The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Applications of Deep Learning and Reinforcement Learning to Biological Data
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?
In the citing paper
“Rapid advances in artificial intelligence and machine learning are already enabling substantial advances in biological design that were unimaginable 5 years ago (193–196), and the application of these techniques to the PFAS problem has already started to appear in the scientific literature (197–199).”
From Toward the development of a molecular toolkit for the microbial remediation of per-and polyfluoroalkyl substances (2024) · cites this paper as [193]
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
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
Sequencing technologies – the next generation
Metzker, M. L. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2626 · Cited by 7,066
Research works printed but not linked: 180
- 10.1016/j.jneumeth.2016.10.008 · DOI
- yang, 2015, On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification, Proc EMBC, 2620
- 10.1109/APWC-on-CSE.2016.017 · DOI
- 10.1109/EUSIPCO.2015.7362882 · DOI
- 10.1088/1741-2560/14/1/016003 · DOI · Paper page
- hajinoroozi, 2015, Prediction of driver’s drowsy and alert states from eeg signals with deep learning, Proc IEEE CAMSAP, 493
- tripathi, 2017, Using deep and convolutional neural networks for accurate emotion classification on DEAP dataset, Proc IAAI, 4746
- 10.1109/ICME.2014.6890301 · DOI
- 10.1155/2012/107046 · DOI
- 10.3389/fnins.2017.00103 · DOI
- 10.1287/ijoc.1080.0305 · DOI
- 10.1109/JBHI.2016.2636665 · DOI · Paper page
- 10.1016/j.neunet.2014.09.003 · DOI · Paper page
- 10.1038/nature14236 · DOI · Paper page
- 10.3389/fnins.2016.00248 · DOI
- glorennec, 2000, Reinforcement learning: An overview, Proc ESIT, 17
- 10.1093/bib/bbw068 · DOI
- 10.1109/TBME.2007.893452 · DOI
- 10.1016/S0925-2312(99)00126-5 · DOI
- 10.1109/IWW-BCI.2016.7457459 · DOI
- 10.3389/fnins.2015.00036 · DOI
- 10.1145/2557500.2557533 · DOI
- 10.1016/j.ins.2016.01.082 · DOI
- 10.1142/S1469026816500218 · DOI
- 10.1109/BSN.2015.7299399 · DOI
- 10.1109/CSE.2014.36 · DOI
- 10.1038/icb.2017.16 · DOI
- bashivan, 2016, Learning representations from EEG with deep recurrent-convolutional neural networks, CoRR
- 10.1016/0004-3702(89)90049-0 · DOI
- 10.1007/BF00337288 · DOI · Paper page
- 10.1080/01969727308546046 · DOI · Paper page
- 10.1038/nrg3920 · DOI
- hartigan, 1975, Clustering Algorithms
- lecun, 1998, Convolutional networks for images, speech, and time series, The Handbook of Brain Theory and Neural Networks, 255
- 10.1016/j.media.2016.07.007 · DOI · Paper page
- 10.1016/j.compmedimag.2016.07.004 · DOI
- 10.1016/j.media.2016.08.008 · DOI
- 10.1016/j.media.2017.01.009 · DOI
- 10.1109/BioCAS.2015.7348440 · DOI
- 10.1016/j.neucom.2016.02.060 · DOI
- 10.1016/j.cmpb.2015.12.014 · DOI
- 10.1109/JBHI.2015.2425041 · DOI · Paper page
- 10.1109/TMI.2016.2535865 · DOI · Paper page
- 10.1109/TMI.2016.2535865 · DOI · Paper page
- 10.1109/ROBIO.2015.7419037 · DOI
- salakhutdinov, 2009, Deep Boltzmann machines, Proc AISTATS, 448
- rifai, 2011, Contractive auto-encoders: Explicit invariance during feature extraction, Proc ICML, 833
- ranzato, 2006, Efficient learning of sparse representations with an energy-based model, Proc NIPS, 1137
- baldi, 2012, Autoencoders, unsupervised learning and deep architectures, Proc ICUTLW, 37
- vincent, 2010, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, J Mach Learn Res, 11, 3371
- 10.1109/CVPR.2015.7298594 · DOI · Paper page
- simonyan, 2015, Very deep convolutional networks for large-scale image recognition, CoRR, abs 1409 1556
- 10.1561/2200000006 · DOI
- zhao, 2015, Deep learning in the EEG diagnosis of Alzheimer’s disease, Proc ACCV, 340
- 10.1088/1741-2560/8/3/036015 · DOI
- 10.1109/TAMD.2015.2431497 · DOI
- 10.1109/BIBE.2014.26 · DOI
- 10.1109/BIBM.2013.6732507 · DOI
- 10.1007/978-3-319-09330-7_25 · DOI
- 10.1109/TNSRE.2016.2601240 · DOI
- 10.1155/2014/627892 · DOI
- 10.1038/nbt1486 · DOI · Paper page
- 10.1126/science.1232919 · DOI
- vadivambal, 2016, Bio-Imaging Principles Techniques and Applications
- 10.1152/physrev.00027.2016 · DOI
- 10.1007/978-3-319-12637-1_63 · DOI
- 10.1038/nature15692 · DOI
- wiatowski, 2015, A mathematical theory of deep convolutional neural networks for feature extraction, CoRR, abs 1512 6293
- 10.1016/j.media.2017.05.001 · DOI
- 10.1038/498255a · DOI
- 10.1186/1471-2342-8-8 · DOI
- lipton, 2015, A critical review of recurrent neural networks for sequence learning, CoRR, abs 1506 19
- saxe, 2014, Exact solutions to the nonlinear dynamics of learning in deep linear neural networks, CoRR, abs 1312 6120
- goodfellow, 2016, Deep Learning
- 10.1016/0364-0213(90)90002-E · DOI · Paper page
- pascanu, 2014, How to construct deep recurrent neural networks, Proc ICLR, 1
- 10.1109/CVPR.2015.7298640 · DOI · Paper page
- 10.4249/scholarpedia.1448 · DOI
- schaul, 2015, Universal value function approximators, Proc ICML, 1312
- fakoor, 2013, Using deep learning to enhance cancer diagnosis and classification, Proc ICML
- danaee, 2016, A deep learning approach for cancer detection and relevant gene identification, Proc Pacific Symp Biocomput, 22, 219
- hasselt, 2010, Double Q-learning, Proc NIPS, 2613
- 10.1007/s10278-017-9965-6 · DOI
- 10.4172/jpb.1000419 · DOI
- lee, 2015, Boosted categorical restricted Boltzmann machine for computational prediction of splice junctions, Proc ICML, 2483
- 10.1109/TPAMI.1984.4767596 · DOI · Paper page
- desjardins, 2012, On training deep Boltzmann machines, CoRR, abs 1203 4416
- fischer, 2012, An introduction to restricted Boltzmann machines, Proc CIARP, 14
- 10.1145/1390156.1390290 · DOI
- 10.1016/j.neucom.2015.09.116 · DOI · Paper page
- 10.1162/neco.2006.18.7.1527 · DOI · Paper page
- poole, 2017, Artificial Intelligence Foundations of Computational Agents, 10.1017/9781108164085 · DOI
- busoniu, 2010, Reinforcement Learning and Dynamic Programming Using Function Approximators, 10.1201/9781439821091 · DOI
- 10.1145/1553374.1553501 · DOI
- 10.1007/s12559-016-9404-x · DOI
- li, 2017, Hierarchical convolutional neural networks for EEG-based emotion recognition, Cognit Comput, 1
- 10.1088/1741-2560/10/6/066005 · DOI
- 10.4028/www.scientific.net/AMM.461.565 · DOI
- 10.1371/journal.pone.0087253 · DOI
- 10.1109/TNNLS.2015.2493079 · DOI
- 10.1093/bioinformatics/btw255 · DOI
- 10.1101/gr.200535.115 · DOI · Paper page
- 10.1109/TBME.2008.926699 · DOI
- 10.1038/nbt.3300 · DOI · Paper page
- 10.1371/journal.pone.0014760 · DOI
- denas, 2013, Deep modeling of gene expression regulation in erythropoiesis model, Proc of the ICML, 1
- 10.1109/NER.2011.5910601 · DOI
- 10.1002/minf.201600045 · DOI
- 10.1186/s12859-016-1334-9 · DOI
- 10.1038/srep18962 · DOI
- park, 2016, deepMiRGene: Deep neural network based precursor microrna prediction, CoRR, abs 1605 17
- 10.1038/nmeth.3547 · DOI · Paper page
- 10.1038/ng.3810 · DOI
- 10.1186/s12859-017-1561-8 · DOI
- 10.1093/bioinformatics/btv315 · DOI
- 10.1093/nar/gkv1025 · DOI
- 10.1093/bioinformatics/btw074 · DOI
- 10.1109/EMBC.2014.6944490 · DOI
- wan, 2016, Deep learning with feature embedding for compound-protein interaction prediction, BioRxiv, 86033
- 10.1093/bioinformatics/btu703 · DOI
- 10.1038/srep11476 · DOI
- 10.1016/j.ymeth.2016.06.024 · DOI
- fernández-delgado, 2014, Do we need hundreds of classifiers to solve real world classification problems?, J Mach Learn Res, 15, 3133
- 10.1109/TAAI.2010.65 · DOI
- 10.1145/2975167.2975212 · DOI
- 10.3389/fninf.2014.00026 · DOI
- 10.1109/TMI.2014.2377694 · DOI · Paper page
- 10.1186/1532-429X-18-S1-P351 · DOI
- 10.1038/nmeth.2016 · DOI · Paper page
- szegedy, 2014, Intriguing properties of neural networks, CoRR, abs 1312 6199
- 10.1515/bmt-2012-4412 · DOI
- 10.1109/ICPR.2010.579 · DOI · Paper page
- angelov, 2016, Challenges in deep learning, Proc ESANN, 489
- mahmud, 2014, A Web-based framework for semi-online parallel processing of extracellular neuronal signals recorded by microelectrode arrays, Proc MEA Meeting, 202
- 10.1007/978-3-319-50478-0_6 · DOI
- 10.1109/JBHI.2017.2655720 · DOI · Paper page
- 10.1007/978-3-319-46723-8_25 · DOI · Paper page
- sarraf, 2016, DeepAD: Alzheimer’s disease classification via deep convolutional neural networks using MRI and fMRI, BioRxiv, 70441
- 10.1016/j.patcog.2015.03.017 · DOI
- 10.1007/978-3-319-46723-8_19 · DOI
- stollenga, 2015, Parallel multi-dimensional LSTM, with application to fast biomedical volumetric image segmentation, Proc NIPS, 2998
- agostinelli, 2013, Adaptive multi-column deep neural networks with application to robust image denoising, Proc NIPS, 1493
- cho, 2015, Medical image deep learning with hospital PACS dataset, CoRR, abs 1511 6348
- 10.1016/j.cmpb.2016.10.007 · DOI
- 10.1016/j.patcog.2016.05.029 · DOI
- 10.1016/j.patcog.2016.09.029 · DOI
- 10.1109/CCBD.2014.42 · DOI
- 10.1371/journal.pone.0137036 · DOI
- magner, 2002, A History of the Life Sciences
- 10.1109/ICDMW.2016.0041 · DOI · Paper page
- coleman, 1977, Biology in the Nineteenth Century Problems of Form Function and Transformatoin
- 10.1016/j.media.2016.05.009 · DOI
- cire?an, 2013, Mitosis detection in breast cancer histology images with deep neural networks, Proc MICCAI, 411
- 10.1109/TIP.2005.852470 · DOI
- 10.1038/srep21471 · DOI
- 10.1109/ICASSP.2014.6853873 · DOI
- 10.1109/TMI.2015.2458702 · DOI · Paper page
- 10.1109/BIBM.2014.6999302 · DOI
- 10.1109/SYNASC.2011.9 · DOI
- 10.1109/SBRN.2010.27 · DOI
- 10.1534/g3.116.033654 · DOI
- 10.1016/j.patcog.2016.07.016 · DOI
- cire?an, 2012, Deep neural networks segment neuronal membranes in electron microscopy images, Proc NIPS, 2843
- 10.1016/j.gpb.2014.10.001 · DOI
- 10.1038/35007262 · DOI
- 10.1109/101.8118 · DOI
- 10.1109/JPROC.2012.2188013 · DOI
- 10.1007/978-94-011-5014-9_11 · DOI
- 10.1016/j.patcog.2016.09.032 · DOI
- 10.1109/TIT.1967.1053964 · DOI · Paper page
- 10.1007/978-3-642-40763-5_72 · DOI
- 10.1109/MASSP.1986.1165342 · DOI · Paper page
- kohavi, 2002, Data mining tasks and methods: Classification: Decision-tree discovery, Handbook of Data Mining and Knowledge Discovery, 267
- 10.1007/978-3-642-40763-5_78 · DOI
- eulenberg, 2016, Deep learning for imaging flow cytometry: Cell cycle analysis of Jurkat cells, BioRxiv, 81364
- 10.1093/bioinformatics/btw252 · DOI
- 10.1109/TMI.2016.2535222 · DOI
- 10.1109/IMCCC.2015.33 · DOI
- 10.1109/JBHI.2015.2429556 · DOI
- 10.3389/fnins.2014.00229 · DOI
Cited by
Sorted by citations
Brain tumor detection using fusion of hand crafted and deep learning features
Saba, T., Sameh Mohamed, A., El-Affendi, M., Amin, J., & Sharif, M. (2020) · Cognitive Systems Research · doi:10.1016/j.cogsys.2019.09.007 · Cited by 433
Reinforcement learning for intelligent healthcare applications: A survey
Coronato, A., Naeem, M., De Pietro, G., & Paragliola, G. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101964 · Cited by 320
Miglani, A., & Kumar, N. (2019) · Vehicular Communications · doi:10.1016/j.vehcom.2019.100184 · Cited by 295
A comprehensive survey on multimodal medical signals fusion for smart healthcare systems
Muhammad, G., Alshehri, F., Karray, F., Saddik, A. E., Alsulaiman, M., & Falk, T. H. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.06.007 · Cited by 287
A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers
Zhang, X., Yao, L., Wang, X., Monaghan, J., McAlpine, D., & Zhang, Y. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abc902 · Cited by 296
Metadata
- Authors
- Mahmud, M., Kaiser, M. S., Hussain, A., & Vassanelli, S.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2018-06
- Volume
- 29
- Issue
- 6
- Pages
- 2063-2079
- ISSN
- 2162-237X
- 2162-2388
- Cited by
- 765
- References
- 29
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Applications of Deep Learning and Reinforcement Learning to Biological Data. https://app.cochat.ai/papers/10.1109/tnnls.2018.2790388
- The paper
- Mahmud, M., Kaiser, M. S., Hussain, A., & Vassanelli, S. (2018). Applications of Deep Learning and Reinforcement Learning to Biological Data. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2018.2790388