Random Forests

Cited by (1,834)

Items 1,001–1,100 of 1,834

1. Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm
   Dunn, Alexander, Wang, Qi, Ganose, Alex, Dopp, Daniel, Jain, Anubhav · 2020 · npj Computational Materials
2. GWAS identifies 14 loci for device-measured physical activity and sleep duration
   Doherty, Aiden, Smith-Byrne, Karl, Ferreira, Teresa, Holmes, Michael V., Holmes, Chris, Pulit, Sara L., Lindgren, Cecilia M. · 2018 · Nature Communications
3. Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge
   Stowell, Dan, Wood, Michael D., Pamuła, Hanna, Stylianou, Yannis, Glotin, Hervé · 2019 · Methods in Ecology and Evolution
4. Comparing automated text classification methods
   Hartmann, Jochen, Huppertz, Juliana, Schamp, Christina, Heitmann, Mark · 2019 · International Journal of Research in Marketing
5. Relationship between microbiota of the colonic mucosa vs feces and symptoms, colonic transit, and methane production in female patients with chronic constipation
   Parthasarathy, Gopanandan, Chen, Jun, Chen, Xianfeng, Chia, Nicholas, O'Connor, Helen M., Wolf, Patricia G., Gaskins, H. Rex, Bharucha, Adil E. · 2016 · Gastroenterology
6. Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey
   Buhrmester, Vanessa, Münch, David, Arens, Michael · 2021 · Machine Learning and Knowledge Extraction
7. Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing
   Syafrudin, Muhammad, Alfian, Ganjar, Fitriyani, Norma Latif, Rhee, Jongtae · 2018 · Sensors
8. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: A pilot study
   Arora, S., Venkataraman, V., Zhan, A., Donohue, S., Biglan, K.M., Dorsey, E.R., Little, M.A. · 2015 · Parkinsonism & Related Disorders
9. Robust Network Traffic Classification
   Zhang, Jun, Chen, Xiao, Xiang, Yang, Zhou, Wanlei, Wu, Jie · 2015 · IEEE/ACM Transactions on Networking
10. Alterations in Enteric Virome Are Associated With Colorectal Cancer and Survival Outcomes
    Nakatsu, Geicho, Zhou, Haokui, Wu, William Ka Kei, Wong, Sunny Hei, Coker, Olabisi Oluwabukola, Dai, Zhenwei, Li, Xiangchun, Szeto, Chun-Ho, Sugimura, Naoki, Lam, Thomas Yuen-Tung et al. · 2018 · Gastroenterology
11. Machine learning in concrete science: applications, challenges, and best practices
    Li, Zhanzhao, Yoon, Jinyoung, Zhang, Rui, Rajabipour, Farshad, Srubar III, Wil V., Dabo, Ismaila, Radlińska, Aleksandra · 2022 · npj Computational Materials
12. Data mining and machine learning in astronomy
    BALL, NICHOLAS M., BRUNNER, ROBERT J. · 2010 · International Journal of Modern Physics D
13. Incremental on-line learning: A review and comparison of state of the art algorithms
    Losing, Viktor, Hammer, Barbara, Wersing, Heiko · 2018 · Neurocomputing
14. C2H2 zinc finger proteins greatly expand the human regulatory lexicon
    Najafabadi, Hamed S, Mnaimneh, Sanie, Schmitges, Frank W, Garton, Michael, Lam, Kathy N, Yang, Ally, Albu, Mihai, Weirauch, Matthew T, Radovani, Ernest, Kim, Philip M et al. · 2015 · Nature Biotechnology
15. Activity in motor-sensory projections reveals distributed coding in somatosensation
    Petreanu, Leopoldo, Gutnisky, Diego A., Huber, Daniel, Xu, Ning-long, O’Connor, Dan H., Tian, Lin, Looger, Loren, Svoboda, Karel · 2012 · Nature
16. Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases
    Ianiro, Gianluca, Punčochář, Michal, Karcher, Nicolai, Porcari, Serena, Armanini, Federica, Asnicar, Francesco, Beghini, Francesco, Blanco-Míguez, Aitor, Cumbo, Fabio, Manghi, Paolo et al. · 2022 · Nature Medicine
17. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project
    Mansouri, Kamel, Abdelaziz, Ahmed, Rybacka, Aleksandra, Roncaglioni, Alessandra, Tropsha, Alexander, Varnek, Alexandre, Zakharov, Alexey, Worth, Andrew, Richard, Ann M., Grulke, Christopher M. et al. · 2016 · Environmental Health Perspectives
18. A comparison of word embeddings for the biomedical natural language processing
    Wang, Yanshan, Liu, Sijia, Afzal, Naveed, Rastegar-Mojarad, Majid, Wang, Liwei, Shen, Feichen, Kingsbury, Paul, Liu, Hongfang · 2018 · Journal of Biomedical Informatics
19. A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop
    Langlotz, Curtis P., Allen, Bibb, Erickson, Bradley J., Kalpathy-Cramer, Jayashree, Bigelow, Keith, Cook, Tessa S., Flanders, Adam E., Lungren, Matthew P., Mendelson, David S., Rudie, Jeffrey D. et al. · 2019 · Radiology
20. Early detection of type 2 diabetes mellitus using machine learning-based prediction models
    Kopitar, Leon, Kocbek, Primoz, Cilar, Leona, Sheikh, Aziz, Stiglic, Gregor · 2020 · Scientific Reports
21. Artificial intelligence, cyber-threats and industry 4.0: challenges and opportunities
    Bécue, Adrien, Praça, Isabel, Gama, João · 2021 · Artificial Intelligence Review
22. DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States
    Yu, Jiafan, Wang, Zhecheng, Majumdar, Arun, Rajagopal, Ram · 2018 · Joule
23. Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers
    Austin, Peter C., Steyerberg, Ewout W. · 2014 · Statistics in Medicine
24. A Bag-of-Features Framework to Classify Time Series
    Baydogan, M. G., Runger, G., Tuv, E. · 2013 · IEEE Transactions on Pattern Analysis and Machine Intelligence
25. FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data
    Herman, Josip S, Sagar, Grün, Dominic · 2018 · Nature Methods
26. Gut microbiota in Parkinson's disease: Temporal stability and relations to disease progression
    Aho, Velma T.E., Pereira, Pedro A.B., Voutilainen, Sari, Paulin, Lars, Pekkonen, Eero, Auvinen, Petri, Scheperjans, Filip · 2019 · eBioMedicine
27. On-body localization of wearable devices: An investigation of position-aware activity recognition
    Sztyler, Timo, Stuckenschmidt, Heiner · 2016 · 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)
28. The need to approximate the use-case in clinical machine learning
    Saeb, Sohrab, Lonini, Luca, Jayaraman, Arun, Mohr, David C., Kording, Konrad P. · 2017 · GigaScience
29. Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening
    Ain, Qurrat Ul, Aleksandrova, Antoniya, Roessler, Florian D., Ballester, Pedro J. · 2015 · WIREs Computational Molecular Science
30. A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease
    El-Sappagh, Shaker, Alonso, Jose M., Islam, S. M. Riazul, Sultan, Ahmad M., Kwak, Kyung Sup · 2021 · Scientific Reports
31. The structural and functional signatures of proteins that undergo multiple events of post‐translational modification
    Pejaver, Vikas, Hsu, Wei‐Lun, Xin, Fuxiao, Dunker, A. Keith, Uversky, Vladimir N., Radivojac, Predrag · 2014 · Protein Science
32. Sensitive periods of amygdala development: the role of maltreatment in preadolescence
    Pechtel, Pia, Lyons-Ruth, Karlen, Anderson, Carl M., Teicher, Martin H. · 2014 · NeuroImage
33. A Gut Microbiome Signature for Cirrhosis Due to Nonalcoholic Fatty Liver Disease
    Caussy, Cyrielle, Tripathi, Anupriya, Humphrey, Greg, Bassirian, Shirin, Singh, Seema, Faulkner, Claire, Bettencourt, Ricki, Rizo, Emily, Richards, Lisa, Xu, Zhenjiang Z. et al. · 2019 · Nature Communications
34. Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet
    Bisanz, Jordan E., Upadhyay, Vaibhav, Turnbaugh, Jessie A., Ly, Kimberly, Turnbaugh, Peter J. · 2019 · Cell Host & Microbe
35. Big data analytics for dynamic energy management in smart grids
    Diamantoulakis, Panagiotis D., Kapinas, Vasileios M., Karagiannidis, George K. · 2015 · Big Data Research
36. Brains and algorithms partially converge in natural language processing
    Caucheteux, Charlotte, King, Jean-Rémi · 2022 · Communications Biology
37. Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF
    VerMilyea, M, Hall, J M M, Diakiw, S M, Johnston, A, Nguyen, T, Perugini, D, Miller, A, Picou, A, Murphy, A P, Perugini, M · 2020 · Human Reproduction
38. Tumour genomic and microenvironmental heterogeneity for integrated prediction of 5-year biochemical recurrence of prostate cancer: a retrospective cohort study
    Lalonde, Emilie, Ishkanian, Adrian S, Sykes, Jenna, Fraser, Michael, Ross-Adams, Helen, Erho, Nicholas, Dunning, Mark J, Halim, Silvia, Lamb, Alastair D, Moon, Nathalie C et al. · 2014 · The Lancet Oncology
39. Artificial Intelligence for Predictive Biomarker Discovery in Immuno-Oncology: A Systematic Review
    Prelaj, A., Miskovic, V., Zanitti, M., Trovo, F., Genova, C., Viscardi, G., Rebuzzi, S.E., Mazzeo, L., Provenzano, L., Kosta, S. et al. · 2024 · Annals of Oncology
40. Global and regional analysis of climate and human drivers of wildfire
    Aldersley, Andrew, Murray, Steven J., Cornell, Sarah E. · 2011 · Science of The Total Environment
41. Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles
    Lines, Jason, Taylor, Sarah, Bagnall, Anthony · 2018 · ACM Transactions on Knowledge Discovery from Data
42. Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild
    Wahle, Fabian, Kowatsch, Tobias, Fleisch, Elgar, Rufer, Michael, Weidt, Steffi · 2016 · JMIR mHealth and uHealth
43. The Effects of Data Quality on Machine Learning Performance on Tabular Data
    Mohammed, Sedir, Budach, Lukas, Feuerpfeil, Moritz, Ihde, Nina, Nathansen, Andrea, Noack, Nele, Patzlaff, Hendrik, Naumann, Felix, Harmouch, Hazar · 2025 · Information Systems
44. Resolving early mesoderm diversification through single-cell expression profiling
    Scialdone, Antonio, Tanaka, Yosuke, Jawaid, Wajid, Moignard, Victoria, Wilson, Nicola K., Macaulay, Iain C., Marioni, John C., Göttgens, Berthold · 2016 · Nature
45. Disinformation on the Web: Impact, Characteristics, and Detection of Wikipedia Hoaxes
    Kumar, Srijan, West, Robert, Leskovec, Jure · 2016 · Proceedings of the 25th International Conference on World Wide Web
46. Ensemble methods for spoken emotion recognition in call-centres
    Morrison, Donn, Wang, Ruili, De Silva, Liyanage C. · 2007 · Speech Communication
47. InterpretML: A Unified Framework for Machine Learning Interpretability
    Nori, Harsha, Jenkins, Samuel, Koch, Paul, Caruana, Rich · 2019 · arXiv
48. Guidelines for the use of acoustic indices in environmental research
    Bradfer‐Lawrence, Tom, Gardner, Nick, Bunnefeld, Lynsey, Bunnefeld, Nils, Willis, Stephen G., Dent, Daisy H. · 2019 · Methods in Ecology and Evolution
49. A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease
    Smedley, Damian, Schubach, Max, Jacobsen, Julius O.B., Köhler, Sebastian, Zemojtel, Tomasz, Spielmann, Malte, Jäger, Marten, Hochheiser, Harry, Washington, Nicole L., McMurry, Julie A. et al. · 2016 · The American Journal of Human Genetics
50. Iterative random forests to discover predictive and stable high-order interactions
    Basu, Sumanta, Kumbier, Karl, Brown, James B., Yu, Bin · 2018 · Proceedings of the National Academy of Sciences
51. Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data
    Koutsoukas, Alexios, Monaghan, Keith J., Li, Xiaoli, Huan, Jun · 2017 · Journal of Cheminformatics
52. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques
    Monte-Moreno, Enric · 2011 · Artificial Intelligence in Medicine
53. Deep learning algorithm predicts diabetic retinopathy progression in individual patients
    Arcadu, Filippo, Benmansour, Fethallah, Maunz, Andreas, Willis, Jeff, Haskova, Zdenka, Prunotto, Marco · 2019 · npj Digital Medicine
54. Using Explainable Artificial Intelligence to Improve Process Quality: Evidence from Semiconductor Manufacturing
    Senoner, Julian, Netland, Torbjørn, Feuerriegel, Stefan · 2022 · Management Science
55. bioinformatics tools for mass spectroscopy-based metabolomic data processing and analysis
    Sugimoto, Masahiro, Kawakami, Masato, Robert, Martin, Soga, Tomoyoshi, Tomita, Masaru · 2012 · Current Bioinformatics
56. Sewage Reflects the Microbiomes of Human Populations
    Newton, Ryan J., McLellan, Sandra L., Dila, Deborah K., Vineis, Joseph H., Morrison, Hilary G., Eren, A. Murat, Sogin, Mitchell L. · 2015 · mBio
57. Calibration drift in regression and machine learning models for acute kidney injury
    Davis, Sharon E, Lasko, Thomas A, Chen, Guanhua, Siew, Edward D, Matheny, Michael E · 2017 · Journal of the American Medical Informatics Association
58. Differences in smoking associated DNA methylation patterns in south asians and europeans
    Elliott, Hannah R, Tillin, Therese, McArdle, Wendy L, Ho, Karen, Duggirala, Aparna, Frayling, Tim M, Davey Smith, George, Hughes, Alun D, Chaturvedi, Nish, Relton, Caroline L · 2014 · Clinical Epigenetics
59. A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials
    Gayvert, Kaitlyn M., Madhukar, Neel S., Elemento, Olivier · 2016 · Cell chemical biology
60. Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks
    Emmert-Streib, Frank, Dehmer, Matthias, Haibe-Kains, Benjamin · 2014 · Frontiers in Cell and Developmental Biology
61. Transaction aggregation as a strategy for credit card fraud detection
    Whitrow, C., Hand, D. J., Juszczak, P., Weston, D., Adams, N. M. · 2009 · Data Mining and Knowledge Discovery
62. SHAP and LIME: An Evaluation of Discriminative Power in Credit Risk
    Gramegna, Alex, Giudici, Paolo · 2021 · Frontiers in Artificial Intelligence
63. Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage
    Sarker, Iqbal H., Kayes, A. S. M., Watters, Paul · 2019 · Journal of Big Data
64. A systematic survey of computer-aided diagnosis in medicine: Past and present developments
    Yanase, Juri, Triantaphyllou, Evangelos · 2019 · Expert Systems with Applications
65. Predictably Unequal? The Effects of Machine Learning on Credit Markets
    Fuster, Andreas, Goldsmith-Pinkham, Paul S., Ramadorai, Tarun, Walther, Ansgar · 2017
66. An empirical analysis of feature engineering for predictive modeling
    Heaton, Jeff · 2016 · SoutheastCon 2016
67. MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud1
    Abbasi, Ahmed, Albrecht, Conan, Vance, Anthony, Hansen, James · 2012 · MIS Quarterly
68. Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints
    Isayev, Olexandr, Fourches, Denis, Muratov, Eugene N., Oses, Corey, Rasch, Kevin, Tropsha, Alexander, Curtarolo, Stefano · 2015 · Chemistry of Materials
69. Large-Scale Computerized Text Analysis in Political Science: Opportunities and Challenges
    Wilkerson, John, Casas, Andreu · 2017 · Annual Review of Political Science
70. Structure of the gut microbiome following colonization with human feces determines colonic tumor burden
    Baxter, Nielson T, Zackular, Joseph P, Chen, Grace Y, Schloss, Patrick D · 2014 · Microbiome
71. Traffic accident detection using random forest classifier
    Dogru, Nejdet, Subasi, Abdulhamit · 2018 · 2018 15th Learning and Technology Conference (L&T)
72. Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming
    Pessach, Dana, Singer, Gonen, Avrahami, Dan, Chalutz Ben-Gal, Hila, Shmueli, Erez, Ben-Gal, Irad · 2020 · Decision Support Systems
73. Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning
    Stowell, Dan, Plumbley, Mark D. · 2014 · PeerJ
74. Genomic Diversity and Versatility of Lactobacillus plantarum, a Natural Metabolic Engineer
    Siezen, Roland J, van Hylckama Vlieg, Johan ET · 2011 · Microbial Cell Factories
75. Climate change rapidly warms and acidifies Australian estuaries
    Scanes, Elliot, Scanes, Peter R., Ross, Pauline M. · 2020 · Nature Communications
76. Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships
    Janet, Jon Paul, Kulik, Heather J. · 2017 · The Journal of Physical Chemistry A
77. Tutorial in biostatistics: data‐driven subgroup identification and analysis in clinical trials
    Lipkovich, Ilya, Dmitrienko, Alex, B., Ralph · 2017 · Statistics in Medicine
78. Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box
    Ciompi, Francesco, de Hoop, Bartjan, van Riel, Sarah J., Chung, Kaman, Scholten, Ernst Th., Oudkerk, Matthijs, de Jong, Pim A., Prokop, Mathias, Ginneken, Bram van · 2015 · Medical Image Analysis
79. A data-driven predictive model of city-scale energy use in buildings
    Kontokosta, Constantine E., Tull, Christopher · 2017 · Applied Energy
80. Forecasting and operational research: a review
    Fildes, R, Nikolopoulos, K, Crone, S F, Syntetos, A A · 2008 · Journal of the Operational Research Society
81. Retention Futility: Targeting High-Risk Customers Might be Ineffective
    Ascarza, Eva · 2018 · Journal of Marketing Research
82. Quantitative Structure−Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure
    Zhu, Hao, Martin, Todd M., Ye, Lin, Sedykh, Alexander, Young, Douglas M., Tropsha, Alexander · 2009 · Chemical Research in Toxicology
83. Automated measurement of fetal head circumference using 2D ultrasound images
    van den Heuvel, Thomas L. A., de Bruijn, Dagmar, de Korte, Chris L., Ginneken, Bram van · 2018 · PLOS ONE
84. CrossCheck: toward passive sensing and detection of mental health changes in people with schizophrenia
    Wang, Rui, Aung, Min S. H., Abdullah, Saeed, Brian, Rachel, Campbell, Andrew T., Choudhury, Tanzeem, Hauser, Marta, Kane, John, Merrill, Michael, Scherer, Emily A. et al. · 2016 · Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
85. Does learning require memorization? a short tale about a long tail
    Feldman, Vitaly · 2020 · Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing
86. Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks
    Taormina, Riccardo, Galelli, Stefano, Tippenhauer, Nils Ole, Salomons, Elad, Ostfeld, Avi, Eliades, Demetrios G., Aghashahi, Mohsen, Sundararajan, Raanju, Pourahmadi, Mohsen, Banks, M. Katherine et al. · 2018 · Journal of Water Resources Planning and Management
87. Auditing black-box models for indirect influence
    Adler, Philip, Falk, Casey, Friedler, Sorelle A., Nix, Tionney, Rybeck, Gabriel, Scheidegger, Carlos, Smith, Brandon, Venkatasubramanian, Suresh · 2018 · Knowledge and Information Systems
88. Effects of landscape anthropization on mosquito community composition and abundance
    Ferraguti, Martina, Martínez-de la Puente, Josué, Roiz, David, Ruiz, Santiago, Soriguer, Ramón, Figuerola, Jordi · 2016 · Scientific Reports
89. On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data
    Schwarz, Daniel F., König, Inke R., Ziegler, Andreas · 2010 · Bioinformatics
90. A review of Earth Artificial Intelligence
    Sun, Ziheng, Sandoval, Laura, Crystal-Ornelas, Robert, Mousavi, S. Mostafa, Wang, Jinbo, Lin, Cindy, Cristea, Nicoleta, Tong, Daniel, Carande, Wendy Hawley, Ma, Xiaogang et al. · 2022 · Computers & Geosciences
91. Cell type prioritization in single-cell data
    Skinnider, Michael A., Squair, Jordan W., Kathe, Claudia, Anderson, Mark A., Gautier, Matthieu, Matson, Kaya J. E., Milano, Marco, Hutson, Thomas H., Barraud, Quentin, Phillips, Aaron A. et al. · 2021 · Nature Biotechnology
92. Mining opinions from instructor evaluation reviews: a deep learning approach
    Onan, Aytuğ · 2020 · Computer Applications in Engineering Education
93. Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants
    Willetts, Matthew, Hollowell, Sven, Aslett, Louis, Holmes, Chris, Doherty, Aiden · 2018 · Scientific Reports
94. Is Multitask Deep Learning Practical for Pharma?
    Ramsundar, Bharath, Liu, Bowen, Wu, Zhenqin, Verras, Andreas, Tudor, Matthew, Sheridan, Robert P., Pande, Vijay · 2017 · Journal of Chemical Information and Modeling
95. PhishStorm: Detecting Phishing With Streaming Analytics
    Marchal, Samuel, Francois, Jerome, State, Radu, Engel, Thomas · 2014 · IEEE Transactions on Network and Service Management
96. Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models
    Flesia, Luca, Monaro, Merylin, Mazza, Cristina, Fietta, Valentina, Colicino, Elena, Segatto, Barbara, Roma, Paolo · 2020 · Journal of Clinical Medicine
97. Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and LSTM recurrent neural networks
    Sakar, C. Okan, Polat, S. Olcay, Katircioglu, Mete, Kastro, Yomi · 2019 · Neural Computing and Applications
98. Explainable intrusion detection for cyber defences in the internet of things: opportunities and solutions
    Moustafa, Nour, Koroniotis, Nickolaos, Keshk, Marwa, Zomaya, Albert Y., Tari, Zahir · 2023 · IEEE Communications Surveys & Tutorials
99. Early emergence of cortical interneuron diversity in the mouse embryo
    Mi, Da, Li, Zhen, Lim, Lynette, Li, Mingfeng, Moissidis, Monika, Yang, Yifei, Gao, Tianliuyun, Hu, Tim Xiaoming, Pratt, Thomas, Price, David J. et al. · 2018 · Science
100. A High-Resolution Enhancer Atlas of the Developing Telencephalon
     Visel, Axel, Taher, Leila, Girgis, Hani, May, Dalit, Golonzhka, Olga, Hoch, Renee V., McKinsey, Gabriel L., Pattabiraman, Kartik, Silberberg, Shanni N., Blow, Matthew J. et al. · 2013 · Cell