Random Forests

Cited by (1,834)

Items 201–300 of 1,834

1. Random Forests
   Adele Cutler, D. Richard Cutler, John R. Stevens · 2012 · Springer eBooks
2. AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems
   Sarker, Iqbal H. · 2022 · SN Computer Science
3. Dynamics of the human gut microbiome in inflammatory bowel disease
   Halfvarson, Jonas, Brislawn, Colin J., Lamendella, Regina, Vázquez-Baeza, Yoshiki, Walters, William A., Bramer, Lisa M., D'Amato, Mauro, Bonfiglio, Ferdinando, McDonald, Daniel, Gonzalez, Antonio et al. · 2017 · Nature Microbiology
4. Genomic selection in plant breeding: from theory to practice
   Jean‐Luc Jannink, Aaron Joel Lorenz, Hiroyoshi Iwata · 2010 · Briefings in Functional Genomics
5. Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage
   Hendriksen, Rene S., Munk, Patrick, Njage, Patrick, van Bunnik, Bram, McNally, Luke, Lukjancenko, Oksana, Röder, Timo, Nieuwenhuijse, David, Pedersen, Susanne Karlsmose, Kjeldgaard, Jette et al. · 2019 · Nature Communications
6. TMVA - Toolkit for Multivariate Data Analysis
   Andreas Höcker, P. Speckmayer, J. Stelzer, Therhaag, J., von Toerne, E., H. Voss, Backes, M., T. Carli, Cohen, O., A. Christov et al. · 2007 · arXiv (Cornell University)
7. Reading text in the wild with convolutional neural networks
   Jaderberg, Max, Simonyan, Karen, Vedaldi, Andrea, Zisserman, Andrew · 2016 · International Journal of Computer Vision
8. Get another label? improving data quality and data mining using multiple, noisy labelers
   Victor S. Sheng, Foster John Provost, Panagiotis G. Ipeirotis · 2008 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
9. Deep Neural Networks and Tabular Data: A Survey
   Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, Gjergji Kasneci · 2022 · IEEE Transactions on Neural Networks and Learning Systems
10. Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics
    Shekhar, Karthik, Lapan, Sylvain W., Whitney, Irene E., Tran, Nicholas M., Macosko, Evan Z., Kowalczyk, Monika, Adiconis, Xian, Levin, Joshua Z., Nemesh, James, Goldman, Melissa et al. · 2016 · Cell
11. Data-Driven Science and Engineering
    Steven L. Brunton, J. Nathan Kutz · 2019 · Cambridge University Press eBooks
12. Gut Microbiomes of Malawian Twin Pairs Discordant for Kwashiorkor
    Smith, Michelle I., Yatsunenko, Tanya, Manary, Mark J., Trehan, Indi, Mkakosya, Rajhab, Cheng, Jiye, Kau, Andrew L., Rich, Stephen S., Concannon, Patrick, Mychaleckyj, Josyf C. et al. · 2013 · Science
13. All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
    Fisher, Aaron, Rudin, Cynthia, Dominici, Francesca · 2018 · arXiv
14. Machine Learning Methods That Economists Should Know About
    Athey, Susan, Imbens, Guido W. · 2019 · Annual Review of Economics
15. Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships
    Ma, Junshui, Sheridan, Robert P., Liaw, Andy, Dahl, George E., Svetnik, Vladimir · 2015 · Journal of Chemical Information and Modeling
16. sdm: a reproducible and extensible R platform for species distribution modelling
    Babak Naimi, Miguel B. Araújo · 2016 · Ecography
17. Data Mining and Analysis
    Mohammed Javeed Zaki, Wagner Meira · 2014 · Cambridge University Press eBooks
18. The Musicality of Non-Musicians: An Index for Assessing Musical Sophistication in the General Population
    Müllensiefen, Daniel, Gingras, Bruno, Musil, Jason, Stewart, Lauren · 2014 · PLoS ONE
19. SMOTE for high-dimensional class-imbalanced data
    Rok Blagus, Lara Lusa · 2013 · BMC Bioinformatics
20. Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review
    Mohammadreza Sheykhmousa, Masoud Mahdianpari, Hamid Ghanbari, Fariba Mohammadimanesh, Pedram Ghamisi, Saeid Homayouni · 2020 · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
21. Ilastik: Interactive learning and segmentation toolkit
    Christoph Sommer, Christoph Straehle, Ullrich Köthe, Fred A. Hamprecht · 2011 · IEEE International Symposium on Biomedical Imaging (ISBI)
22. GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery
    Xiao Zhang, Liangyun Liu, Xidong Chen, Yuan Gao, Shuai Xie, Jun Mi · 2021 · Earth system science data
23. Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables
    Tomislav Hengl A, Madlene Nussbaum, Marvin N. Wright, G.B.M. Heuvelink, Benedikt Gräler · 2018 · PeerJ
24. A comprehensive review on ensemble deep learning: Opportunities and challenges
    Mohammed, Ammar, Kora, Rania · 2023 · Journal of King Saud University - Computer and Information Sciences
25. Evidence of climatic niche shift during biological invasion
    Olivier Broennimann, Urs Albert Treier, Heinz Müller‐Schärer, Wilfried Thuiller, A. Townsend Peterson, Antoine Guisan · 2007 · Ecology Letters
26. Gut Microbiome-Based Metagenomic Signature for Non-invasive Detection of Advanced Fibrosis in Human Nonalcoholic Fatty Liver Disease
    Loomba, Rohit, Seguritan, Victor, Li, Weizhong, Long, Tao, Klitgord, Niels, Bhatt, Archana, Dulai, Parambir Singh, Caussy, Cyrielle, Bettencourt, Richele, Highlander, Sarah K. et al. · 2017 · Cell Metabolism
27. Accurate predictions on small data with a tabular foundation model
    Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, Frank Hutter · 2025 · Nature
28. Empirical characterization of random forest variable importance measures
    Kellie J. Archer, Ryan Kimes · 2007 · Computational Statistics & Data Analysis
29. Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges
    Bernd Bischl, Martin Binder, Michel Lang, Tobias Pielok, Jakob Richter, Stefan Coors, Janek Thomas, Theresa Ullmann, Marc Becker, Anne‐Laure Boulesteix et al. · 2023 · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
30. Correlation and variable importance in random forests
    Baptiste Gregorutti, Bertrand Michel, Philippe Saint-Pierre · 2016 · Statistics and Computing
31. A Survey of Dimension Reduction Techniques
    Imola K. Fodor · 2002
32. SRAMP: prediction of mammalian N 6 -methyladenosine (m 6 A) sites based on sequence-derived features
    Yuan Zhou, Pan Zeng, Yanhui Li, Ziding Zhang, Qinghua Cui · 2016 · Nucleic Acids Research
33. A high-bias, low-variance introduction to Machine Learning for physicists
    Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G. R. Day, Clint Richardson, Charles K. Fisher, David J. Schwab · 2019 · Physics Reports
34. Variable Importance Assessment in Regression: Linear Regression versus Random Forest
    Ulrike Grömping · 2009 · The American Statistician
35. Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression
    Jennifer F. Bobb, Birgit Claus Henn, Linda Valeri, Brent A. Coull · 2018 · Environmental Health
36. Global histone modification patterns predict risk of prostate cancer recurrence
    David B. Seligson, Steve Horvath, Tao Shi, Hong‐Jeng Yu, Sheila Tze, Michael R. Grunstein, Siavash K. Kurdistani · 2005 · Nature
37. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change
    Fernando Valladares, Silvia Matesanz, François Guilhaumon, Miguel B. Araújo, Luís Balaguer, Marta Benito Garzón, William K. Cornwell, Ernesto Gianoli, Mark van Kleunen, Daniel E. Naya et al. · 2014 · Ecology Letters
38. Uses and misuses of bioclimatic envelope modeling
    Miguel B. Araújo, A. Townsend Peterson · 2012 · Ecology
39. Global soil pollution by toxic metals threatens agriculture and human health
    Deyi Hou, Xiyue Jia, Liuwei Wang, S. P. McGrath, Yong‐Guan Zhu, Qing Hu, Fangjie J. Zhao, Michael S. Bank, David O’Connor, Jerome O. Nriagu · 2025 · Science
40. DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability
    Rodrigues, Carlos HM, Pires, Douglas EV, Ascher, David B · 2018 · Nucleic Acids Research
41. The global distribution and trajectory of tidal flats
    Nicholas Murray, Stuart Phinn, Michael DeWitt, Renata Ferrari, Renee Johnston, Mitchell Lyons, Nicholas Clinton, David Thau, Richard A. Fuller · 2018 · Nature
42. Machine-learning-guided directed evolution for protein engineering
    Kevin Yang, Zachary Wu, Frances H. Arnold · 2019 · Nature Methods
43. DeepTox: toxicity prediction using deep learning
    Mayr, Andreas, Klambauer, Günter, Unterthiner, Thomas, Hochreiter, Sepp · 2016 · Frontiers in Environmental Science
44. Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data
    Forrest Robert Stevens, Andrea Elizabeth Gaughan, Catherine Linard, Andrew J. Tatem · 2015 · PLoS ONE
45. Ensemble Classifiers for Steganalysis of Digital Media
    Jan Kodovský, Jessica Fridrich, Vojtěch Holub · 2011 · IEEE Transactions on Information Forensics and Security
46. ModBase, a database of annotated comparative protein structure models, and associated resources
    Ursula Pieper, Benjamin Webb, David T. Barkan, Dina Schneidman‐Duhovny, Avner Schlessinger, Hannes Braberg, Z. Yang, Elvin Meng, Eric F. Pettersen, C.C. Huang et al. · 2010 · Nucleic Acids Research
47. Massively parallel single-nucleus RNA-seq with DroNc-seq
    Habib, Naomi, Avraham-Davidi, Inbal, Basu, Anindita, Burks, Tyler, Shekhar, Karthik, Hofree, Matan, Choudhury, Sourav R, Aguet, François, Gelfand, Ellen, Ardlie, Kristin et al. · 2017 · Nature Methods
48. Global predictions of primary soil salinization under changing climate in the 21st century
    Amirhossein Hassani, Adisa Azapagic, Nima Shokri · 2021 · Nature Communications
49. A survey on missing data in machine learning
    Emmanuel, Tlamelo, Maupong, Thabiso, Mpoeleng, Dimane, Semong, Thabo, Mphago, Banyatsang, Tabona, Oteng · 2021 · Journal of Big Data
50. Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans
    Heather Jane Cordell · 2002 · Human Molecular Genetics
51. Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies
    Alexis Diamond, Jasjeet Singh Sekhon · 2012 · The Review of Economics and Statistics
52. Learning To Count Objects in Images
    Victor Lempitsky, Andrew Zisserman · 2010
53. Aging and Environmental Exposures Alter Tissue-Specific DNA Methylation Dependent upon CpG Island Context
    Brock C. Christensen, Eugene Andres Houseman, Carmen J. Marsit, Shichun Zheng, Margaret Wrensch, Joseph Leo Wiemels, Heather H. Nelson, Margaret Rita Karagas, James F. Padbury, Raphael Bueno et al. · 2009 · PLoS Genetics
54. Predicting reaction performance in C–N cross-coupling using machine learning
    Ahneman, Derek T., Estrada, Jesús G., Lin, Shishi, Dreher, Spencer D., Doyle, Abigail G. · 2018 · Science
55. Vision-Language Models for Vision Tasks: A Survey
    Zhang, Jingyi, Huang, Jiaxing, Jin, Sheng, Lu, Shijian · 2024 · IEEE Transactions on Pattern Analysis and Machine Intelligence
56. Radiomics: the facts and the challenges of image analysis
    Stefania Rizzo, Francesca Botta, Sara Raimondi, Daniela Origgi, Cristiana Fanciullo, ALESSIO GIUSEPPE MORGANTI, Massimo Bellomi · 2018 · European Radiology Experimental
57. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
    Xin Yu Yang, Yifei Wang, Ryan Byrne, Gisbert Schneider, Shengyong Yang · 2019 · Chemical Reviews
58. Deep learning to represent subgrid processes in climate models
    Rasp, Stephan, Pritchard, Michael S., Gentine, Pierre · 2018 · Proceedings of the National Academy of Sciences
59. Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds
    E.S. Salmina, Norbert Haider, Igor V. Tetko · 2015 · Molecules
60. Forecasting: theory and practice
    F. Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos, M. Z. Babai, Devon K. Barrow, Souhaib Ben Taieb, C. Bergmeir, R. Bessa, J. Bijak, J. Boylan et al. · 2020 · International Journal of Forecasting
61. Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation
    Thomas, Andrew Maltez, Manghi, Paolo, Asnicar, Francesco, Pasolli, Edoardo, Armanini, Federica, Zolfo, Moreno, Beghini, Francesco, Manara, Serena, Karcher, Nicolai, Pozzi, Chiara et al. · 2019 · Nature Medicine
62. P-N learning: Bootstrapping binary classifiers by structural constraints
    Zdenek Kalal, Jiřı́ Matas, Krystian Mikolajczyk · 2010 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
63. Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption
    Muhammad Waseem Ahmad, Monjur M. Mourshed, Yacine Rezgui · 2017 · Energy and Buildings
64. Semantic texton forests for image categorization and segmentation
    Jamie Shotton, Matthew Johnson, Roberto Cipolla · 2008 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
65. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study
    Chilamkurthy, Sasank, Ghosh, Rohit, Tanamala, Swetha, Biviji, Mustafa, Campeau, Norbert G, Venugopal, Vasantha Kumar, Mahajan, Vidur, Rao, Pooja, Warier, Prashant · 2018 · The Lancet
66. Data mining for credit card fraud: a comparative study
    Bhattacharyya, Siddhartha, Jha, Sanjeev, Tharakunnel, Kurian, Westland, J. Christopher · 2011 · Decision Support Systems
67. Combining SVMs with Various Feature Selection Strategies
    Yiwei Chen, Chih‐Jen Lin · 2008 · Studies in fuzziness and soft computing
68. Machine learning models and bankruptcy prediction
    Flávio Barboza, Herbert Kimura, Edward I. Altman · 2017 · Expert Systems with Applications
69. Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems
    Kim‐Anh Lê Cao, Simon Boitard, Philippe C. Besse · 2011 · BMC Bioinformatics
70. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery
    Dennis C. Duro, Steven E. Franklin, Monique G. Dubé · 2011 · Remote Sensing of Environment
71. Spatial validation reveals poor predictive performance of large-scale ecological mapping models
    Pierre Ploton, Frédéric Mortier, Maxime Réjou‐Méchain, Nicolas Barbier, Nicolas Picard, Vivien Rossi, Carsten F. Dormann, Guillaume Cornu, Gaëlle Viennois, Nicolas Bayol et al. · 2020 · Nature Communications
72. The Soil Microbiome Influences Grapevine-Associated Microbiota
    Iratxe Zarraonaindia, Sarah M. Owens, Pamela B. Weisenhorn, Kristin West, Jarrad Hampton‐Marcell, Simon M. Lax, Nicholas A. Bokulich, David Andrew Mills, Gilles Martin, Safiyh Taghavi et al. · 2015 · mBio
73. Electromyography data for non-invasive naturally-controlled robotic hand prostheses
    Manfredo Atzori, Arjan Gijsberts, Claudio Castellini, Barbara Caputo B, Anne-Gabrielle Mittaz Hager, Simone Elsig, Giorgio Giatsidis, Franco Bassetto, Henning Müller · 2014 · Scientific Data
74. Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View
    Luo, Wei, Phung, Dinh, Tran, Truyen, Gupta, Sunil, Rana, Santu, Karmakar, Chandan, Shilton, Alistair, Yearwood, John, Dimitrova, Nevenka, Ho, Tu Bao et al. · 2016 · Journal of Medical Internet Research
75. A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
    Pedro J. Ballester, John B. O. Mitchell · 2010 · Bioinformatics
76. How Many Trees in a Random Forest?
    Thais Mayumi Oshiro, Pedro Santoro Perez, José Augusto Baranauskas · 2012 · Lecture notes in computer science
77. Letter of intent for KM3NeT 2.0
    Silvia Adrián-Martínez, M. Ageron, F. Aharonian, S. Aiello, A. Albert, F. Ameli, E.G. Anassontzis, Michel L. Andre, Giorgos Androulakis, Marco Anghinolfi et al. · 2016 · Journal of Physics G Nuclear and Particle Physics
78. metaX: a flexible and comprehensive software for processing metabolomics data
    Bo Wen, Zhanlong Mei, Chunwei Zeng, Siqi Liu · 2017 · BMC Bioinformatics
79. Global stocks and capacity of mineral-associated soil organic carbon
    Georgiou, Katerina, Jackson, Robert B., Vindušková, Olga, Abramoff, Rose Z., Ahlström, Anders, Feng, Wenting, Harden, Jennifer W., Pellegrini, Adam F. A., Polley, H. Wayne, Soong, Jennifer L. et al. · 2022 · Nature Communications
80. Machine Learning: New Ideas and Tools in Environmental Science and Engineering
    Shifa Zhong, Kai Zhang, Majid Bagheri, Joel G. Burken, April Z. Gu, Baikun Li, Xingmao Ma, Babetta L. Marrone, Zhiyong Jason Ren, Joshua Schrier et al. · 2021 · Environmental Science & Technology
81. First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe
    Markus Immitzer, Francesco Vuolo, Clement Atzberger · 2016 · Remote Sensing
82. Determinants of community structure in the global plankton interactome
    Gipsi Lima‐Mendez, Karoline Faust, Nicolas Henry, Johan Decelle, Sébastien Colin, Fabrizio Carcillo, Samuel Chaffron, J. Cesar Ignacio‐Espinoza, Simon Roux, Flora Vincent et al. · 2015 · Science
83. Large scale deep learning for computer aided detection of mammographic lesions
    Kooi, Thijs, Litjens, Geert, van Ginneken, Bram, Gubern-Mérida, Albert, Sánchez, Clara I., Mann, Ritse, den Heeten, Ard, Karssemeijer, Nico · 2017 · Medical Image Analysis
84. Fast Edge Detection Using Structured Forests
    Piotr Dollár, C. Lawrence Zitnick · 2014 · IEEE Transactions on Pattern Analysis and Machine Intelligence
85. A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction
    Nicholas Pudjihartono, Tayaza Fadason, Andreas W. Kempa-Liehr, Justin Martin O'Sullivan · 2022 · Frontiers in Bioinformatics
86. Deep Forest: Towards An Alternative to Deep Neural Networks
    Zhi‐Hua Zhou, Ji Feng · 2017 · International Joint Conference on Artificial Intelligence
87. Soli: ubiquitous gesture sensing with millimeter wave radar
    Lien, Jaime, Gillian, Nicholas, Karagozler, M. Emre, Amihood, Patrick, Schwesig, Carsten, Olson, Erik, Raja, Hakim, Poupyrev, Ivan · 2016 · ACM Transactions on Graphics
88. Translational biomarker discovery in clinical metabolomics: an introductory tutorial
    Jianguo Xia, David Broadhurst, Michael Wilson, David Scott Wishart · 2012 · Metabolomics
89. Random forests for genomic data analysis
    Xi Chen, Hemant Ishwaran · 2012 · Genomics
90. Predicting Diabetes Mellitus With Machine Learning Techniques
    Quan Zou, Kaiyang Qu, Yamei Luo, Dehui Yin, Ying Ju, Hua Tang · 2018 · Frontiers in Genetics
91. A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility
    Wei Chen, Xiaoshen Xie, Jiale Wang, Biswajeet Pradhan, Haoyuan Hong, Dieu Tien Bui, Zhao Duan, Jianquan Ma · 2016 · CATENA
92. Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions
    Tomislav Hengl A, G.B.M. Heuvelink, Bas Kempen, J.G.B. Leenaars, Markus Walsh, Keith D. Shepherd, Andrew Sila, R.A. MacMillan, J.S. Mendes de Jesus, Lulseged D. Tamene et al. · 2015 · PLoS ONE
93. Understanding Random Forests: From Theory to Practice
    Gilles Louppe · 2014 · arXiv (Cornell University)
94. Random Forests for Global and Regional Crop Yield Predictions
    Jig Han Jeong, Jonathan P. Resop, Nathaniel D. Mueller, David H. Fleisher, Kyungdahm Yun, Ethan E. Butler, Dennis Timlin, Kyo‐Moon Shim, James Gerber, Vangimalla R. Reddy et al. · 2016 · PLoS ONE
95. Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics
    Anne‐Laure Boulesteix, Silke Janitza, Jochen Kruppa, Inke Regina König · 2012 · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
96. Putting the ‘landscape’ in landscape genetics
    Andrew T. Storfer, Melanie A. Murphy, Jeffrey S. Evans, Caren S. Goldberg, Stacie J. Robinson, Stephen F. Spear, Raymond J. Dezzani, Eric M. Delmelle, Lee Alexandre Vierling, Lisette P. Waits · 2006 · Heredity
97. Distribution-Free Predictive Inference for Regression
    Lei, Jing, G’Sell, Max, Rinaldo, Alessandro, Tibshirani, Ryan J., Wasserman, Larry · 2018 · Journal of the American Statistical Association
98. A review of emotion recognition using physiological signals
    Shu, Lin, Xie, Jinyan, Yang, Mingyue, Li, Ziyi, Li, Zhenqi, Liao, Dan, Xu, Xiangmin, Yang, Xinyi · 2018 · Sensors
99. K DEEP : Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks
    José Jiménez-Luna, Miha Škalič, Gerard Martínez-Rosell, Gianni De Fabritiis · 2018 · Journal of Chemical Information and Modeling
100. Machine learning for internet of things data analysis: a survey
     Mahdavinejad, Mohammad Saeid, Rezvan, Mohammadreza, Barekatain, Mohammadamin, Adibi, Peyman, Barnaghi, Payam, Sheth, Amit P. · 2018 · Digital Communications and Networks