A citation-based method for searching scientific literature

A fast learning algorithm for deep belief nets.
Geoffrey E Hinton, Simon Osindero, Yee-Whye Teh. Neural Comput 2006
Times Cited: 1233






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List of co-cited articles
183 articles co-cited >1



Times Cited
  Times     Co-cited
Similarity


Deep learning.
Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Nature 2015
8428
27
27

Long short-term memory.
S Hochreiter, J Schmidhuber. Neural Comput 1997
3838
17
17

Reducing the dimensionality of data with neural networks.
G E Hinton, R R Salakhutdinov. Science 2006
1547
13
13

The perceptron: a probabilistic model for information storage and organization in the brain.
F ROSENBLATT. Psychol Rev 1958
940
12
12

Representation learning: a review and new perspectives.
Yoshua Bengio, Aaron Courville, Pascal Vincent. IEEE Trans Pattern Anal Mach Intell 2013
957
9
9

Deep learning in neural networks: an overview.
Jürgen Schmidhuber. Neural Netw 2015
1659
9
9

A survey on deep learning in medical image analysis.
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A W M van der Laak, Bram van Ginneken, Clara I Sánchez. Med Image Anal 2017
2237
9
9

Training products of experts by minimizing contrastive divergence.
Geoffrey E Hinton. Neural Comput 2002
391
6
6

Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position.
K Fukushima. Biol Cybern 1980
667
6
6

Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis.
Heung-Il Suk, Seong-Whan Lee, Dinggang Shen. Neuroimage 2014
231
5
5

Deep Learning in Medical Image Analysis.
Dinggang Shen, Guorong Wu, Heung-Il Suk. Annu Rev Biomed Eng 2017
909
5
5

A logical calculus of the ideas immanent in nervous activity. 1943.
W S McCulloch, W Pitts. Bull Math Biol 1990
668
5
5

Dermatologist-level classification of skin cancer with deep neural networks.
Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, Sebastian Thrun. Nature 2017
2928
5
5

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.
Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla. IEEE Trans Pattern Anal Mach Intell 2017
995
5
5

Human-level control through deep reinforcement learning.
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski,[...]. Nature 2015
959
4
4

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans.
Jie-Zhi Cheng, Dong Ni, Yi-Hong Chou, Jing Qin, Chui-Mei Tiu, Yeun-Chung Chang, Chiun-Sheng Huang, Dinggang Shen, Chung-Ming Chen. Sci Rep 2016
222
4
4

Re-epithelialization and immune cell behaviour in an ex vivo human skin model.
Ana Rakita, Nenad Nikolić, Michael Mildner, Johannes Matiasek, Adelheid Elbe-Bürger. Sci Rep 2020
3053
4
4

Mastering the game of Go with deep neural networks and tree search.
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot,[...]. Nature 2016
1026
4
4

Object Detection With Deep Learning: A Review.
Zhong-Qiu Zhao, Peng Zheng, Shou-Tao Xu, Xindong Wu. IEEE Trans Neural Netw Learn Syst 2019
185
4
4

Deep learning for healthcare: review, opportunities and challenges.
Riccardo Miotto, Fei Wang, Shuang Wang, Xiaoqian Jiang, Joel T Dudley. Brief Bioinform 2018
409
4
4

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.
Hoo-Chang Shin, Holger R Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel Mollura, Ronald M Summers. IEEE Trans Med Imaging 2016
946
4
4

Molecular graph convolutions: moving beyond fingerprints.
Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, Patrick Riley. J Comput Aided Mol Des 2016
356
3
3

Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.
Freddie Bray, Jacques Ferlay, Isabelle Soerjomataram, Rebecca L Siegel, Lindsey A Torre, Ahmedin Jemal. CA Cancer J Clin 2018
38991
3
3

Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks.
David R Kelley, Jasper Snoek, John L Rinn. Genome Res 2016
327
3
3

Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution.
Peijun Hu, Fa Wu, Jialin Peng, Ping Liang, Dexing Kong. Phys Med Biol 2016
74
3
4

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records.
Riccardo Miotto, Li Li, Brian A Kidd, Joel T Dudley. Sci Rep 2016
410
3
3

Latent feature representation with stacked auto-encoder for AD/MCI diagnosis.
Heung-Il Suk, Seong-Whan Lee, Dinggang Shen. Brain Struct Funct 2015
142
3
3

Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease.
Andrés Ortiz, Jorge Munilla, Juan M Górriz, Javier Ramírez. Int J Neural Syst 2016
112
3
3

Representational power of restricted boltzmann machines and deep belief networks.
Nicolas Le Roux, Yoshua Bengio. Neural Comput 2008
81
3
3

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L Yuille. IEEE Trans Pattern Anal Mach Intell 2018
795
3
3

Deep Learning for Computer Vision: A Brief Review.
Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Eftychios Protopapadakis. Comput Intell Neurosci 2018
243
3
3

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.
Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun. IEEE Trans Pattern Anal Mach Intell 2017
1364
3
3

Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review.
Waseem Rawat, Zenghui Wang. Neural Comput 2017
333
3
3

Opportunities and obstacles for deep learning in biology and medicine.
Travers Ching, Daniel S Himmelstein, Brett K Beaulieu-Jones, Alexandr A Kalinin, Brian T Do, Gregory P Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M Hoffman,[...]. J R Soc Interface 2018
525
3
3

deepDR: a network-based deep learning approach to in silico drug repositioning.
Xiangxiang Zeng, Siyi Zhu, Xiangrong Liu, Yadi Zhou, Ruth Nussinov, Feixiong Cheng. Bioinformatics 2019
151
3
3

Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning.
Nicolas Coudray, Paolo Santiago Ocampo, Theodore Sakellaropoulos, Navneet Narula, Matija Snuderl, David Fenyö, Andre L Moreira, Narges Razavian, Aristotelis Tsirigos. Nat Med 2018
675
3
3

Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.
D H HUBEL, T N WIESEL. J Physiol 1962
6639
3
3

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).
Bjoern H Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest,[...]. IEEE Trans Med Imaging 2015
762
3
3

Deep learning based imaging data completion for improved brain disease diagnosis.
Rongjian Li, Wenlu Zhang, Heung-Il Suk, Li Wang, Jiang Li, Dinggang Shen, Shuiwang Ji. Med Image Comput Comput Assist Interv 2014
111
3
3

Convolutional neural networks: an overview and application in radiology.
Rikiya Yamashita, Mizuho Nishio, Richard Kinh Gian Do, Kaori Togashi. Insights Imaging 2018
372
3
3

Deep learning for computational biology.
Christof Angermueller, Tanel Pärnamaa, Leopold Parts, Oliver Stegle. Mol Syst Biol 2016
483
3
3

The ChEMBL database in 2017.
Anna Gaulton, Anne Hersey, Michał Nowotka, A Patrícia Bento, Jon Chambers, David Mendez, Prudence Mutowo, Francis Atkinson, Louisa J Bellis, Elena Cibrián-Uhalte,[...]. Nucleic Acids Res 2017
887
2
2

Learning multiple layers of representation.
Geoffrey E Hinton. Trends Cogn Sci 2007
155
2
2

Deep neural nets as a method for quantitative structure-activity relationships.
Junshui Ma, Robert P Sheridan, Andy Liaw, George E Dahl, Vladimir Svetnik. J Chem Inf Model 2015
346
2
2

Drug repositioning: identifying and developing new uses for existing drugs.
Ted T Ashburn, Karl B Thor. Nat Rev Drug Discov 2004
1548
2
2

De novo generation of hit-like molecules from gene expression signatures using artificial intelligence.
Oscar Méndez-Lucio, Benoit Baillif, Djork-Arné Clevert, David Rouquié, Joerg Wichard. Nat Commun 2020
83
2
2

DeepPhos: prediction of protein phosphorylation sites with deep learning.
Fenglin Luo, Minghui Wang, Yu Liu, Xing-Ming Zhao, Ao Li. Bioinformatics 2019
55
2
3

Recurrent Neural Network for Predicting Transcription Factor Binding Sites.
Zhen Shen, Wenzheng Bao, De-Shuang Huang. Sci Rep 2018
52
2
3

Musite, a tool for global prediction of general and kinase-specific phosphorylation sites.
Jianjiong Gao, Jay J Thelen, A Keith Dunker, Dong Xu. Mol Cell Proteomics 2010
168
2
2

Artificial Intelligence-Based Classification of Multiple Gastrointestinal Diseases Using Endoscopy Videos for Clinical Diagnosis.
Muhammad Owais, Muhammad Arsalan, Jiho Choi, Tahir Mahmood, Kang Ryoung Park. J Clin Med 2019
19
2
10


Co-cited is the co-citation frequency, indicating how many articles cite the article together with the query article. Similarity is the co-citation as percentage of the times cited of the query article or the article in the search results, whichever is the lowest. These numbers are calculated for the last 100 citations when articles are cited more than 100 times.

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