The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
MissForest—non-parametric missing value imputation for mixed-type data
Open accessAbstract
MOTIVATION: Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data, the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a non-parametric method which can cope with different types of variables simultaneously. RESULTS: We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest, we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple datasets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in datasets including different types of variables. In our comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data. AVAILABILITY: The package missForest is freely available from http://stat.ethz.ch/CRAN/. CONTACT: stekhoven@stat.math.ethz.ch; buhlmann@stat.math.ethz.ch
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Depending on the cause, the failure must be addressed in different ways, such as manual recalibration or data imputation [71,72].”
From Case-Based Data Quality Management for IoT Logs: A Case Study Focusing on Detection of Data Quality Issues (2025) · cites this paper as [71]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
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
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,385
Analysis of Incomplete Multivariate Data
Schafer, J. L. (1997) · doi:10.1201/9781439821862 · Cited by 6,012
Analysis of Incomplete Multivariate Data
Booth, D. E., & Schafer, J. L. (2000) · Technometrics · doi:10.2307/1271466 · Cited by 5,627
Statistical Analysis With Missing Data
Ghosh, S., Little, R. J. A., & Rubin, D. B. (1988) · Technometrics · doi:10.2307/1269814 · Cited by 4,730
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
Multiple imputation of discrete and continuous data by fully conditional specification
van Buuren, S. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206074463 · Cited by 2,827
A multivariate technique for multiply imputing missing values using a sequence of regression models
Raghunathan, T. E., Lepkowski, J. M., Hoewyk, J. V., & Solenberger, P. W. (2001) · Survey methodology · Cited by 1,981
Flagged first, then by citations
Research works printed but not linked: 2
- Rubin, 1978, Multiple imputations in sample surveys-a phenomenological Bayesian approach to nonresponse, 20
- Rubin, 1990, Efficiently creating multiple imputations for incomplete multivariate normal data, 83
Cited by
Sorted by citations
Items 701–800 of 1,024
Fuzzy rule-based oversampling technique for imbalanced and incomplete data learning
Liu, G., Yang, Y., & Li, B. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.05.044 · Cited by 38
Gougherty, A. V., & Gougherty, S. W. (2018) · New Phytologist · doi:10.1111/nph.15270 · Cited by 38
Biccler, J. L., Eloranta, S., Brown, P. D. N., Frederiksen, H., Jerkeman, M., Jørgensen, J. M., Jakobsen, L. H., Smedby, K. E. E., Bøgsted, M., & El‐Galaly, T. C. (2018) · JCO Clinical Cancer Informatics · doi:10.1200/cci.18.00025 · Cited by 39
Predicting Acute Pain After Surgery
Baca, Q. J., Marti, F., Poblete, B., Gaudilliere, B. L., Aghaeepour, N., & Angst, M. S. (2019) · Annals of Surgery · doi:10.1097/sla.0000000000003400 · Cited by 38
Hourly solar irradiance prediction using deep BiLSTM network
Li, C., Zhang, Y., Zhao, G., & Ren, Y. (2020) · Earth Science Informatics · doi:10.1007/s12145-020-00511-3 · Cited by 38
Machine Learning Prediction of SARS-CoV-2 Polymerase Chain Reaction Results with Routine Blood Tests
Tschoellitsch, T., Dünser, M. W., Böck, C., Schwarzbauer, K., & Meier, J. M. (2020) · Laboratory Medicine · doi:10.1093/labmed/lmaa111 · Cited by 38
Construction and validation of a game-based intelligence assessment in minecraft
Peters, H., Kyngdon, A., & Stillwell, D. J. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106701 · Cited by 46
Massafra, R., Latorre, A. C., Fanizzi, A., Bellotti, R., Didonna, V., Giotta, F., Forgia, D. L., Nardone, A., Pastena, M. I., Ressa, C. M., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.576007 · Cited by 38
Kortesniemi, M. K., Slupsky, C. M., Aatsinki, A., Sinkkonen, J., Karlsson, L., Linderborg, K. M., Yang, B., Karlsson, H., & Kailanto, H.-M. (2021) · Food Chemistry · doi:10.1016/j.foodchem.2021.129628 · Cited by 39
Impact of hepatopulmonary syndrome in liver transplantation candidates and the role of angiogenesis
Kawut, S. M., Krowka, M. J., Forde, K. A., Al‐Naamani, N., Krok, K. L., Patel, M. J., Bartoli, C. R., Doyle, M. F., Moutchia, J., Lin, G., et al. (2021) · European Respiratory Journal · doi:10.1183/13993003.02304-2021 · Cited by 38
Segar, M. W., Patel, K. V., Hellkamp, A. S., Vaduganathan, M., Lokhnygina, Y. V., Green, J. B., Wan, S., Kolkailah, A. A., Holman, R. R., Peterson, E. D., et al. (2022) · Journal of the American Heart Association · doi:10.1161/jaha.121.024094 · Cited by 38
Landes, U., Hochstadt, A., Manevich, L., Webb, J. G., Sathananthan, J., Sievert, H., Piayda, K., Leon, M. B., Nazif, T. M., Blusztein, D. I., et al. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad146 · Cited by 38
Patel, K. V., Segar, M. W., Klonoff, D. C., Khan, M. S., Usman, M. S., Lam, C. S. P., Verma, S., DeFilippis, A. P., Nasir, K., Bakker, S. J. L., et al. (2024) · Circulation · doi:10.1161/circulationaha.123.067530 · Cited by 38
Predicting early dropout in online versus face-to-face guided self-help: A machine learning approach
Gonzalez Salas Duhne, P., Delgadillo, J., & Lutz, W. (2022) · Behaviour Research and Therapy · doi:10.1016/j.brat.2022.104200 · Cited by 38
Truchot, A., Raynaud, M., Kamar, N., Naesens, M., Legendre, C. M., Delahousse, M., Thaunat, O., Büchler, M., Crespo, M., Linhares, K., et al. (2022) · Kidney International · doi:10.1016/j.kint.2022.12.011 · Cited by 44
Keefe, J. R., Rodriguez-Seijas, C. A., Jackson, S. D., Bränström, R., Harkness, A., Safren, S. A., Hatzenbuehler, M. L., & Pachankis, J. E. (2023) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000799 · Cited by 40
Covariation among reproductive traits in flowering plants shapes their interactions with pollinators
Lanuza, J. B., Rader, R., Stavert, J. R., Kendall, L., Saunders, M. E., & Bartomeus, Í. (2023) · Functional Ecology · doi:10.1111/1365-2435.14340 · Cited by 37
Forrest, I. S., Petrazzini, B. O., Duffy, Á., Park, J. K., O’Neal, A. J., Jordan, D. M., Rocheleau, G., Nadkarni, G. N., Cho, J. H., Blazer, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37996-7 · Cited by 39
Multi-omic prediction of incident type 2 diabetes
Carrasco-Zanini, J., Pietzner, M., Wheeler, E., Kerrison, N. D., Langenberg, C., & Wareham, N. J. (2023) · Diabetologia · doi:10.1007/s00125-023-06027-x · Cited by 38
Lokumcu, T., Iskar, M., Schneider, M., Helm, D., Klinke, G., Schlicker, L., Bethke, F., Müller, G., Richter, K., Poschet, G., et al. (2024) · ACS Nano · doi:10.1021/acsnano.3c11427 · Cited by 39
Major arrhythmias in non-dilated left ventricular cardiomyopathy: a novel prediction score
Peretto, G., Merlo, M., Ambrosi, A., Bacigalupi, E., Villatore, A., Molinari, L., Anguera, I., Claver, E., Ferro, M. D., Suwalski, P., et al. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf477 · Cited by 39
Štiglic, G., Kocbek, P., Fijačko, N., Sheikh, A., & Pajnkihar, M. (2017) · Health Informatics Journal · doi:10.1177/1460458217733288 · Cited by 37
Benavente, E. D., Campos, M., Phelan, J. E., Nolder, D., Dombrowski, J. G., Marinho, C. R. F., Sriprawat, K., Taylor, A. R., Watson, J. A., Roper, C., et al. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008576 · Cited by 35
Jiang, X., Nelson, A. E., Cleveland, R. J., Beavers, D. P., Schwartz, T. A., Arbeeva, L. S., Álvarez, C., Callahan, L. F., Messier, S. P., Loeser, R. F., et al. (2020) · Arthritis Care & Research · doi:10.1002/acr.24179 · Cited by 37
Yin, C., Yang, F., Wang, J., & Ye, Y. (2020) · Remote Sensing · doi:10.3390/rs12071174 · Cited by 37
Gomes, G., Seman, L. O., & Carmona, L. J. D. M. (2020) · Technology Analysis and Strategic Management · doi:10.1080/09537325.2020.1814953 · Cited by 43
Metelmann, S., Pattni, K., Brierley, L., Cavalerie, L., Caminade, C., Blagrove, M. S. C., Turner, J., Sharkey, K. J., & Baylis, M. (2021) · One Health · doi:10.1016/j.onehlt.2021.100221 · Cited by 37
Machine learning–based prediction of health outcomes in pediatric organ transplantation recipients
Killian, M., Payrovnaziri, S. N., Gupta, D., Desai, D. M., & He, Z. (2021) · JAMIA Open · doi:10.1093/jamiaopen/ooab008 · Cited by 37
The individual, place, and wellbeing – a network analysis
McElroy, E., Ashton, M., Bagnall, A., Comerford, T., McKeown, M., Patalay, P., Pennington, A., South, J., Wilson, T. J., & Corcoran, R. (2021) · BMC Public Health · doi:10.1186/s12889-021-11553-7 · Cited by 39
Cho, E., Chang, T., & Hwang, G. (2022) · Electronics · doi:10.3390/electronics11030477 · Cited by 37
FedTMI: Knowledge aided federated transfer learning for industrial missing data imputation
Yao, Z., & Zhao, C. (2022) · Journal of Process Control · doi:10.1016/j.jprocont.2022.08.004 · Cited by 37
Bayón, Á., Godoy, Ó., Maurel, N., van Kleunen, M., & Vilà, M. (2021) · Urban forestry & urban greening · doi:10.1016/j.ufug.2021.127215 · Cited by 37
Cervilla, Ó., & Sierra, J. C. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.903361 · Cited by 36
Wang, C., Sun, M. J., Shao, C., Schlicker, L., Zhuo, Y., Harim, Y., Peng, T., Tian, W., Stöffler, N., Schneider, M., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00500-5 · Cited by 40
An osteoarthritis pathophysiological continuum revealed by molecular biomarkers
Kraus, V. B., Sun, S., Reed, A., Soderblom, E. J., Moseley, M. A., Zhou, K., Jain, V., Arden, N., & Li, Y. (2024) · Science Advances · doi:10.1126/sciadv.adj6814 · Cited by 36
Sun, T., Yang, J., Lei, F., Huang, X., Liu, W., Zhang, X., Lin, L., Sun, L., Xie, X., Zhang, X., et al. (2024) · Cardiovascular Diabetology · doi:10.1186/s12933-024-02333-9 · Cited by 36
Reinhardt, J. D., Post, M. W. M., Fekete, C., Trezzini, B., Brinkhof, M. W. G., & on behalf of SwiSCI Study Group (2016) · PLoS ONE · doi:10.1371/journal.pone.0166955 · Cited by 36
Andrews, S. J., Eramudugolla, R., Velez, J. I., Cherbuin, N., Easteal, S., & Anstey, K. J. (2017) · Alzheimer s Research & Therapy · doi:10.1186/s13195-017-0240-3 · Cited by 37
Maternal pre- and postnatal anxiety symptoms and infant attention disengagement from emotional faces
Kataja, E.-L., Karlsson, L., Parsons, C. E., Pelto, J., Pesonen, H., Häikiö, T., Hyönä, J., Nolvi, S., Korja, R., & Karlsson, H. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.09.064 · Cited by 36
Nagler, M., Nägele, T., Gilli, C., Fragner, L., Korte, A., Platzer, A., Farlow, A., Nordborg, M., & Weckwerth, W. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.01556 · Cited by 35
Improving and Maintaining Winter Hardiness and Frost Tolerance in Bread Wheat by Genomic Selection
Michel, S., Löschenberger, F., Hellinger, J., Strasser, V., Ametz, C., Pachler, B., Sparry, E., & Buerstmayr, H. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2019.01195 · Cited by 35
Discharge Disposition After Anterior Cervical Discectomy and Fusion
Karhade, A. V., Ogink, P. T., Thio, Q. C. B. S., Cha, T. D., Hershman, S. H., Schoenfeld, A. J., Bono, C. M., & Schwab, J. H. (2019) · World Neurosurgery · doi:10.1016/j.wneu.2019.09.026 · Cited by 36
Elbasyoni, I. S., El-Orabey, W. M., Morsy, S. M., Baenziger, P. S., Ajlouni, Z. A., & Dowikat, I. (2019) · PLoS ONE · doi:10.1371/journal.pone.0222755 · Cited by 36
Webb‐Robertson, B., Bramer, L., Stanfill, B., Akers, S. M., Nakayasu, E. S., Metz, T., Frohnert, B. I., Norris, J. M., Johnson, R. K., Rich, S. S., et al. (2020) · Journal of Diabetes · doi:10.1111/1753-0407.13093 · Cited by 32
Walnut Consumption, Plasma Metabolomics, and Risk of Type 2 Diabetes and Cardiovascular Disease
Guasch‐Ferré, M., Hernández‐Alonso, P., Drouin‐Chartier, J., Ruiz‐Canela, M., Razquín, C., Toledo, E., Li, J., Dennis, C., Wittenbecher, C., Corella, D., et al. (2020) · Journal of Nutrition · doi:10.1093/jn/nxaa374 · Cited by 35
Cox, N. J., Bowyer, R. C. E., Lochlainn, M. N., Wells, P. M., Roberts, H. C., & Steves, C. J. (2021) · Journal of Cachexia Sarcopenia and Muscle · doi:10.1002/jcsm.12683 · Cited by 37
Water vapor adsorption by dry soils: A potential link between the water and carbon cycles
López-Canfín, C., Lázaro, R., & Sánchez‐Cañete, E. P. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.153746 · Cited by 36
Yen, H., Ogink, P. T., Huang, C., Groot, O. Q., Su, C.-C., Chen, S.-F., Chen, C., Karhade, A. V., Peng, K.-P., Lin, W., et al. (2022) · The Spine Journal · doi:10.1016/j.spinee.2022.02.009 · Cited by 37
Novel Methods for Imputing Missing Values in Water Level Monitoring Data
Khampuengson, T., & Wang, W. (2023) · Water Resources Management · doi:10.1007/s11269-022-03408-6 · Cited by 36
Estimation of the incidence of animal rabies in Punjab, India
Gill, G. S., Singh, B. B., Dhand, N. K., Aulakh, R. S., Sandhu, B. S., Ward, M. P., & Brookes, V. (2019) · PLoS ONE · doi:10.1371/journal.pone.0222198 · Cited by 36
SAVER: Gene expression recovery for UMI-based single cell RNA sequencingPreprint
Huang, M. C., Wang, J., Torre, E. A., Dueck, H., Shaffer, S., Bonasio, R., Murray, J. I., Raj, A., Li, M., & Zhang, N. R. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/138677 · Cited by 38
Model-assisted estimation through random forests in finite population samplingPreprint
Dagdoug, M., Goga, C., & Haziza, D. (2020) · arXiv (Cornell University) · doi:10.1080/01621459.2021.1987250 · Cited by 36
Fortmeier, V., Lachmann, M., Körber, M. I., Unterhuber, M., von Scheidt, M., Rippen, E., Harmsen, G., Gerçek, M., Friedrichs, K. P., Roder, F., et al. (2022) · JACC: Cardiovascular Interventions · doi:10.1016/j.jcin.2021.12.043 · Cited by 35
CLIMFILL v0.9: a framework for intelligently gap filling Earth observations
Bessenbacher, V., Seneviratne, S. I., & Gudmundsson, L. (2022) · Geoscientific model development · doi:10.5194/gmd-15-4569-2022 · Cited by 35
Augmenting energy time-series for data-efficient imputation of missing values
Liguori, A., Markovic, R., Ferrando, M., Frisch, J., Causone, F., & van Treeck, C. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120701 · Cited by 35
Umar, N., & Gray, A. J. (2023) · Water · doi:10.3390/w15081519 · Cited by 35
MsImpute: Estimation of Missing Peptide Intensity Data in Label-Free Quantitative Mass Spectrometry
Hediyeh-zadeh, S., Webb, A. I., & Davis, M. J. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100558 · Cited by 33
Lyall, L. M., Sangha, N., Zhu, X., Lyall, D. M., Ward, J., Strawbridge, R. J., Cullen, B., & Smıth, D. J. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.04.138 · Cited by 42
Yin, J., Wang, H., Li, S., Zhao, L., You, Y., Yang, J., & Liu, Y. (2023) · BMC Psychiatry · doi:10.1186/s12888-023-05130-y · Cited by 35
Zhou, Y., Tang, Q., & Zhao, G. (2023) · Journal of Hydrology · doi:10.1016/j.jhydrol.2023.130404 · Cited by 35
Ahuvia, I. L., Schleider, J. L., Kneeland, E. T., Moser, J. S., & Schroder, H. S. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.01.229 · Cited by 37
Jiang, S., Xu, W., Xia, Q. X., Yi, M., Zhou, Y., Shang, J., & Cheng, X. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.134309 · Cited by 35
Guo, C., Liu, Z., Fan, H. X., Wang, H., Zhang, X., Zhao, S., Li, Y., Han, X., Wang, T., Chen, X., et al. (2024) · Hepatology · doi:10.1097/hep.0000000000000879 · Cited by 35
Brzenczek, C., Klopfenstein, Q., Hähnel, T., Sapienza, S., Klucken, J., Fröhlich, H., Glaab, E., Acharya, G., Aguayo, G., Alexandre, M., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01236-z · Cited by 36
Ömerovic, E., Råmunddal, T., Pétursson, P., Angerås, O., Rawshani, A., Jha, S., Skoglund, K., Mohammad, M. A., Persson, J., Alfredsson, J., et al. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae700 · Cited by 39
Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics
Xie, R., Seum, T., Sha, S., Trares, K., Holleczek, B., Brenner, H., & Schöttker, B. (2025) · Cardiovascular Diabetology · doi:10.1186/s12933-025-02581-3 · Cited by 35
Myocardial Proteome in Human Heart Failure With Preserved Ejection Fraction
Jani, V., Yoo, E., Binek, A., Guo, A., Kim, J. S., Aguilan, J. T., Keykhaei, M., Jenkin, S. R., Sidoli, S., Sharma, K., et al. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.124.038945 · Cited by 39
Climente-González, H., Oh, M., Chajewska, U., Hosseini, R., Mukherjee, S., Gan, W., Traylor, M., Hu, S., Fatemifar, G., Ghouse, J., et al. (2025) · Communications Medicine · doi:10.1038/s43856-025-00872-0 · Cited by 40
A comparative study on karyotypic diversification rate in mammals
Martínez, P. A., Jacobina, U. P., Fernandes, R. V., Bessa-Brito, C. D., Penone, C., Amado, T. F., Fonseca, C. R., & Bidau, C. J. (2016) · Heredity · doi:10.1038/hdy.2016.110 · Cited by 35
Performance Comparison of Recent Imputation Methods for Classification Tasks over Binary Data
Ghorbani, S., & Desmarais, M. C. (2017) · Applied Artificial Intelligence · doi:10.1080/08839514.2017.1279046 · Cited by 37
Orphanou, K., Dagliati, A., Sacchi, L., Stassopoulou, A., Keravnou, E., & Bellazzi, R. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.03.002 · Cited by 35
Distinct serum biosignatures are associated with different tuberculosis treatment outcomes
Ronacher, K., Chegou, N. N., Kleynhans, L., Siawaya, J. F. D., du Plessis, N., Loxton, A. G., Maasdorp, E., Tromp, G., Kidd, M., Stanley, K., et al. (2019) · Tuberculosis · doi:10.1016/j.tube.2019.101859 · Cited by 36
Decoupled erosion of amphibians’ phylogenetic and functional diversity due to extinction
Oliveira, B. F., Scheffers, B. R., & Costa, G. C. (2019) · Global Ecology and Biogeography · doi:10.1111/geb.13031 · Cited by 33
Lejawa, M., Osadnik, K., Czuba, Z. P., Osadnik, T., & Pawlas, N. (2021) · Life · doi:10.3390/life11121350 · Cited by 35
Blume, M., Schmidt, R., Schmidt, J., Martin, A., & Hilbert, A. (2021) · Neurotherapeutics · doi:10.1007/s13311-021-01149-9 · Cited by 35
Nearest neighbor classifiers over incomplete information
Karlaš, B., Li, P., Wu, R., Gürel, N. M., Chu, X., Wu, W., & Zhang, C. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3430915.3430917 · Cited by 35
Oosterhoff, J. H. F., Savelberg, A. B. M. C., Karhade, A. V., Gravesteijn, B. Y., Doornberg, J. N., Schwab, J. H., & Heng, M. M. (2022) · European Journal of Trauma and Emergency Surgery · doi:10.1007/s00068-022-01981-4 · Cited by 36
Functional diversity metrics can perform well with highly incomplete data sets
Stewart, K., Carmona, C. P., Clements, C. F., Venditti, C., Tobias, J. A., & González‐Suárez, M. (2023) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14202 · Cited by 43
He, S., Zhao, Y., Mette, M. F., Bothe, R., Ebmeyer, E., Sharbel, T. F., Reif, J. C., & Jiang, Y. (2015) · BMC Genomics · doi:10.1186/s12864-015-1366-y · Cited by 35
Sapkota, S., Huan, T., Tran, T., Zheng, J., Camicioli, R. M., Li, L., & Dixon, R. A. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00296 · Cited by 36
Peters, E. M., Dong, L. Y., Thomas, T., Khalaj, S., Balbuena, L., Baetz, M., Osgood, N., & Bowen, R. (2022) · Archives of Suicide Research · doi:10.1080/13811118.2020.1783410 · Cited by 34
Paul, S. E., Winograd, R., & Oltmanns, T. F. (2021) · Journal of Psychopathology and Behavioral Assessment · doi:10.1007/s10862-020-09862-z · Cited by 35
Microclimate relationships of intraspecific trait variation in sub‐Arctic plants
Kemppinen, J., & Niittynen, P. (2022) · Oikos · doi:10.1111/oik.09507 · Cited by 34
Venkatasubramaniam, A., Mateen, B. A., Shields, B. M., Hattersley, A. T., Jones, A. G., Vollmer, S. J., & Dennis, J. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02207-2 · Cited by 40
Prediction of 2-Year Cognitive Outcomes in Very Preterm Infants Using Machine Learning Methods
Bowe, A. K., Lightbody, G., Staines, A. S., Murray, D. M., & Norman, M. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.49111 · Cited by 33
Body Size, Diet Quality, and Epigenetic Aging: Cross-Sectional and Longitudinal Analyses
Li, D. L., Hodge, A. M., Cribb, L., Southey, M. C., Giles, G. G., Milne, R. L., & Dugué, P. (2024) · The Journals of Gerontology Series A · doi:10.1093/gerona/glae026 · Cited by 39
Explaining deep learning models for ozone pollution prediction via embedded feature selection
Jiménez-Navarro, M. J., Martínez-Ballesteros, M., Alvarez, F. M., & Cortés, G. A. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111504 · Cited by 34
Socioeconomic Conditions in Childhood, Adolescence, and Adulthood and the Risk of Ischemic Stroke
Becher, H., Palm, F., Aigner, A., Safer, A., Urbanek, C., Buggle, F., Grond‐Ginsbach, C., & Grau, A. (2015) · Stroke · doi:10.1161/strokeaha.115.011523 · Cited by 34
McCuish, E., Lussier, P., & Rocque, M. (2019) · Journal of Youth and Adolescence · doi:10.1007/s10964-019-01178-w · Cited by 34
Educational assortative mating and time use in the home
Miller, R. N. (2020) · Social Science Research · doi:10.1016/j.ssresearch.2020.102440 · Cited by 34
Kashef, A., Khatibi, T., & Mehrvar, A. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100399 · Cited by 34
Ruiz, E., Ferreño, D., Cuartas, M., López, A., Arroyo, V., & Gutiérrez-Solana, F. (2020) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2020.1803505 · Cited by 34
Zimmermann, D., Wampold, B. E., Rubel, J. A., Schwartz, B., Poster, K., Schilling, V. N. L. S., Deisenhofer, A., Hehlmann, M. I., Penedo, J. M. G., & Lutz, W. (2020) · Psychotherapy Research · doi:10.1080/10503307.2020.1847344 · Cited by 36
Delory, B. M., Schempp, H., Spachmann, S. M., Störzer, L., van Dam, N. M., Temperton, V. M., & Weinhold, A. (2021) · Plant Cell & Environment · doi:10.1111/pce.13999 · Cited by 35
Modulo 9 model-based learning for missing data imputation
Ngueilbaye, A., Wang, H., Ahmat, D., & Junaidu, S. B. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107167 · Cited by 36
Rek, S. V., Bühner, M., Reinhard, M. A., Freeman, D. H., Keeser, D., Adorjan, K., Falkai, P., & Padberg, F. (2021) · BMC Psychiatry · doi:10.1186/s12888-021-03425-6 · Cited by 35
Evolution and dispersal of snakes across the Cretaceous-Paleogene mass extinction
Klein, C. G., Pisani, D., Field, D. J., Lakin, R. J., Wills, M. A., & Longrich, N. R. (2021) · Nature Communications · doi:10.1038/s41467-021-25136-y · Cited by 35
Rosecrans, C. Z., Belitz, K., Ransom, K. M., Stackelberg, P. E., & McMahon, P. B. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.150960 · Cited by 34
Patel, K. V., Segar, M. W., Lavie, C. J., Kondamudi, N., Neeland, I. J., Almandoz, J. P., Martin, C. K., Carbone, S., Butler, J., Powell‐Wiley, T. M., et al. (2021) · Circulation · doi:10.1161/circulationaha.121.055830 · Cited by 34
Efficient and effective data imputation with influence functions
Miao, X., Wu, Y., Lü, C., Gao, Y., Wang, J., & Yin, J. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3494124.3494143 · Cited by 34
Metadata
- Authors
- Stekhoven, D. J., & Bühlmann, P.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Stekhoven, Daniel J.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-10-28
- Volume
- 28
- Issue
- 1
- Pages
- 112-118
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2064186732
- Cited by
- 6,156
- References
- 27
- Citations by year
- 2026: 901 · 2025: 1098 · 2024: 933 · 2023: 794 · 2022: 634 · 2021: 614 · 2020: 397 · 2019: 250 · 2018: 212 · 2017: 114 · 2016: 86 · 2015: 51 · 2014: 39 · 2013: 18 · 2012: 10
- FWCI
- 19.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- arXiv (Cornell University)
- ETH Zürich Research Collection
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- missing data imputation, mixed data, missForest, random forest, multiple imputation, high-throughput data, high-dimensional data, biological data
- MeSH terms
- Algorithms, Data Interpretation, Statistical, Escherichia coli, Humans, Arabidopsis, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
