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A Review of Integrative Imputation for Multi-Omics Datasets
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Multi-omics studies, which explore the interactions between multiple types of biological factors, have significant advantages over single-omics analysis for their ability to provide a more holistic view of biological processes, uncover the causal and functional mechanisms for complex diseases, and facilitate new discoveries in precision medicine. However, omics datasets often contain missing values, and in multi-omics study designs it is common for individuals to be represented for some omics layers but not all. Since most statistical analyses cannot be applied directly to the incomplete datasets, imputation is typically performed to infer the missing values. Integrative imputation techniques which make use of the correlations and shared information among multi-omics datasets are expected to outperform approaches that rely on single-omics information alone, resulting in more accurate results for the subsequent downstream analyses. In this review, we provide an overview of the currently available imputation methods for handling missing values in bioinformatics data with an emphasis on multi-omics imputation. In addition, we also provide a perspective on how deep learning methods might be developed for the integrative imputation of multi-omics datasets.
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“However, omics data often lack certain values, and in studies on omics data, individuals are generally represented in some, but not all, omics layers [105].”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [105]
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PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Integrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368
Carroll, J. D., & Chang, J.-J. (1970) · Psychometrika · doi:10.1007/bf02310791 · Cited by 4,761
Missing Value Estimation Methods for DNA Microarrays
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., & Altman, R. B. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.6.520 · Cited by 4,230
Howie, B. N., Donnelly, P., & Marchini, J. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000529 · Cited by 3,928
Next-generation genotype imputation service and methods
Das, S., Forer, L., Schönherr, S., Sidore, C., Locke, A. E., Kwong, A., Vrieze, S. I., Chew, E. Y., Levy, S., McGue, M., et al. (2016) · Nature Genetics · doi:10.1038/ng.3656 · Cited by 3,808
Review The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge
Tomczak, K. J., Czerwińska, P., & Wiznerowicz, M. (2015) · Współczesna Onkologia · doi:10.5114/wo.2014.47136 · Cited by 3,624
Central Dogma of Molecular Biology
CRICK, F. (1970) · Nature · doi:10.1038/227561a0 · Cited by 3,444
Browning, S. R., & Browning, B. L. (2007) · The American Journal of Human Genetics · doi:10.1086/521987 · Cited by 3,217
Simultaneous epitope and transcriptome measurement in single cells
Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., & Smibert, P. (2017) · Nature Methods · doi:10.1038/nmeth.4380 · Cited by 3,098
Deep generative modeling for single-cell transcriptomics
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018) · Nature Methods · doi:10.1038/s41592-018-0229-2 · Cited by 2,530
Integrative approaches for large-scale transcriptome-wide association studies
Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B. W. J. H., Jansen, R., de Geus, E. J. C., Boomsma, D. I., Wright, F. A., et al. (2016) · Nature Genetics · doi:10.1038/ng.3506 · Cited by 2,479
Opportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310
Harshman, R. A. (1970) · Cited by 2,292
A One-Penny Imputed Genome from Next-Generation Reference Panels
Browning, B. L., Zhou, Y., & Browning, S. R. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.07.015 · Cited by 2,159
Scheet, P. A., & Stephens, M. (2006) · The American Journal of Human Genetics · doi:10.1086/502802 · Cited by 1,959
MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes
Li, Y., Willer, C. J., Ding, J., Scheet, P., & Abecasis, G. R. (2010) · Genetic Epidemiology · doi:10.1002/gepi.20533 · Cited by 1,843
Recovering Gene Interactions from Single-Cell Data Using Data Diffusion
van Dijk, D., Sharma, R., Nainys, J., Yim, K., Kathail, P., Carr, A. J., Burdziak, C., Moon, K. R., Chaffer, C. L., Pattabiraman, D., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.061 · Cited by 1,829
A gene-based association method for mapping traits using reference transcriptome data
Gamazon, E. R., Wheeler, H. E., Shah, K. P., Mozaffari, S. V., Aquino-Michaels, K., Carroll, R. J., Eyler, A. E., Denny, J. C., Nicolae, D. L., Cox, N. J., et al. (2015) · Nature Genetics · doi:10.1038/ng.3367 · Cited by 1,790
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
Integrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460
Eleven grand challenges in single-cell data science
Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1926-6 · Cited by 1,327
MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data
Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J. C., & Stegle, O. (2020) · Genome Biology · doi:10.1186/s13059-020-02015-1 · Cited by 1,091
Single-cell RNA-seq denoising using a deep count autoencoder
Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S., & Theis, F. J. (2019) · Nature Communications · doi:10.1038/s41467-018-07931-2 · Cited by 1,084
Hou, L., Kellis, M., Liu, Y., Park, Y., & Rinaldi, N. J. (2018) · DSpace@MIT (Massachusetts Institute of Technology) · doi:10.34944/dspace/4648 · Cited by 1,082
imputeTS: Time Series Missing Value Imputation in R
Möritz, S., & Bartz–Beielstein, T. (2017) · The R Journal · doi:10.32614/rj-2017-009 · Cited by 1,024
Opportunities and challenges for transcriptome-wide association studies
Wainberg, M., Sinnott-Armstrong, N., Mancuso, N., Barbeira, A., Knowles, D. A., Golan, D., Ermel, R., Ruusalepp, A., Quertermous, T., Hao, K., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0385-z · Cited by 906
Multiplexed quantification of proteins and transcripts in single cells
Peterson, V. M., Zhang, K. X., Kumar, N., Wong, J., Li, L., Wilson, D. C., Moore, R., McClanahan, T. K., Sadekova, S., & Klappenbach, J. A. (2017) · Nature Biotechnology · doi:10.1038/nbt.3973 · Cited by 832
Polygenic Modeling with Bayesian Sparse Linear Mixed Models
Zhou, X., Carbonetto, P. S., & Stephens, M. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003264 · Cited by 802
Random forest missing data algorithms
Tang, F., & Ishwaran, H. (2017) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11348 · Cited by 793
More Is Better: Recent Progress in Multi-Omics Data Integration Methods
Huang, S., Chaudhary, K., & Garmire, L. X. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00084 · Cited by 739
SAVER: gene expression recovery for single-cell RNA sequencing
Huang, M. C., Wang, J., Torre, E. A., Dueck, H., Shaffer, S. M., Bonasio, R., Murray, J. I., Raj, A., Li, M., & Zhang, N. R. (2018) · Nature Methods · doi:10.1038/s41592-018-0033-z · Cited by 739
An accurate and robust imputation method scImpute for single-cell RNA-seq data
Li, W. V., & Li, J. J. (2018) · Nature Communications · doi:10.1038/s41467-018-03405-7 · Cited by 693
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
RNA splicing is a primary link between genetic variation and disease
Li, Y. I., van de Geijn, B., Raj, A., Knowles, D. A., Petti, A. A., Golan, D., Gilad, Y., & Pritchard, J. K. (2016) · Science · doi:10.1126/science.aad9417 · Cited by 660
Clark, S. J., Argelaguet, R., Kapourani, C.-A., Stubbs, T. M., Lee, H. J., Alda-Catalinas, C., Krueger, F., Sanguinetti, G., Kelsey, G., Marioni, J. C., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03149-4 · Cited by 657
Genome-wide genetic data on ~500,000 UK Biobank participantsPreprint
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/166298 · Cited by 622
Systems genetics approaches to understand complex traits
Civelek, M., & Lusis, A. J. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3575 · Cited by 621
Dudbridge, F. (2008) · Human Heredity · doi:10.1159/000119108 · Cited by 607
Lazar, C., Gatto, L., Ferro, M., Bruley, C., & Bürger, T. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00981 · Cited by 549
DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning
Angermueller, C., Lee, H. J., Reik, W., & Stegle, O. (2017) · Genome Biology · doi:10.1186/s13059-017-1189-z · Cited by 536
A Mass Spectrometric-Derived Cell Surface Protein Atlas
Bausch‐Fluck, D., Hofmann, A., Bock, T., Frei, A. P., Cerciello, F., Jacobs, A., Moest, H., Omasits, U., Gundry, R. L., Yoon, C., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0121314 · Cited by 441
Efficient haplotype matching and storage using the positional Burrows–Wheeler transform (PBWT)
Durbin, R. M. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu014 · Cited by 436
A review on machine learning principles for multi-view biological data integration
Li, Y., Wu, F., & Ngom, A. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw113 · Cited by 432
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
A statistical framework for cross-tissue transcriptome-wide association analysis
Hu, Y., Li, M., Lu, Q., Weng, H., Wang, J., Zekavat, S. M., Yu, Z., Li, B., Gu, J., Muchnik, S. K., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0345-7 · Cited by 407
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
Late Fusion Incomplete Multi-View Clustering
Liu, X., Zhu, X., Li, M., Wang, L., Tang, C., Yin, J., Shen, D. G., Wang, H., & Gao, W. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2879108 · Cited by 368
Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues
Ernst, J., & Kellis, M. (2015) · Nature Biotechnology · doi:10.1038/nbt.3157 · Cited by 357
Li, Q., & Lin, N. (2010) · Bayesian Analysis · doi:10.1214/10-ba506 · Cited by 345
A road map for understanding molecular and genetic determinants of osteoporosis
Yang, T.-L., Shen, H., Liu, A., Dong, S.-S., Zhang, L., Deng, F.-Y., Zhao, Q., & Deng, H.-W. (2020) · Nature Reviews Endocrinology · doi:10.1038/s41574-019-0282-7 · Cited by 341
Self-citationIntegrating predicted transcriptome from multiple tissues improves association detection
Barbeira, A., Pividori, M., Zheng, J., Wheeler, H. E., Nicolae, D. L., & Im, H. K. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1007889 · Cited by 337
DrImpute: imputing dropout events in single cell RNA sequencing data
Gong, W., Kwak, I., Pota, P., Koyano‐Nakagawa, N., & Garry, D. J. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2226-y · Cited by 318
A systematic evaluation of single-cell RNA-sequencing imputation methods
Hou, W., Ji, Z., Ji, H., & Hicks, S. C. (2020) · Genome biology · doi:10.1186/s13059-020-02132-x · Cited by 307
Arisdakessian, C. G., Poirion, O., Yunits, B., Zhu, X., & Garmire, L. X. (2019) · Genome biology · doi:10.1186/s13059-019-1837-6 · Cited by 302
Webb-Robertson, B.-J. M., Wiberg, H. K., Matzke, M. M., Brown, J. N., Wang, J., McDermott, J. E., Smith, R. D., Rodland, K. D., Metz, T. O., Pounds, J. G., et al. (2015) · Journal of Proteome Research · doi:10.1021/pr501138h · Cited by 294
Exploring Single-Cell Data with Deep Multitasking Neural Networks
Amodio, M., van Dijk, D., Srinivasan, K., Chen, W. S., Mohsen, H., Moon, K. R., Campbell, A., Zhao, Y.-J., Wang, X.-M., Venkataswamy, M., et al. (2017) · Nature Methods · doi:10.1038/s41592-019-0576-7 · Cited by 288
Kowalski, M. H., Qian, H., Hou, Z., Rosen, J. D., Tapia, A. L., Shan, Y., Jain, D., Argos, M., Arnett, D. K., Avery, C. L., et al. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008500 · Cited by 273
Genotype Imputation from Large Reference Panels
Das, S., Abecasis, G. R., & Browning, B. L. (2018) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083117-021602 · Cited by 232
SCALE method for single-cell ATAC-seq analysis via latent feature extraction
Xiong, L., Xu, K., Tian, K., Shao, Y., Tang, L., Gao, G., Zhang, M., Jiang, T., & Zhang, Q. C. (2019) · Nature Communications · doi:10.1038/s41467-019-12630-7 · Cited by 208
K-Nearest Neighbor (K-NN) based Missing Data Imputation
Murti, D. M. P., Pujianto, U., Wibawa, A. P., & Akbar, M. (2019) · International Conference on Science in Information Technology · doi:10.1109/icsitech46713.2019.8987530 · Cited by 207
Gaussian mixture clustering and imputation of microarray data
Ouyang, M., Welsh, W. J., & Georgopoulos, P. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth007 · Cited by 202
Single Cell Multi-Omics Technology: Methodology and Application
Hu, Y., An, Q., Sheu, K. M., Trejo, B. M., Fan, S., & Guo, Y. Q. (2018) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2018.00028 · Cited by 201
Data denoising with transfer learning in single-cell transcriptomics
Wang, J., Agarwal, D., Huang, M. C., Hu, G., Zhou, Z., Ye, C., & Zhang, N. R. (2019) · Nature Methods · doi:10.1038/s41592-019-0537-1 · Cited by 190
scIGANs: single-cell RNA-seq imputation using generative adversarial networks
Xu, Y., Zhang, Z., You, L., Liu, J., Fan, Z., & Zhou, X. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa506 · Cited by 188
Testing Untyped Alleles (TUNA)—applications to genome‐wide association studies
Nicolae, D. L. (2006) · Genetic Epidemiology · doi:10.1002/gepi.20182 · Cited by 185
Making multi-omics data accessible to researchers
Conesa, A., & Beck, S. (2019) · Scientific Data · doi:10.1038/s41597-019-0258-4 · Cited by 183
A Powerful and Adaptive Association Test for Rare Variants
Pan, W., Kim, J., Zhang, Y., Shen, X., & Wei, P. (2014) · Genetics · doi:10.1534/genetics.114.165035 · Cited by 172
AutoImpute: Autoencoder based imputation of single-cell RNA-seq data
Talwar, D., Mongia, A., Sengupta, D., & Majumdar, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-34688-x · Cited by 166
Välikangas, T., Suomi, T., & Elo, L. L. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx054 · Cited by 143
False signals induced by single-cell imputation
Andrews, T. S., & Hemberg, M. (2018) · F1000Research · doi:10.12688/f1000research.16613.1 · Cited by 142
Chen, M., & Zhou, X. (2018) · Genome biology · doi:10.1186/s13059-018-1575-1 · Cited by 135
Nagpal, S., Meng, X., Epstein, M. P., Tsoi, L. C., Patrick, M. T., Gibson, G. C., Jager, P. L. D., Bennett, D. A., Wingo, A. P., Wingo, T. S., et al. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.05.018 · Cited by 134
Schreiber, J. M., Durham, T., Bilmes, J. A., & Noble, W. S. (2020) · Genome biology · doi:10.1186/s13059-020-01977-6 · Cited by 115
Improving missing value estimation in microarray data with gene ontology
Tuikkala, J., Elo, L. L., Nevalainen, O., & Aittokallio, T. A. (2005) · Bioinformatics · doi:10.1093/bioinformatics/btk019 · Cited by 114
Simple and Efficient Analysis of Disease Association with Missing Genotype Data
Lin, D. Y., Hu, Y., & Huang, B. E. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2007.11.004 · Cited by 91
Zhang, W., Voloudakis, G., Rajagopal, V. M., Readhead, B., Dudley, J. T., Schadt, E. E., Björkegren, J., Kim, Y., Fullard, J. F., Hoffman, G. E., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11874-7 · Cited by 90
Surface protein imputation from single cell transcriptomes by deep neural networks
Zhou, Z., Ye, C., Wang, J., & Zhang, N. R. (2020) · Nature Communications · doi:10.1038/s41467-020-14391-0 · Cited by 86
Autoregressive-Model-Based Missing Value Estimation for DNA Microarray Time Series Data
Choong, M. K., Charbit, M., & Yan, H. (2009) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2008.2007421 · Cited by 76
PREDICTD PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition
Durham, T., Libbrecht, M. W., Howbert, J. J., Bilmes, J., & Noble, W. S. (2018) · Nature Communications · doi:10.1038/s41467-018-03635-9 · Cited by 74
Statistical single cell multi-omics integration
Colomé‐Tatché, M., & Theis, F. J. (2018) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2018.01.003 · Cited by 72
A Powerful Framework for Integrating eQTL and GWAS Summary Data
Xu, Z., Wu, C., Wei, P., & Pan, W. (2017) · Genetics · doi:10.1534/genetics.117.300270 · Cited by 72
Self-citationBarbeira, A. N., Dickinson, S. P., Bonazzola, R., Zheng, J., Wheeler, H. E., Torres, J. M., Torstenson, E. S., Shah, K. P., Garcia, T., Edwards, T. L., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03621-1 · Cited by 69
Voillet, V., Besse, P. C., Liaubet, L., Cristobal, M. S., & González, I. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1273-5 · Cited by 66
Melissa: Bayesian clustering and imputation of single-cell methylomes
Kapourani, C.-A., & Sanguinetti, G. (2019) · Genome biology · doi:10.1186/s13059-019-1665-8 · Cited by 64
Sequential imputation for missing values
Verboven, S., Branden, K. V., & Goos, P. (2007) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2007.07.001 · Cited by 62
TOBMI: trans-omics block missing data imputation using a k-nearest neighbor weighted approach
Dong, X., Lin, L., Zhang, R., Zhao, Y., Christiani, D. C., Wei, Y., & Chen, F. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty796 · Cited by 50
Zhou, X., Chai, H., Zhao, H., Luo, C., & Yang, Y. (2020) · GigaScience · doi:10.1093/gigascience/giaa076 · Cited by 50
Sparse Convolutional Denoising Autoencoders for Genotype Imputation
Chen, J., & Shi, X. M. (2019) · Genes · doi:10.3390/genes10090652 · Cited by 49
Exploring Single-Cell Data with Deep Multitasking Neural NetworksPreprint
Amodio, M., van Dijk, D., Srinivasan, K., Chen, W. S., Mohsen, H., Moon, K. R., Campbell, A. M., Zhao, Y., Wang, X., Venkataswamy, M. M., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/237065 · Cited by 45
Hot Deck Methods for Imputing Missing Data
Joenssen, D. W., & Bankhofer, U. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31537-4_6 · Cited by 41
An integrative imputation method based on multi-omics datasets
Lin, D., Zhang, J., Li, J., Xu, C., Deng, H., & Wang, Y. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1122-6 · Cited by 39
Self-citationEvaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression
Li, B., Verma, S. S., Veturi, Y., Verma, A., Bradford, Y., Haas, D. W., & Ritchie, M. D. (2017) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813235533_0041 · Cited by 36
Polygenic Modeling with Bayesian Sparse Linear Mixed ModelsPreprint
Zhou, X., Carbonetto, P., & Stephens, M. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1209.1341 · Cited by 31
Yang, Y., Shi, X., Jiao, Y., Huang, J., Chen, M., Zhou, X., Sun, L., Lin, X., Yang, C., & Liu, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz880 · Cited by 31
Fryett, J. J., Morris, A. P., & Cordell, H. J. (2020) · Genetic Epidemiology · doi:10.1002/gepi.22290 · Cited by 28
Missing Data: A Comparison of Neural Network and Expectation Maximisation TechniquesPreprint
Nelwamondo, F. V., Mohamed, S., & Marwala, T. (2007) · arXiv (Cornell University) · doi:10.48550/arxiv.0704.3474 · Cited by 26
Multi-scale deep tensor factorization learns a latent representation of the human epigenomePreprint
Schreiber, J. M., Durham, T., Bilmes, J. A., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/364976 · Cited by 25
FISH: fast and accurate diploid genotype imputation via segmental hidden Markov model
Zhang, L., Pei, Y., Fu, X., Lin, Y., Wang, Y., & Deng, H. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu143 · Cited by 23
Self-citationHu, Z., Zu, S., & Liu, J. S. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa077 · Cited by 17
scIGANs: single-cell RNA-seq imputation using generative adversarial networksPreprint
Xu, Y., Zhang, Z., You, L., Liu, J., Fan, Z., & Zhou, X. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.20.913384 · Cited by 12
Yeung, K.-F., Yang, Y., Yang, C., & Liu, J. (2019) · Bioinformatics and Biology Insights · doi:10.1177/1177932219881435 · Cited by 5
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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
A roadmap for multi-omics data integration using deep learning
Kang, M., Ko, E., & Mersha, T. B. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab454 · Cited by 389
Xu, Y., Zhang, X., Li, H., Zheng, H., Zhang, J., Olsen, M. S., Varshney, R. K., Prasanna, B. M., & Qian, Q. (2022) · Molecular Plant · doi:10.1016/j.molp.2022.09.001 · Cited by 399
Artificial intelligence assists precision medicine in cancer treatment
Liao, J., Li, X., Gan, Y., Han, S., Rong, P., Wang, W., Li, W., & Zhou, L. P. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2022.998222 · Cited by 280
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 274
Metadata
- Authors
- Song, M., Greenbaum, J., Luttrell, J., Zhou, W., Wu, C., Shen, H., Gong, P., Zhang, C., & Deng, H.-W.
- Affiliations
- University of Southern Mississippi (US)
- Tulane University (US)
- Michigan Technological University (US)
- Florida State University (US)
- United States Army (US)
- Corresponding author
- Zhang, Chaoyang
- Deng, Hong-Wen
- ORCID
- Venue
- Frontiers in Genetics
- Publisher
- Frontiers Media SA
- Published
- 2020-10-15
- Volume
- 11
- Pages
- 570255-570255
- ISSN
- 1664-8021 (ISSN-L)
- PMC
- PMC7594632
- OpenAlex
- W3100788068
- Cited by
- 127
- References
- 103
- Citations by year
- 2026: 11 · 2025: 30 · 2024: 29 · 2023: 25 · 2022: 21 · 2021: 11
- FWCI
- 1.36
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Genetics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Commons - Michigan Tech (Michigan Technological University) (submittedVersion, cc-by)
- Aquila Digital Community (University of Southern Mississippi) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (P20GM109036, R01AR069055, U19AG055373, R01AG061917, AR-27065, M01 RR00585)
- Engineer Research and Development Center (W912HZ20P0023)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- multi-omics data, missing data imputation, deep learning imputation, data integration, precision medicine, complex diseases
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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