Random Forests

Breiman, Leo·2001·Machine Learning

Cited by (1,833)

Items 501–600 of 1,833

  1. A survey on multi‐output regression
    Hanen Borchani, Gherardo Varando, Concha Bielza, Pedro Larrañaga · 2015 · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
  2. Analysis of a Random Forests Model
    Gérard Biau A · 2010 · arXiv (Cornell University)
  3. Ensemble machine learning paradigms in hydrology: A review
    Mohammad Zounemat‐Kermani, Okke Batelaan, Marzieh Fadaee, Reinhard Hinkelmann · 2021 · Journal of Hydrology
  4. Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements
    Tustison, Nicholas J., Cook, Philip A., Klein, Arno, Song, Gang, Das, Sandhitsu R., Duda, Jeffrey T., Kandel, Benjamin M., van Strien, Niels, Stone, James R., Gee, James C. et al. · 2014 · NeuroImage
  5. Comparing methods for detecting multilocus adaptation with multivariate genotype–environment associations
    Brenna R. Forester, Jesse R. Lasky, Helene H. Wagner, Dean L. Urban · 2018 · Molecular Ecology
  6. The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification
    Davide Chicco, Giuseppe Jurman · 2023 · BioData Mining
  7. Oral selinexor-dexamethasone for triple-class refractory multiple myeloma.
    Chari, Ajai, Vogl, Dan T., Gavriatopoulou, Maria, Nooka, Ajay K., Yee, Andrew J., Huff, Carol A., Moreau, Philippe, Dingli, David, Cole, Craig, Lonial, Sagar et al. · 2019 · New England Journal of Medicine
  8. Effects of plastic mulch film residues on wheat rhizosphere and soil properties
    Yueling Qi, Adam Ossowicki, Xiaomei Yang, Esperanza Huerta Lwanga, Francisco Dini‐Andreote, Violette Geissen, Paolina Garbeva · 2019 · Journal of Hazardous Materials
  9. Fifty Years of Classification and Regression Trees
    Wei‐Yin Loh · 2014 · International Statistical Review
  10. New perspectives and methods in link prediction
    Ryan N. Lichtenwalter, Jake T. Lussier, Nitesh V. Chawla · 2010 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
  11. Ensemble approaches for regression
    João Mendes‐Moreira, Carlos Soares, Alí­pio Jorge, Jorge Freire de Sousa · 2012 · ACM Computing Surveys
  12. M5 accuracy competition: Results, findings, and conclusions
    Spyros Makridakis, Evangelos Spiliotis, Vassilios Assimakopoulos · 2022 · International Journal of Forecasting
  13. Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest
    Christian Debes, Andreas Merentitis, Roel Heremans, Jürgen Hahn, Nikolaos Frangiadakis, Tim L. M. van Kasteren, Wenzhi Liao, Rik Bellens, Aleksandra Pižurica, Sidharta Gautama et al. · 2014 · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  14. Predicting into unknown space? Estimating the area of applicability of spatial prediction models
    Meyer, Hanna, Pebesma, Edzer · 2021 · Methods in Ecology and Evolution
  15. A bitter cup: climate change profile of global production of Arabica and Robusta coffee
    Christian Bunn, Peter Läderach, Oriana Ovalle, Dieter Kirschke · 2014 · Climatic Change
  16. Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products
    Pablo M. Granitto, Cesare Furlanello, Franco Biasioli A, Flavia Gasperi · 2006 · Chemometrics and Intelligent Laboratory Systems
  17. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years
    Jingfeng Xiao, Frédéric Chevallier, Cécile Gomez A, Luis Guanter, Jeffrey A. Hicke, ALFREDO R. HUETE, Kazuhito Ichii, Wenjian Ni, Yong Pang, Abdullah Faiz Rahman et al. · 2019 · Remote Sensing of Environment
  18. Mapping high resolution National Soil Information Grids of China
    Feng Liu, Huayong Wu, Yu-Guo Zhao, Chao Li, Jinling Yang, Xiaodong Song, Zhou Shi, A‐Xing Zhu, Gan‐Lin Zhang · 2021 · 中国科学通报:英文版
  19. The protein corona from nanomedicine to environmental science
    Morteza Mahmoudi, Markita Patricia Landry, Anna V. Moore, Roxana Coreas · 2023 · Nature Reviews Materials
  20. Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights
    Pasolli, Edoardo, Truong, Duy Tin, Malik, Faizan, Waldron, Levi, Segata, Nicola · 2016 · PLOS Computational Biology
  21. Deforestation and Reforestation of L atin A merica and the C aribbean (2001–2010)
    Thomas Mitchell Aide, Matthew L. Clark, H. Ricardo Grau, David López‐Carr, Marc A. Levy, Daniel Redo, Martha Bonilla‐Moheno, George Riner, María José Andrade‐Núñez, María Muñiz · 2012 · Biotropica
  22. A Review on Random Forest: An Ensemble Classifier
    Aakash Parmar, Rakesh Katariya, Vatsal Patel · 2018 · Lecture notes on data engineering and communications technologies
  23. Machine learning and its applications to biology
    Tarca, Adi L, Carey, Vincent J, Chen, Xue-wen, Romero, Roberto, Drăghici, Sorin · 2007 · PLoS Computational Biology
  24. Effective Heart Disease Prediction Using Machine Learning Techniques
    Chintan Bhatt, P. V. Patel, Tarang Ghetia, Pier Luigi Mazzeo · 2023 · Algorithms
  25. Assessing transferability of ecological models: an underappreciated aspect of statistical validation
    Seth J. Wenger, Julian D. Olden · 2012 · Methods in Ecology and Evolution
  26. Multi-omic machine learning predictor of breast cancer therapy response
    Sammut, Stephen-John, Crispin-Ortuzar, Mireia, Chin, Suet-Feung, Provenzano, Elena, Bardwell, Helen A., Ma, Wenxin, Cope, Wei, Dariush, Ali, Dawson, Sarah-Jane, Abraham, Jean E. et al. · 2022 · Nature
  27. A novel hybrid artificial intelligence approach for flood susceptibility assessment
    Kamran Chapi, Vijay P. Singh, Ataollah Shirzadi, Himan Shahabi, Dieu Tien Bui, Binh Thai Pham, Khabat Khosravi · 2017 · Environmental Modelling & Software
  28. Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
    Faber, Felix A., Hutchison, Luke, Huang, Bing, Gilmer, Justin, Schoenholz, Samuel S., Dahl, George E., Vinyals, Oriol, Kearnes, Steven, Riley, Patrick F., von Lilienfeld, O. Anatole · 2017 · Journal of Chemical Theory and Computation
  29. ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images
    Paul A. Yushkevich, Yang Gao, Guido Gerig · 2016 · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  30. DeepSynergy: predicting anti-cancer drug synergy with Deep Learning
    Kristina Preuer, Richard P I Lewis, Sepp Hochreiter, Andreas Bender, Krishna C. Bulusu, Günter Klambauer · 2017 · Bioinformatics
  31. A molecular portrait of microsatellite instability across multiple cancers
    Isidro Cortés‐Ciriano, Sejoon Lee, Woong‐Yang Park, Tae‐Min Kim, Peter J. Park · 2017 · Nature Communications
  32. The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support
    Saman Razavi, Anthony J. Jakeman, Andrea Saltelli, Clémentine Prieur, Bertrand Iooss, Emanuele Borgonovo, Elmar Plischke, Samuele Lo Piano, Takuya Iwanaga, William Becker et al. · 2020 · Environmental Modelling & Software
  33. Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review
    Alessia Sarica, Antonio Cerasa, Aldo Quattrone · 2017 · Frontiers in Aging Neuroscience
  34. 21st century climate change threatens mountain flora unequally across Europe
    Robin Engler, Christophe F. Randin, Wilfried Thuiller, Stefan Dullinger, Niklaus E. Zimmermann, Miguel B. Araújo, Peter B. Pearman, Gwenaëlle Le Lay, Christian Piedallu, Cécile Hélène Albert et al. · 2010 · Global Change Biology
  35. Optimal classification trees
    Dimitris Bertsimas, Jack Dunn · 2017 · Machine Learning
  36. Discovery and Validation of a Prostate Cancer Genomic Classifier that Predicts Early Metastasis Following Radical Prostatectomy
    Nicholas Erho, Anamaria Crisan, Ismael A. Vergara, Anirban Pradip Mitra, Mercedeh Ghadessi, Christine Buerki, Eric J. Bergstralh, Thomas M. Kollmeyer, Stephanie R. Fink, Zaid Haddad et al. · 2013 · PLoS ONE
  37. Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees
    Muhammad Waseem Ahmad, Jonathan F. Reynolds, Yacine Rezgui · 2018 · Journal of Cleaner Production
  38. Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data
    P. Schratz, Jannes Muenchow, E. Iturritxa, J. Richter, A. Brenning · 2018 · Ecological Modelling
  39. Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data
    Abdulhakim M. Abdi · 2019 · GIScience & Remote Sensing
  40. Machine Learning–Based Model for Prediction of Outcomes in Acute Stroke
    JoonNyung Heo, Jihoon G. Yoon, Hyungjong Park, Young Dae Kim, Hyo Suk Nam, Ji Hoe Heo · 2019 · Stroke
  41. Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms
    Lago, Jesus, De Ridder, Fjo, De Schutter, Bart · 2018 · Applied Energy
  42. Estimating PM 2.5 Concentrations in the Conterminous United States Using the Random Forest Approach
    Xuefei Hu, Jessica H. Belle, Xia Meng, Avani Wildani, Lance Allyn Waller, Matthew J. Strickland, Yang Liu · 2017 · Environmental Science & Technology
  43. Degrading permafrost puts Arctic infrastructure at risk by mid-century
    Jan Hjort, Olli Karjalainen, Juha Aalto, Sebastian Westermann, V. E. Romanovsky, Frederick E. Nelson, Bernd Etzelmüller, Miska Luoto · 2018 · Nature Communications
  44. Random Forest based hourly building energy prediction
    Zeyu Wang, Yueren Wang, Ruochen Zeng, Ravi Shankar Srinivasan, Sherry Boland Ahrentzen · 2018 · Energy and Buildings
  45. A survey on fake news and rumour detection techniques
    Bondielli, Alessandro, Marcelloni, Francesco · 2019 · Information Sciences
  46. Learning from positive and unlabeled data: a survey
    Bekker, Jessa, Davis, Jesse · 2020 · Machine Learning
  47. EMG feature evaluation for improving myoelectric pattern recognition robustness
    Angkoon Phinyomark, Franck Quaine, Sylvie Charbonnier, Christine Servière, Franck Tarpin-Bernard, Yann Laurillau · 2013 · Expert Systems with Applications
  48. A Survey on Malware Detection Using Data Mining Techniques
    Yanfang Ye, Tao Li, Donald Adjeroh, S. S. Iyengar · 2017 · ACM Computing Surveys
  49. Mapping carbon accumulation potential from global natural forest regrowth
    Susan C. Cook‐Patton, Sara M. Leavitt, David A. Gibbs, Nancy L. Harris, Kristine Lister, Kristina J. Anderson‐Teixeira, Russell D. Briggs, Robin L. Chazdon, Thomas Ward Crowther, Peter Woods Ellis et al. · 2020 · Nature
  50. Soil microbial diversity–biomass relationships are driven by soil carbon content across global biomes
    Felipe Bastida, David John Eldridge, Carlos Prospero García, G. Kenny Png, Richard D. Bardgett, Manuel Delgado‐Baquerizo · 2021 · The ISME Journal
  51. Learning to detect phishing emails
    Ian Fette, Norman Sadeh, Anthony Tomasic · 2007 · International World Wide Web Conference (WWW)
  52. OPERA models for predicting physicochemical properties and environmental fate endpoints
    Kamel Mansouri, Chris Grulke, Richard Judson, Antony John Williams · 2018 · Journal of Cheminformatics
  53. Organ aging signatures in the plasma proteome track health and disease
    Oh, Hamilton Se-Hwee, Rutledge, Jarod, Nachun, Daniel, Pálovics, Róbert, Abiose, Olamide, Moran-Losada, Patricia, Channappa, Divya, Urey, Deniz Yagmur, Kim, Kate, Sung, Yun Ju et al. · 2023 · Nature
  54. Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg?
    Chu, Zi, Gianvecchio, Steven, Wang, Haining, Jajodia, Sushil · 2012 · IEEE Transactions on Dependable and Secure Computing
  55. Fast and accurate image upscaling with super-resolution forests
    Samuel Schulter, Christian Leistner, Horst Bischof · 2015 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  56. A time series forest for classification and feature extraction
    Houtao Deng, George C. Runger, Eugene Tuv, Martyanov Vladimir · 2013 · Information Sciences
  57. Survey of machine learning techniques for malware analysis
    Daniele Ucci, Leonardo Aniello, Roberto Baldoni · 2018 · Computers & Security
  58. Anomaly detection in time series
    Sebastian Schmidl, Phillip Wenig, Thorsten Papenbrock · 2022 · Proceedings of the VLDB Endowment
  59. Ensemble of keyword extraction methods and classifiers in text classification
    Onan, Aytuğ, Korukoğlu, Serdar, Bulut, Hasan · 2016 · Expert Systems with Applications
  60. Virality Prediction and Community Structure in Social Networks
    Li-Lian Weng, F. Menczer, Yong-Yeol Ahn · 2013 · Scientific Reports
  61. Microbial regulation of the soil carbon cycle: evidence from gene–enzyme relationships
    Pankaj Trivedi, Manuel Delgado‐Baquerizo, Chanda Trivedi, Hang‐Wei Hu, Ian C. Anderson, Thomas C. Jeffries, Jizhong Z. Zhou, Brajesh Kumar Singh · 2016 · The ISME Journal
  62. Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas
    Charlotte Pelletier, Silvia Valero, Jordi Inglada, Nicolas Champion, Gérard Dedieu · 2016 · Remote Sensing of Environment
  63. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone
    Davide Chicco, Giuseppe Jurman · 2020 · BMC Medical Informatics and Decision Making
  64. Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers
    Md. Kamrul Hasan, Md. Ashraful Alam, Dola Das, Eklas Hossain, Mahmudul Hasan · 2020 · IEEE Access
  65. An exploratory study of the pull-based software development model
    Gousios, Georgios, Pinzger, Martin, Deursen, Arie van · 2014 · Proceedings of the 36th International Conference on Software Engineering
  66. Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants
    Ahmed M. Alaa, Thomas R Bolton, Emanuele Di Angelantonio, James H F Rudd, Mihaela van der Schaar · 2019 · PLoS ONE
  67. Cuffless Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring
    Kachuee, Mohammad, Kiani, Mohammad Mahdi, Mohammadzade, Hoda, Shabany, Mahdi · 2017 · IEEE Transactions on Biomedical Engineering
  68. A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation
    Azal Ahmad Khan, Omkar Chaudhari, Rohitash Chandra · 2023 · Expert Systems with Applications
  69. Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing
    Ketan Tang, Jianchao Yang, Jue Wang · 2014 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  70. AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions
    Iqbal Hasan Sarker, Md. Hasan Furhad, Raza Nowrozy · 2021 · SN Computer Science
  71. On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping
    Koreen Millard, Murray C. Richardson · 2015 · Remote Sensing
  72. Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making
    Pete Burnap, Matthew L. Williams · 2015 · Policy & Internet
  73. Combining Machine Learning Systems and Multiple Docking Simulation Packages to Improve Docking Prediction Reliability for Network Pharmacology
    Kun‐Yi Hsin, Samik Ghosh, Hiroaki Kitano · 2013 · PLoS ONE
  74. VSURF: An R Package for Variable Selection Using Random Forests
    Robin Genuer, Jean‐Michel Poggi, Christine Tuleau-Malot · 2015 · The R Journal
  75. Initial soil microbiome composition and functioning predetermine future plant health
    Zhong Wei, Yian Gu, V. Friman, G. Kowalchuk, Yang-Chun Xu, Q. Shen, A. Jousset · 2019 · Science Advances
  76. MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers
    Jaeyong Kang, Zahid Ullah, Jeonghwan Gwak · 2021 · Sensors
  77. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks
    Ángel Cruz-Roa, Ajay N. Basavanhally, Fabio Augusto Gonzalez, Hannah L. Gilmore, Michael D. Feldman, Shridar Ganesan, Natalie N. C. Shih, John Tomaszewski, Anant Madabhushi · 2014 · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE
  78. Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change
    José Alexandre Felizola Diniz‐Filho, Luís Mauricio Bini, Thiago Fernando Rangel, Rafael Aguiar Loyola, Christian Hof, David Nogués‐Bravo, Miguel B. Araújo · 2009 · Ecography
  79. Consistency of random forests
    Scornet, Erwan, Biau, Gérard, Vert, Jean-Philippe · 2015 · The Annals of Statistics
  80. Machine learning in acoustics: Theory and applications
    Bianco, Michael J., Gerstoft, Peter, Traer, James, Ozanich, Emma, Roch, Marie A., Gannot, Sharon, Deledalle, Charles-Alban · 2019 · The Journal of the Acoustical Society of America
  81. Unraveling the drivers of intensifying forest disturbance regimes in Europe
    Rupert Seidl, Mart‐Jan Schelhaas, Manfred Josef Lexer · 2011 · Global Change Biology
  82. Learning a no-reference quality metric for single-image super-resolution
    Chao Ma, Chih‐Yuan Yang, Xiaokang Yang, Ming–Hsuan Yang · 2017 · Computer Vision and Image Understanding
  83. Integration of nanoscale memristor synapses in neuromorphic computing architectures
    Giacomo Indiveri, B. Linares-Barranco, Robert A. Legenstein, George Deligeorgis, Themis Prodromakis · 2013 · Nanotechnology
  84. Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran
    Omid Rahmati, Hamid Reza Pourghasemi, Assefa Mekonnen Melesse · 2015 · CATENA
  85. Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data
    Kangyang Chen, Hexia Chen, Chuanlong Zhou, Yichao Huang, Xiangyang Qi, Ruqin Shen, Fengrui Liu, Min Zuo, Xinyi Zou, Jinfeng Wang et al. · 2019 · Water Research
  86. Twenty Years of Mixture of Experts
    Yuksel, S. E., Wilson, J. N., Gader, P. D. · 2012 · IEEE Transactions on Neural Networks and Learning Systems
  87. Using recursive feature elimination in random forest to account for correlated variables in high dimensional data
    Burcu F. Darst, Kristen M. C. Malecki, Corinne D. Engelman · 2018 · BMC Genetics
  88. Evaluating Complexity, Code Churn, and Developer Activity Metrics as Indicators of Software Vulnerabilities
    Yonghee Shin, Andrew Meneely, Laurie A. Williams, Jason A. Osborne · 2010 · IEEE Transactions on Software Engineering
  89. Classification and Regression Trees, Bagging, and Boosting
    Clifton D. Sutton · 2005 · Handbook of statistics
  90. Evaluating time series forecasting models: an empirical study on performance estimation methods
    Vítor Cerqueira, Luı́s Torgo, Igor Mozetič · 2020 · Machine Learning
  91. Advancing Our Understanding of the Human Microbiome Using QIIME
    Jose Antonio Navas-Molina, Juan Manuel Peralta‐Sánchez, Antonio González, Paul J. McMurdie, Yoshiki Vázquez‐Baeza, Zhenjiang Zech Xu, Luke K. Ursell, Christian L. Lauber, Hongwei Zhou, Se Jin Song et al. · 2013 · Methods in enzymology on CD-ROM/Methods in enzymology
  92. Machine learning and AI in marketing–Connecting computing power to human insights
    Ma, Liye, Sun, Baohong · 2020 · International Journal of Research in Marketing
  93. Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation
    Hanna Meyer, Christoph Reudenbach, Tomislav Hengl A, Marwan Katurji, Thomas Nauß · 2017 · Environmental Modelling & Software
  94. A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring
    Naomi Zimmerman, Albert A. Presto, Sriniwasa P. N. Kumar, Jason J. Gu, Aliaksei Hauryliuk, Ellis Shipley Robinson, Allen L. Robinson, R. Subramanian · 2018 · Atmospheric measurement techniques
  95. A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information
    Gongbo Chen, Shanshan Li, Luke D. Knibbs, Nicholas Hamm, Wei Cao, Tiantian Li, Jianping Guo, Hongyan Ren, Michael John Abramson, Yuming Guo · 2018 · The Science of The Total Environment
  96. Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images
    Saurabh Gupta, Pablo Arbeláez, Jitendra Malik · 2013 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  97. A Comparative Analysis of Logistic Regression, Random Forest and KNN Models for the Text Classification
    Kanish Shah, Henil Patel, Devanshi Sanghvi, Manan Shah · 2020 · Augmented Human Research
  98. Dispersal will limit ability of mammals to track climate change in the Western Hemisphere
    Carrie A. Schloss, Tristan A. Nuñez, Joshua J. Lawler · 2012 · Proceedings of the National Academy of Sciences
  99. Evaluating multiple classifiers for stock price direction prediction
    Ballings, Michel, Van den Poel, Dirk, Hespeels, Nathalie, Gryp, Ruben · 2015 · Expert Systems with Applications
  100. Spatiotemporal Developmental Trajectories in the Arabidopsis Root Revealed Using High-Throughput Single Cell RNA Sequencing
    Tom Denyer, Xiaoli Ma, Simon Klesen, Emanuele Scacchi, Kay Nieselt, Marja C.P. Timmermans · 2018 · Developmental Cell