Bayesian joint analysis of heterogeneous genomics data
Open accessAbstract
SUMMARY: A non-parametric Bayesian factor model is proposed for joint analysis of multi-platform genomics data. The approach is based on factorizing the latent space (feature space) into a shared component and a data-specific component with the dimensionality of these components (spaces) inferred via a beta-Bernoulli process. The proposed approach is demonstrated by jointly analyzing gene expression/copy number variations and gene expression/methylation data for ovarian cancer patients, showing that the proposed model can potentially uncover key drivers related to cancer. AVAILABILITY AND IMPLEMENTATION: The source code for this model is written in MATLAB and has been made publicly available at https://sites.google.com/site/jointgenomics/. CONTACT: catherine.ll.zheng@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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JBF used joint factor analysis to evaluate the feature space and converted it into shared and datatype-specific components.
“joint factor analysis to evaluate the feature space and converted it into shared and datatype-specific components.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Ray et al., 2014
JBF improved the sparsity problem in JIVE.
“The sparsity problem in JIVE was improved by JBF (Joint Bayes Factor) (Ray et al., 2014).”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Ray et al., 2014
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Cites
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033 · see citing works
Proceedings of the 25th international conference on Machine learning
Cohen, W. W., McCallum, A., & Roweis, S. T. (2008) · Cited by 5,691 · see citing works
Akaike, H. (1987) · Psychometrika · doi:10.1007/bf02294359 · Cited by 5,264 · see citing works
Akaike, H. (1987) · Springer Series in Statistics · doi:10.1007/978-1-4612-1694-0_29 · Cited by 4,027 · see citing works
The interaction of TIGIT with PVR and PVRL2 inhibits human NK cell cytotoxicity
Stanietsky, N., Simic, H., Arapovic, J., Toporik, A., Levy, O., Novik, A., Levine, Z., Beiman, M., Dassa, L., Achdout, H., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0903474106 · Cited by 3,524 · 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
Diversity of gene expression in adenocarcinoma of the lung
Garber, M. E., Troyanskaya, O. G., Schlüns, K., Petersen, S., Thäsler, Z., Pacyna-Gengelbach, M., van de Rijn, M., Rosen, G., Perou, C. M., Whyte, R. I., et al. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.241500798 · Cited by 1,301 · see citing works
Spike and slab variable selection: Frequentist and Bayesian strategies
Ishwaran, H., & Rao, J. S. (2005) · The Annals of Statistics · doi:10.1214/009053604000001147 · Cited by 1,031 · see citing works
Supervised Dictionary LearningPreprint
Mairal, J., Bach, F., Ponce, J., Sapiro, G., & Zisserman, A. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0809.3083 · Cited by 996 · see citing works
DNA methylation, methyltransferases, and cancer
Robertson, K. D. (2001) · Oncogene · doi:10.1038/sj.onc.1204341 · Cited by 732 · see citing works
Independent filtering increases detection power for high-throughput experiments
Bourgon, R., Gentleman, R. C., & Huber, W. K. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914005107 · Cited by 731 · see citing works
A statistical framework for genomic data fusion
Lanckriet, G. R. G., Bie, T. D., Cristianini, N., Jordan, M. I., & Noble, W. S. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth294 · Cited by 667 · see citing works
Bayesian Treed Gaussian Process Models With an Application to Computer Modeling
Gramacy, R. B., & Lee, H. K. H. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000689 · Cited by 666 · see citing works
An integrated approach to uncover drivers of cancer
Akavia, U. D., Litvin, O., Kim, J., Sanchez-Garcia, F., Kotliar, D., Causton, H. C., Pochanard, P., Mozes, E., Garraway, L. A., & Pe'er, D. (2010) · Cell · doi:10.1016/j.cell.2010.11.013 · Cited by 484 · see citing works
High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics
Carvalho, C. M., Chang, J. T., Lucas, J. E., Nevins, J. R., Wang, Q., & West, M. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000869 · Cited by 471 · see citing works
The Proceedings of the National Academy of Sciences
Wilson, E. B. (1918) · Science · doi:10.1126/science.48.1239.322.b · Cited by 402 · see citing works
Infinite Mixtures of Gaussian Process Experts
Rasmussen, C. E., & Ghahramani, Z. (2001) · Cited by 357 · see citing works
Hierarchical Beta Processes and the Indian Buffet Process
Thibaux, R., & Jordan, M. I. (2007) · UC Berkeley · Cited by 349 · see citing works
Nonparametric factor analysis with beta process priors
Paisley, J. W., & Carin, L. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553474 · Cited by 268 · see citing works
Gentleman, R. C., Carey, V. J., Huber, W. K., Irizarry, R. A., & Dudoit, S. (2005) · Springer eBooks · Cited by 264 · see citing works
An HDP-HMM for systems with state persistence
Fox, E. B., Sudderth, E. B., Jordan, M. I., & Willsky, A. S. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390196 · Cited by 260 · see citing works
Li, M., Balch, C., Montgomery, J. S., Jeong, M., Chung, J. H., Yan, P. S., Huang, T. H. ‐., Kim, S., & Nephew, K. P. (2009) · BMC Medical Genomics · doi:10.1186/1755-8794-2-34 · Cited by 225 · see citing works
Mixtures of Gaussian Processes
Tresp, V. (2000) · Cited by 192 · see citing works
Infinite Latent Feature Models and the Indian Buffet Process
Griffiths, T., & Ghahramani, Z. (2005) · Cambridge University Engineering Department Publications Database · Cited by 176 · see citing works
Rennstam, K., Ahlstedt-Soini, M., Baldetorp, B., Bendahl, P.-O., Borg, Å., Karhu, R., Tanner, M., Tirkkonen, M., & Isola, J. J. (2003) · PubMed · Cited by 164 · see citing works
Statistical Methods for Expression Quantitative Trait Loci (eQTL) Mapping
Kendziorski, C. M., Chen, M., Yuan, M., Lan, H., & Attie, A. (2006) · Biometrics · doi:10.1111/j.1541-0420.2005.00437.x · Cited by 155 · see citing works
iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data
Wang, W., Baladandayuthapani, V., Morris, J. S., Broom, B. M., Manyam, G. C., & Do, K.-A. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts655 · Cited by 154 · see citing works
Emdad, L., Sarkar, D., Su, Z.-Z., Lee, S., Kang, D., Bruce, J. N., Volsky, D. J., & Fisher, P. B. (2007) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2007.01.010 · Cited by 151 · see citing works
Tang, X., Lucas, J. E., Chen, J. L.-Y., LaMonte, G., Wu, J., Wang, M. C., Koumenis, C., & Chi, J. (2011) · Cancer Research · doi:10.1158/0008-5472.can-11-2076 · Cited by 126 · see citing works
Complex nature of SNP genotype effects on gene expression in primary human leucocytes
Heap, G., Trynka, G., Jansen, R. C., Bruinenberg, M., Swertz, M. A., Dinesen, L. C., Hunt, K. A., Wijmenga, C., van Heel, D. A., & Franke, L. (2009) · BMC Medical Genomics · doi:10.1186/1755-8794-2-1 · Cited by 121 · see citing works
Talloen, W., Clevert, D.-A., Hochreiter, S., Amaratunga, D., Bijnens, L., Kass, S. U., & Göhlmann, H. W. H. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm478 · Cited by 117 · see citing works
Sparse Probabilistic Projections
Archambeau, C., & Bach, F. R. (2009) · Cited by 117 · see citing works
Relationship of XIST expression and responses of ovarian cancer to chemotherapy.
Huang, K. C., Rao, P. H., Lau, C. C., Heard, É., Ng, S., Brown, C. J., Mok, S. C., Berkowitz, R. S., & Ng, S. W. (2002) · PubMed · Cited by 117 · see citing works
CNAmet: an R package for integrating copy number, methylation and expression data
Louhimo, R., & Hautaniemi, S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr019 · Cited by 116 · see citing works
An Alternative Infinite Mixture Of Gaussian Process Experts
Meeds, E., & Osindero, S. (2005) · Cited by 111 · see citing works
Five genes from chromosomal band 8p22 are significantly down‐regulated in ovarian carcinoma
Pils, D., Horak, P., Gleiß, A., Sax, C., Fabjani, G., Moebus, V. J., Zielinski, C. C., Reinthaller, A., Zeillinger, R., & Krainer, M. (2005) · Cancer · doi:10.1002/cncr.21538 · Cited by 102 · see citing works
Copy number variant in the candidate tumor suppressor gene MTUS1 and familial breast cancer risk
Frank, B., Bermejo, J. L., Hemminki, K., Sutter, C., Wappenschmidt, B., Meindl, A., Kiechle, M. B., Bugert, P., Schmutzler, R. K., Bartram, C. R., et al. (2007) · Carcinogenesis · doi:10.1093/carcin/bgm033 · Cited by 100 · see citing works
Misbehaviour of XIST RNA in Breast Cancer Cells
Sirchia, S. M., Tabano, S. M., Monti, L., Recalcati, M. P., Gariboldi, M., Grati, F. R., Porta, G., Finelli, P., Radice, P., & Miozzo, M. R. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005559 · Cited by 84 · see citing works
Probabilistic approach to detecting dependencies between data sets
Klami, A., & Kaski, S. (2008) · Neurocomputing · doi:10.1016/j.neucom.2007.12.044 · Cited by 54 · see citing works
Expression of EBAG9/RCAS1 is associated with advanced disease in human epithelial ovarian cancer
Akahira, J.-I., Aoki, M., Suzuki, T., Moriya, T., Niikura, H., Ito, K., Inoue, S., Okamura, K., Sasano, H., & Yaegashi, N. (2004) · British Journal of Cancer · doi:10.1038/sj.bjc.6601832 · Cited by 49 · see citing works
Lucas, J. E., Kung, H., & Chi, J. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000920 · Cited by 48 · see citing works
E2F5 status significantly improves malignancy diagnosis of epithelial ovarian cancer
Kothandaraman, N., Bajić, V. B., Brendan, P. N., Huak, C. Y., Peh, B. K., Razvi, K., Salto‐Tellez, M., & Choolani, M. A. (2010) · BMC Cancer · doi:10.1186/1471-2407-10-64 · Cited by 47 · see citing works
Miyamoto, K., Morishita, Y., Yamazaki, M., Minamino, N., Kangawa, K., Matsuo, H., Mizutani, T., Yamada, K., & Minegishi, T. (2001) · Archives of Biochemistry and Biophysics · doi:10.1006/abbi.2001.2367 · Cited by 39 · see citing works
Predicting Viral Infection From High-Dimensional Biomarker Trajectories
Chen, M., Zaas, A. K., Woods, C. W., Ginsburg, G. S., Lucas, J., Dunson, D. B., & Carin, L. (2011) · Journal of the American Statistical Association · doi:10.1198/jasa.2011.ap10611 · Cited by 31 · see citing works
VSGP/F-Spondin: A New Ovarian Cancer Marker
Pyle-Chenault, R. A., Stolk, J. A., Molesh, D. A., Harlan, D., McNeill, P. D., Repasky, E. A., Jiang, Z., Fanger, G. R., & Xu, J. (2005) · Tumor Biology · doi:10.1159/000087379 · Cited by 30 · see citing works
Aneuploidy in Health and Disease
Štorchová, Z. (2012) · InTech eBooks · doi:10.5772/2155 · Cited by 22 · see citing works
Sticky Hidden Markov Modeling of Comparative Genomic Hybridization
Du, L., Chen, M., Lucas, J. E., & Carin, L. (2010) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2010.2053033 · Cited by 20 · see citing works
Jeong, J., Li, L., Liu, Y., Nephew, K. P., Huang, T. H. ‐., & Shen, C. (2010) · BMC Medical Genomics · doi:10.1186/1755-8794-3-55 · Cited by 18 · see citing works
A Bayesian graphical model for integrative analysis of TCGA data
Xu, Y., Zhang, J., Yuan, Y., Mitra, R., Müller, P., & Ji, Y. (2012) · IEEE International Workshop on Genomic Signal Processing and Statistics · doi:10.1109/gensips.2012.6507747 · Cited by 12 · see citing works
Statistics: A Key to Innovation in a Data-Centric World!
Pantula, S. G. (2011) · Journal of the American Statistical Association · doi:10.1198/jasa.2011.ap10508 · Cited by 9 · see citing works
Hierarchical Bayesian methods for integration of various types of genomics data
Jennings, E. M., Morris, J. S., Carroll, R. J., Manyam, G. C., & Baladandayuthapani, V. (2012) · IEEE International Workshop on Genomic Signal Processing and Statistics · doi:10.1109/gensips.2012.6507713 · Cited by 3 · see citing works
Non-parametric Bayesian modeling and fusion of spatio-temporal information sources
Ray, P., & Carin, L. (2011) · Cited by 3 · see citing works
Uncover Cancer Genomics by Jointly Analysing Aneuploidy and Gene Expression
Zheng, L., & Lucas, J. E. (2012) · InTech eBooks · doi:10.5772/36338 · Cited by 2 · see citing works
Flagged first, then by citations
3 of the 56 references couldn’t be matched to a known paper.
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
Data Fusion and IoT for Smart Ubiquitous Environments: A Survey
Alam, F., Mehmood, R., Katib, I. A., Albogami, N., & Albeshri, A. A. (2017) · IEEE Access · doi:10.1109/access.2017.2697839 · Cited by 389 · see citing works
Lock, E. F., & Dunson, D. B. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt425 · Cited by 308 · see citing works
Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools
Nicora, G., Vitali, F., Dagliati, A., Geifman, N., & Bellazzi, R. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01030 · Cited by 284 · see citing works
Multi-omics data integration considerations and study design for biological systems and disease
Graw, S., Chappell, K., Washam, C. L., Gies, A. J., Bird, J. T., Robeson, M. S., & Byrum, S. D. (2020) · Molecular Omics · doi:10.1039/d0mo00041h · Cited by 223 · 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
Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction
Zhou, G., Cichocki, A. S., Zhang, Y., & Mandic, D. P. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2487364 · Cited by 181 · see citing works
Cembrowska-Lech, D., Krzemińska, A., Miller, T. I., Nowakowska, A., Adamski, C., Radaczyńska, M., Mikiciuk, G., & Mikiciuk, M. (2023) · Biology · doi:10.3390/biology12101298 · Cited by 193 · 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
Arto Klami, A., Virtanen, S., Leppäaho, E., & Samuel Kaski, A. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2376974 · Cited by 105 · see citing works
Common and distinct components in data fusion
Smilde, A. K., Måge, I., Næs, T., Hankemeier, T., Lips, M. A., Kiers, H. A. L., Acar, E., & Bro, R. (2017) · Journal of Chemometrics · doi:10.1002/cem.2900 · 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
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
R.JIVE for exploration of multi-source molecular data
O’Connell, M. J., & Lock, E. F. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw324 · Cited by 80 · see citing works
Structural Learning and Integrative Decomposition of Multi-View Data
Gaynanova, I., & Li, G. (2019) · Biometrics · doi:10.1111/biom.13108 · Cited by 78 · see citing works
Genetic heterogeneity: Challenges, impacts, and methods through an associative lens
Woodward, A., Urbanowicz, R. J., Naj, A. C., & Moore, J. H. (2022) · Genetic Epidemiology · doi:10.1002/gepi.22497 · Cited by 69 · 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
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
Bayesian group factor analysis with structured sparsity.
Zhao, S., Gao, C., Mukherjee, S., & Engelhardt, B. E. (2016) · PubMed · doi:10.5555/2946645.3053478 · Cited by 48 · see citing works
Mallik, S., & Zhao, Z. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bby120 · Cited by 46 · see citing works
Champion, M., Brennan, K., Croonenborghs, T., Gentles, A. J., Pochet, N. L. M. M., & Gevaert, O. (2017) · EBioMedicine · doi:10.1016/j.ebiom.2017.11.028 · Cited by 36 · 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
Integrative clustering of high-dimensional data with joint and individual clusters
Hellton, K. H., & Thoresen, M. (2016) · Biostatistics · doi:10.1093/biostatistics/kxw005 · 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
Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer
Sharma, V., Singh, A. K., Chauhan, S., Sharma, P. K., Chaudhary, S., Sharma, A., Porwal, O., & Fuloria, N. K. (2023) · Current Drug Delivery · doi:10.2174/1567201821666230905090621 · Cited by 35 · see citing works
Vitali, F., Li, Q., Schissler, A. G., Berghout, J., Kenost, C., & Lussier, Y. A. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx149 · Cited by 31 · see citing works
Prediction With Dimension Reduction of Multiple Molecular Data Sources for Patient Survival
Kaplan, A. N., & Lock, E. F. (2017) · Cancer Informatics · doi:10.1177/1176935117718517 · Cited by 31 · see citing works
Incorporating Covariates Into Integrated Factor Analysis of Multi-View Data
Li, G., & Jung, S. (2017) · Biometrics · doi:10.1111/biom.12698 · Cited by 30 · see citing works
Zhu, H., Li, G., & Lock, E. F. (2018) · Biostatistics · doi:10.1093/biostatistics/kxy052 · Cited by 29 · see citing works
Integrative omics - from data to biology
Dihazi, H., Asif, A. R., Beissbarth, T., Bohrer, R., Feussner, K., Feussner, I., Jahn, O., Lenz, C., Majcherczyk, A., Schmidt, B., et al. (2018) · Espert Review of Proteomics · doi:10.1080/14789450.2018.1476143 · Cited by 23 · see citing works
Patient-Specific Multi-Omics Models and the Application in Personalized Combination Therapy
John, A. J., Qin, B., Kalari, K. R., Wang, L., & Yu, J. (2020) · Future Oncology · doi:10.2217/fon-2020-0119 · Cited by 27 · see citing works
Clustering Longitudinal Data: A Review of Methods and Software Packages
Lu, Z. (2024) · International Statistical Review · doi:10.1111/insr.12588 · Cited by 23 · see citing works
Integrative Analyses of Cancer Data: A Review from a Statistical Perspective
Wei, Y. (2015) · Cancer Informatics · doi:10.4137/cin.s17303 · Cited by 21 · see citing works
Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges
Coates, J. T. T., Pirovano, G., & Naqa, I. E. (2021) · Journal of medical imaging · doi:10.1117/1.jmi.8.3.031902 · Cited by 21 · see citing works
Multimodal Data Fusion in High-Dimensional Heterogeneous Datasets Via Generative Models
Yılmaz, Y., Aktukmak, M., & Hero, A. O. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2021.3109375 · Cited by 21 · see citing works
Demirel, H. C., Arici, M. K., & Tunçbağ, N. (2021) · Molecular Omics · doi:10.1039/d1mo00158b · Cited by 26 · see citing works
Review of prostate cancer genomic studies in Africa
Samtal, C., Jaddaoui, I. E., Hamdi, S., Bouguenouch, L., Ouldim, K., Nejjari, C., Ghazal, H., & Bekkari, H. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.911101 · Cited by 17 · see citing works
Integrating hypertension phenotype and genotype with hybrid non-negative matrix factorization
Luo, Y., Mao, C., Yang, Y., Wang, F., Ahmad, F. S., Arnett, D. K., Irvin, M. R., & Shah, S. J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz049 · Cited by 17 · see citing works
DSP based entropy estimation for identification and classification of Homo sapiens cancer genes
Das, J., & Barman, S. (2016) · Microsystem Technologies · doi:10.1007/s00542-016-3056-3 · Cited by 16 · see citing works
DIFFERENT GENOMIC SIGNAL PROCESSING METHODS FOR EUKARYOTIC GENE PREDICTION: A SYSTEMATIC REVIEW
Mabrouk, M. S., Naeem, S. M., & Eldosoky, M. A. A. (2017) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237217300012 · Cited by 14 · see citing works
Integrating hypertension phenotype and genotype with hybrid non-negative matrix factorization
Luo, Y., Mao, C., Yang, Y., Wang, F., Ahmad, F. S., Arnett, D. K., Irvin, M. R., & Shah, S. J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty804 · Cited by 12 · see citing works
Nikolova, O. H., Moser, R. D., Kemp, C. J., Gönen, M., & Margolin, A. A. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw836 · Cited by 12 · see citing works
Tuly, S. R., Ranjbari, S., Murat, E. A., & Arslanturk, S. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110108 · Cited by 11 · see citing works
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Zhou, K., Kottoori, B. S., Munj, S. A., Zhang, Z., Drăghici, S., & Arslanturk, S. (2022) · Biology · doi:10.3390/biology11030360 · Cited by 12 · see citing works
Bayesian multitask learning regression for heterogeneous patient cohorts
Gonçalves, A. R., Ray, P., Soper, B., Widemann, D. P., Nygård, M., Nygård, J. F., & Sales, A. P. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.yjbinx.2019.100059 · Cited by 10 · see citing works
Integrative Analysis of Multimodal Omics Data
Li, G., & Lock, E. F. (2025) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-042424-113016 · Cited by 7 · 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
Mass spectrometry for mitochondrial multi-omics
Wang, Z., Liu, B., Lin, L., & Qiao, L. (2023) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2023.117063 · Cited by 7 · see citing works
Bayesian Joint Modeling of Chemical Structure and Dose Response Curves
Moran, K. R., Dunson, D. B., Wheeler, M. W., & Herring, A. H. (2020) · The Annals of Applied Statistics · doi:10.1214/21-aoas1461 · Cited by 8 · see citing works
Dai, L.-Y., Zhu, R., & Wang, J. (2020) · Complexity · doi:10.1155/2020/3917812 · Cited by 6 · see citing works
panomiX: Investigating mechanisms of trait emergence through multi-omics data integration
Sahu, A., Psaroudakis, D., Rolletschek, H., Neumann, K., Borisjuk, L., Himmelbach, A., Pinninti, K., Knoch, D., Töpfer, N., & Szymański, J. (2025) · Plant Phenomics · doi:10.1016/j.plaphe.2025.100131 · Cited by 5 · see citing works
Bayesian Fusion in Cancer Gene Prediction
Das, J., & Barman, S. (2015) · Cited by 5 · see citing works
Bayesian time-aligned factor analysis of paired multivariate time series
Roy, A., Borg, J. S., & Dunson, D. B. (2019) · PubMed · doi:10.48550/arxiv.1904.12103 · Cited by 5 · 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
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
Zhang, Q., Chang, C., Shen, L., & Long, Q. (2024) · Biometrics · doi:10.1093/biomtc/ujad014 · Cited by 4 · see citing works
Bayesian group latent factor analysis with structured sparsityPreprint
Zhao, S., Gao, C., Mukherjee, S., & Engelhardt, B. E. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.2698 · Cited by 4 · see citing works
Deep Multi-View Learning via Task-Optimal CCAPreprint
Couture, H. D., Kwitt, R., Marron, J. S., Troester, M. A., Perou, C. M., & Niethammer, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.07739 · Cited by 4 · see citing works
Integrative Analysis of Multi-Omics Data
Tripathi, L. P., Esaki, T., Itoh, M. N., Chen, Y. A., & Mizuguchi, K. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20096-4 · 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
Biological pathway selection through Bayesian integrative modeling
Zheng, L., Xiao, Y., Suchindran, S., Dressman, H. K., Chute, J. P., & Lucas, J. E. (2014) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2013-0043 · Cited by 2 · see citing works
Application of Multiblock Analysis on Small Metabolomic Multi-Tissue Dataset
Torell, F., Skotare, T., & Trygg, J. (2020) · Metabolites · doi:10.3390/metabo10070295 · 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
Precision Oncology: Multimodal and Multiscale Methods to Promote Mechanistic Understanding
Öztürk, K., Klie, A., & Carter, H. K. (2026) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-092724-043507 · Cited by 1 · see citing works
Alzu'bi, A. A., & Zhou, L. (2017) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2017.8 · 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
Poythress, J. C., Park, C., & Ahn, J. (2021) · Journal of Applied Statistics · doi:10.1080/02664763.2021.1967892 · Cited by 1 · see citing works
Supervised Multimodal Fission Learning
Mao, L., Wang, Q., Su, Y., Lure, F. Y. M., Chong, C. D., Schwedt, T. J., & Li, J. (2026) · INFORMS Journal on Data Science · doi:10.1287/ijds.2024.0059 · Cited by 1 · see citing works
Machine Learning Meets Big Data: An Overview of Diagnostic and Prognostic Prediction for Cancer
Ma, B., Chai, B., Fang, M., Qi, J., Hou, X., Zhang, X., Gao, Z., Meng, F., & Song, F. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-1354-8_21 · Cited by 1 · see citing works
Champion, M., Brennan, K., Croonenborghs, T., Gentles, A. J., Pochet, N. L. M. M., & Gevaert, O. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/216754 · Cited by 1 · see citing works
panomiX: Investigating Mechanisms Of Trait Emergence Through Multi-Omics Data IntegrationPreprint
Sahu, A., Psaroudakis, D., Rolletschek, H., Neumann, K., Borisjuk, L., Himmelbach, A., Pinninti, K., Töpfer, N., & Szymański, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.11.648356 · Cited by 1 · see citing works
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
Integrative Analyses for Multi-source Data with Multiple Shared Dimensions
O’Connell, M. J. (2018) · University of Minnesota Digital Conservancy (University of Minnesota)
Incorporating Covariates into Integrated Factor Analysis of Multi-View DataPreprint
Li, G., & Jung, S. (2017) · arXiv (Cornell University)
Common and Distinct Components in Data Fusion
Smilde, A. K., Måge, I., Næs, T., Hankemeier, T., Lips, M. A., Kiers, H. A. L., Acar, E., & Bro, R. (2016) · NORA - Norwegian Open Research Archives · doi:10.48550/arxiv.1607.02328
Angle-Based Joint and Individual Variation ExplainedPreprint
Feng, Q., Jiang, M., Hannig, J., & Marron, J. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.02060
Applications of Computational Systems Biology in Cancer Signaling Pathways
Sandhu, V., Manem, V. S., Mer, A. S., Kure, E. H., & Haibe‐Kains, B. (2019) · Springer eBooks · doi:10.1007/978-981-32-9816-3_19
Rincourt, S.-L., Michiels, S., & Drubay, D. (2022) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720022500226
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
- Authors
- Ray, P., Zheng, L., Lucas, J., & Carin, L.
- Affiliations
- Indian Institute of Technology Kharagpur (IN)
- 1 G.S.Sanyal School of Telecommunications, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India, 2Computational Biology & Bioinformatics, Duke University, Durham, NC 27705, USA, 3Quintiles, Durham, NC 27703, USA and 4Electrical & Computer Engineering Department, Duke University, Durham, NC 27705, USA
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-01-30
- Volume
- 30
- Issue
- 10
- Pages
- 1370-1376
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2130186565
- Cited by
- 84
- References
- 56
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 3 · 2023: 5 · 2022: 8 · 2021: 8 · 2020: 9 · 2019: 9 · 2018: 9 · 2017: 13 · 2016: 4 · 2015: 4 · 2014: 3 · 2013: 1
- FWCI
- 3.28
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Biomedical Advanced Research and Development Authority
- Office of Naval Research
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- ovarian cancer, cancer drivers
- MeSH terms
- Bayes Theorem, Female, Gene Expression Regulation, Humans, Ovarian Neoplasms, Software, DNA Methylation, Genomics, DNA Copy Number Variations
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