125567.111 · Cited by 676 · see citing works

Number-theoretic Methods in Statistics

Fang, K.-T., & Wang, Y.-X. (1994) · Springer eBooks · doi:10.1007/978-1-4899-3095-8 · Cited by 641 · see citing works

Number-Theoretic Methods in Statistics.

Ripley, B. D., Fang, K.-T., & Wang, Y. (1995) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2983421 · Cited by 621 · see citing works

Valid post-selection inference

Berk, R. A., Brown, L., Buja, A., Zhang, K., & Zhao, L. H. (2013) · The Annals of Statistics · doi:10.1214/12-aos1077 · Cited by 617 · see citing works

Genomic analysis of a key innovation in an experimental Escherichia coli population

Blount, Z. D., Barrick, J. E., Davidson, C. J., & Lenski, R. E. (2012) · Nature · doi:10.1038/nature11514 · Cited by 612 · see citing works

Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis

Shen, R., Olshen, A. B., & Ladanyi, M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp659 · Cited by 566 · see citing works

Joint and individual variation explained (JIVE) for integrated analysis of multiple data types

Lock, E. F., Hoadley, K. A., Marron, J. S., & Nobel, A. B. (2013) · The Annals of Applied Statistics · doi:10.1214/12-aoas597 · Cited by 554 · see citing works

De Novo Discovery of Mutated Driver Pathways in Cancer

Vandin, F., Upfal, E., & Raphael, B. J. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20036-6_44 · Cited by 434 · see citing works

The t(8;9)(p22;p24) Is a Recurrent Abnormality in Chronic and Acute Leukemia that Fuses PCM1 to JAK2

Reiter, A., Walz, C., Watmore, A. E., Schoch, C., Blau, I., Schlegelberger, B., Berger, U., Telford, N., Aruliah, S., Yin, J. A. L., et al. (2005) · Cancer Research · doi:10.1158/0008-5472.can-04-4263 · Cited by 269 · see citing works

Can one estimate the conditional distribution of post-model-selection estimators?

Leeb, H., & Pötscher, B. M. (2006) · The Annals of Statistics · doi:10.1214/009053606000000821 · Cited by 163 · see citing works

Deletion of XPC leads to lung tumors in mice and is associated with early events in human lung carcinogenesis

Hollander, M. C., Philburn, R. T., Patterson, A. D., Velasco-Miguel, S., Friedberg, E. C., Linnoila, R. I., & Fornace, A. J. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0503133102 · Cited by 135 · see citing works

Studies of the Human c- myb Gene and Its Product in Human Acute Leukemias

Slamon, D. J., Boone, T. C., Murdock, D. C., Keith, D. E., Press, M. F., Larson, R. A., & Souza, L. M. (1986) · Science · doi:10.1126/science.3014652 · Cited by 125 · see citing works

Patient-specific data fusion defines prognostic cancer subtypes

Yuan, Y., Savage, R. S., & Markowetz, F. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002227 · Cited by 97 · see citing works

Relationship Between the c- myb Locus and the 6q- Chromosomal Aberration in Leukemias and Lymphomas

Barletta, C., Pelicci, P. G., Kenyon, L. C., Smith, S. D., & Dalla‐Favera, R. (1987) · Science · doi:10.1126/science.3469751 · Cited by 97 · see citing works

Integrative analysis of genome-scale data by using pseudoinverse projection predicts novel correlation between DNA replication and RNA transcription

Alter, O., & Golub, G. H. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0406767101 · Cited by 78 · see citing works

p73-alpha is capable of inducing scotin and ER stress

Terrinoni, A., Ranalli, M., Cadot, B., Leta, A., Bagetta, G., Vousden, K. H., & Melino, G. (2004) · Oncogene · doi:10.1038/sj.onc.1207342 · Cited by 58 · see citing works

Number‐Theoretic Methods

Fang, K., & Wang, Y. (2014) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat02239 · Cited by 17 · see citing works

Comprehensive molecular profiling of lung adenocarcinoma

The Cancer Genome Atlas Research Network (2014) · Nature · doi:10.1038/nature13385 · Cited by 5,732 · see citing works

Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer

Hoadley, K. A., Yau, C., Hinoue, T., Wolf, D. M., Lazar, A. J., Drill, E., Shen, R., Taylor, A. M., Cherniack, A. D., Thorsson, V., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.022 · Cited by 2,546 · see citing works

Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma

Ally, A., Balasundaram, M., Carlsen, R., Chuah, E., Clarke, A., Dhalla, N., Holt, R. A., Jones, S. J. M., Lee, D., Ma, Y., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.046 · Cited by 2,490 · see citing works

Integrated genomic characterization of oesophageal carcinoma

Sun, Q., Kelsen, D. P., Wiznerowicz, M., Lu, Y., Marra, M. A., Wan, Y., Weinhold, N., Wong, A. M., Carvalho, A. L., Luketich, J. D., et al. (2017) · Nature · doi:10.1038/nature20805 · Cited by 1,833 · see citing works

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

Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets

Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J. C., Buettner, F., Huber, W., & Stegle, O. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178124 · Cited by 1,510 · see citing works

Genomic spectra of biliary tract cancer

Nakamura, H. K., Arai, Y., Totoki, Y., Shirota, T., Elzawahry, A., Kato, M., Hama, N., Hosoda, F., Urushidate, T., Ohashi, S., et al. (2015) · Nature Genetics · doi:10.1038/ng.3375 · Cited by 1,287 · see citing works

Intertumoral Heterogeneity within Medulloblastoma Subgroups

Cavalli, F. M. G., Remke, M., Rampasek, L., Peacock, J., Shih, D. J. H., Luu, B., Garzia, L., Torchia, J., Nor, C., Morrissy, A. S., et al. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.05.005 · Cited by 1,219 · see citing works

Comprehensive 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 · see citing works

Genomic and Transcriptomic Landscape of Triple-Negative Breast Cancers: Subtypes and Treatment Strategies

Jiang, Y., Ma, D., Suo, C., Shi, J., Xue, M., Hu, X., Xiao, Y., Yu, K., Liu, Y., Yu, Y., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.02.001 · Cited by 1,069 · see citing works

Whole-Genome and Epigenomic Landscapes of Etiologically Distinct Subtypes of Cholangiocarcinoma

Jusakul, A., Cutcutache, I., Yong, C. H., Lim, J. Q., Huang, M. N., Padmanabhan, N., Nellore, V., Kongpetch, S., Ng, A. W. T., Ng, L. M., et al. (2017) · Cancer Discovery · doi:10.1158/2159-8290.cd-17-0368 · Cited by 972 · 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

Integration 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 · see citing works

Targeting the STAT3 signaling pathway in cancer: Role of synthetic and natural inhibitors

Siveen, K. S., Sikka, S., Surana, R., Dai, X., Zhang, J., Kumar, A. P., Tan, B. K. H., Sethi, G., & Bishayee, A. (2014) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2013.12.005 · Cited by 637 · 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

The 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 · see citing works

MOLI: multi-omics late integration with deep neural networks for drug response prediction

Sharifi-Noghabi, H., Zolotareva, O., Collins, C. C., & Ester, M. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz318 · Cited by 421 · see citing works

Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis

Menyhárt, O., & Győrffy, B. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.01.009 · Cited by 388 · see citing works

Machine 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 · see citing works

A 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 343 · see citing works

Machine 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 341 · see citing works

A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data

Yang, Z., & Michailidis, G. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btv544 · Cited by 295 · see citing works

Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study

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 312 · see citing works

Bayesian consensus clustering

Lock, E. F., & Dunson, D. B. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt425 · Cited by 308 · see citing works

Proteogenomic 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 · see citing works

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 292 · 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

NEMO: cancer subtyping by integration of partial multi-omic data

Rappoport, N., & Shamir, R. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz058 · Cited by 286 · see citing works

A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data

Mo, Q., Shen, R., Guo, C., Vannucci, M., Chan, K. S., & Hilsenbeck, S. G. (2017) · Biostatistics · doi:10.1093/biostatistics/kxx017 · Cited by 281 · see citing works

Integrative clustering of multi-level ‘omic data based on non-negative matrix factorization algorithm

Chalise, P., & Fridley, B. L. (2017) · PLoS ONE · doi:10.1371/journal.pone.0176278 · Cited by 233 · see citing works

Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification

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

A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches

Baião, A. R., Cai, Z., Poulos, R. C., Robinson, P. J., Reddel, R. R., Zhong, Q., Vinga, S., & Gonçalves, E. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf355 · Cited by 249 · see citing works

Integrative and comparative genomic analyses identify clinically relevant pulmonary carcinoid groups and unveil the supra-carcinoids

Alcala, N., Leblay, N., Gabriel, A., Mangiante, L., Hervás, D., Giffon, T., Sertier, A.-S., Ferrari, A., Derks, J. L., Ghantous, A., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11276-9 · Cited by 222 · see citing works

Integrative 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

A Selective Review of Multi-Level Omics Data Integration Using Variable Selection

Wu, C., Zhou, F., Ren, J., Li, X., Jiang, Y., & Ma, S. (2019) · Microarrays · doi:10.3390/ht8010004 · Cited by 213 · see citing works

Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival

Ramazzotti, D., Lal, A., Wang, B., Batzoglou, S., & Sidow, A. (2018) · Nature Communications · doi:10.1038/s41467-018-06921-8 · Cited by 211 · see citing works

NADPH Oxidases: A Perspective on Reactive Oxygen Species Production in Tumor Biology

Meitzler, J. L., Antony, S., Wu, Y., Juhasz, A. N., Liu, H., Jiang, G., Lu, J., Roy, K., & Doroshow, J. H. (2013) · Antioxidants and Redox Signaling · doi:10.1089/ars.2013.5603 · Cited by 205 · see citing works

A 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 · see citing works

A novel approach for data integration and disease subtypingPreprint

Nguyen, T., Tagett, R., Diaz, D., & Drăghici, S. (2017) · Genome Research · doi:10.1101/gr.215129.116 · Cited by 178 · 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

Subtype-GAN: a deep learning approach for integrative cancer subtyping of multi-omics data

Yang, H., Chen, R., Li, D., & Wang, Z. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab109 · Cited by 157 · see citing works

Methods, Tools and Current Perspectives in Proteogenomics

Ruggles, K. V., Krug, K., Wang, X., Clauser, K. R., Wang, J., Payne, S., Fenyö, D., Zhang, B., & Mani, D. R. (2017) · Molecular & Cellular Proteomics · doi:10.1074/mcp.mr117.000024 · Cited by 148 · see citing works

Multi-omics integration—a comparison of unsupervised clustering methodologies

Tini, G., Marchetti, L., Priami, C., & Scott‐Boyer, M. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx167 · Cited by 147 · see citing works

Multi-omics analysis identifies osteosarcoma subtypes with distinct prognosis indicating stratified treatment

Jiang, Y., Wang, J., Sun, M., Zuo, D., Wang, H., Shen, J., Jiang, W., Mu, H., Ma, X., Yin, F., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34689-5 · Cited by 148 · see citing works

Multiplatform-based molecular subtypes of non-small-cell lung cancer

Chen, F., Zhang, Y., Parra, E. R., Rodriguez‐Canales, J., Behrens, C., Akbani, R., Lu, Y., Kurie, J. M., Gibbons, D. L., Mills, G. B., et al. (2016) · Oncogene · doi:10.1038/onc.2016.303 · Cited by 142 · see citing works

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 141 · see citing works

PINSPlus: a tool for tumor subtype discovery in integrated genomic data

Nguyen, H., Shrestha, S., Drăghici, S., & Nguyen, T. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty1049 · Cited by 140 · 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

Recent human adaptation: genomic approaches, interpretation and insights

Scheinfeldt, L., & Tishkoff, S. A. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3604 · Cited by 113 · see citing works

Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method

Yu, Y., Zhang, N., Mai, Y., Ren, L., Chen, Q., Cao, Z., Chen, Q., Liu, Y., Hou, W., Yang, J., et al. (2023) · Genome biology · doi:10.1186/s13059-023-03047-z · Cited by 116 · 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

Integrating Clinical and Multiple Omics Data for Prognostic Assessment across Human Cancers

Zhu, B., Song, N., Shen, R., Arora, A., Machiela, M. J., Song, L., Landi, M. T., Ghosh, D., Chatterjee, N., Baladandayuthapani, V., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-17031-8 · Cited by 109 · see citing works

Multi-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 124 · see citing works

Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges

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

Systematic analysis of somatic mutations impacting gene expression in 12 tumour types

Ding, J., McConechy, M. K., Horlings, H. M., Ha, G., Chan, F. C., Funnell, T., Mullaly, S. C., Reimand, J., Bashashati, A., Bader, G. D., et al. (2015) · Nature Communications · doi:10.1038/ncomms9554 · Cited by 104 · see citing works

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

O’Connor, L. M., O’Connor, B. A., Lim, S. B., Zeng, J., & Lo, C. H. (2023) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2023.06.011 · Cited by 107 · 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

Molecular Subtypes of Colorectal Cancer and Their Clinicopathologic Features, With an Emphasis on the Serrated Neoplasia Pathway

Bae, J. M., Kim, J. H., & Kang, G. H. (2016) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2015-0310-ra · Cited by 100 · see citing works

Evaluation of colorectal cancer subtypes and cell lines using deep learning

Ronen, J., Hayat, S., & Akalin, A. (2019) · Life Science Alliance · doi:10.26508/lsa.201900517 · Cited by 101 · see citing works

Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration

Pierre‐Jean, M., Deleuze, J., Floch, É. L., & Mauger, F. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz138 · Cited by 100 · 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

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 · see citing works

Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology

Raufaste-Cazavieille, V., Santiago, R., & Droit, A. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.962743 · Cited by 98 · see citing works

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 90 · see citing works

Molecular heterogeneity of non-small cell lung carcinoma patient-derived xenografts closely reflect their primary tumors

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 · 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

Comprehensive molecular profiling of lung adenocarcinoma

Lander, E. S. (2014) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 84 · see citing works

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 · see citing works

Integrative 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 · see citing works

Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer

Matsuoka, T., & Yashiro, M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25115880 · Cited by 83 · see citing works

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 76 · see citing works

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 63 · see citing works

Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data

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 · see citing works

A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping

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 68 · see citing works

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 72 · see citing works

Integrated molecular characterization reveals potential therapeutic strategies for pulmonary sarcomatoid carcinoma

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 · see citing works

Multi‐omics consensus ensemble refines the classification of muscle‐invasive bladder cancer with stratified prognosis, tumour microenvironment and distinct sensitivity to frontline therapies

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 66 · see citing works

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 · see citing works

A label-free electrochemical biosensor for highly sensitive and selective detection of DNA via a dual-amplified strategy

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 · see citing works

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 · see citing works

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 62 · see citing works

The 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 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

Simultaneous Interrogation of Cancer Omics to Identify Subtypes With Significant Clinical Differences

Xu, A., Chen, J., Peng, H., Han, G., & Cai, H. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00236 · Cited by 59 · 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 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 60 · 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

Subtype-DCC: decoupled contrastive clustering method for cancer subtype identification based on multi-omics data

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 54 · see citing works

Identification of diagnostic markers in colorectal cancer via integrative epigenomics and genomics data

Sin, 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 · see citing works

Approximate 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 52 · see citing works

Understanding the molecular drivers of disease heterogeneity in Crohn's disease using multi-omic data integration and network analysis

Sudhakar, 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 · see citing works

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 · see citing works

Omics 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 52 · see citing works

Results 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 52 · see citing works

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 50 · see citing works

Tensor GSVD of Patient- and Platform-Matched Tumor and Normal DNA Copy-Number Profiles Uncovers Chromosome Arm-Wide Patterns of Tumor-Exclusive Platform-Consistent Alterations Encoding for Cell Transformation and Predicting Ovarian Cancer Survival

Preethi, S., Schomay, T. E., Aiello, K. A., & Alter, O. (2015) · PLoS ONE · doi:10.1371/journal.pone.0121396 · Cited by 46 · see citing works

Multi-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 48 · see citing works

The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes

Lu, X., Xing, L., Liu, P., Qian, X., Miao, Q., & Peng, S. (2018) · Molecules · doi:10.3390/molecules23020183 · Cited by 30 · 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

Integrated Cancer Subtyping using Heterogeneous Genome-Scale Molecular Datasets

Arslanturk, S., Drăghici, S., & Nguyen, T. (2019) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811215636_0049 · Cited by 12 · see citing works

Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis

Shen, R., Olshen, A. B., & Ladanyi, M. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp543 · Cited by 849 · see citing works

Pan-cancer subtyping in a 2D-map shows substructures that are driven by specific combinations of molecular characteristics

Taskesen, E., Huisman, S. M. H., Mahfouz, A. E., Krijthe, J. H., de Ridder, J., van de Stolpe, A., van den Akker, E. B., Verheagh, W., & Reinders, M. J. T. (2016) · Scientific Reports · doi:10.1038/srep24949 · Cited by 23 · see citing works