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A curated list of resources of machine learning related datasets and open-source models for biomedicine.

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deep-medicine

A curated list of resources of machine learning related datasets and open-source models for biomedicine.

Part content modified from beamandrew/medical-data.

Reference & Publications & PPT

  • DLradiologyRSNA2016
  • Susskind, Joshua, Volodymyr Mnih, and Geoffrey Hinton. "On deep generative models with applications to recognition." Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 2011.
  • Brosch, Tom, Roger Tam, and Alzheimer’s Disease Neuroimaging Initiative. "Manifold learning of brain MRIs by deep learning." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer Berlin Heidelberg, 2013.
  • Wu, Zhirong, et al. "3d shapenets: A deep representation for volumetric shapes." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
  • Calandra, Roberto, et al. "Learning deep belief networks from non-stationary streams." International Conference on Artificial Neural Networks. Springer Berlin Heidelberg, 2012.
  • Conv Nets: A Modular Perspective ( http://colah.github.io/posts/2014-07-Conv-Nets-Modular/ )
  • A BRIEF REPORT OF THE HEURITECH DEEP LEARNING MEETUP #5 (https://blog.heuritech.com/2016/02/29/a-brief-report-of-the-heuritechdeep-learning-meetup-5/)
  • Deep Learning Tutorial ICML, Atlanta 2013, Yann LeCun and Marc'Aurelio Ranzato
  • Zeiler, Matthew D., and Rob Fergus. "Visualizing and understanding convolutional networks." European Conference on Computer Vision. Springer International Publishing, 2014.
  • Ji, Shuiwang, et al. "3D convolutional neural networks for human action recognition." IEEE transactions on pattern analysis and machine intelligence35.1 (2013): 221-231.
  • Tran, Du, et al. "Learning spatiotemporal features with 3d convolutional networks." 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015.
  • Deep Style: Inferring the Unknown to Predict the Future of Fashion, TJ TORRES, http://multithreaded.stitchfix.com/blog/2015/09/17/deepstyle/
  • Recurrent Neural Networks Neural Computation : Lecture 12, John A. Bullinaria, 2015 http://www.cs.bham.ac.uk/~jxb/INC/l12.pdf
  • LSTM Networks for Sentiment Analysis, http://deeplearning.net/tutorial/lstm.html
  • Yan, Zhennan, et al. "Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart Recognition." IEEE transactions on medical imaging 35.5 (2016): 1332-1343.
  • Roth, Holger R., et al. "Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer International Publishing, 2015.
  • Cha, Kenny H., et al. "Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets." Medical physics43.4 (2016): 1882-1896.
  • Miao, Shun, Z. Jane Wang, and Rui Liao. "A CNN Regression Approach for Real-Time 2D/3D Registration." IEEE transactions on medical imaging 35.5 (2016): 1352-1363. References
  • van Tulder, Gijs, and Marleen de Bruijne. "Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines." IEEE transactions on medical imaging 35.5 (2016): 1262-1272.
  • Roth, Holger R., et al. "A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations."International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer International Publishing, 2014.
  • Setio, Arnaud Arindra Adiyoso, et al. "Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks."IEEE transactions on medical imaging 35.5 (2016): 1160-1169.
  • Li, Wen, Fucang Jia, and Qingmao Hu. "Automatic Segmentation of Liver Tumor in CT Images with Deep Convolutional Neural Networks." Journal of Computer and Communications 3.11 (2015): 146.
  • Guo, Yanrong, Yaozong Gao, and Dinggang Shen. "Deformable MR prostate segmentation via deep feature learning and sparse patch matching." IEEE transactions on medical imaging 35.4 (2016): 1077-1089.
  • Avendi, M. R., Arash Kheradvar, and Hamid Jafarkhani. "A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI." Medical image analysis 30 (2016): 108-119.
  • Moeskops, Pim, et al. "Automatic segmentation of MR brain images with a convolutional neural network." IEEE transactions on medical imaging 35.5 (2016): 1252-1261.
  • Hosseini-Asl, Ehsan, Robert Keynton, and Ayman El-Baz. "Alzheimer's disease diagnostics by adaptation of 3D convolutional network." Image Processing (ICIP), 2016 IEEE International Conference on. IEEE, 2016.
  • Zhen, Xiantong, et al. "Multi-scale deep networks and regression forests for direct bi-ventricular volume estimation." Medical image analysis 30 (2016): 120-129.
  • Nie, Dong, et al. "3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer International Publishing, 2016.
  • Vasilakos, Athanasios V., Yu Tang, and Yuanzhe Yao. "Neural networks for computer-aided diagnosis in medicine: A review." Neurocomputing (2016).
  • https://www.researchgate.net/profile/Yingju_Chen/publication/233806620/figure/fig4/AS:202659295436813@1425329149496/General-stepsinvolving-in-computer-aided-diagnosis-CAD-system-where-gray-boxes-may-be.png
  • Tajbakhsh, Nima, et al. "Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?." IEEE transactions on medical imaging 35.5 (2016): 1299-1312.
  • 5 Industries Being Most Affected By Artificial Intelligence https://www.fowcommunity.com/blog/future-work/5-industries-being-most-affectedartificial-intelligence
  • Cheng, Jie-Zhi, et al. "Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans." Scientific reports 6 (2016).
  • Artificial Intelligence & Machine Learning for Semantic Imaging, Imperial College London http://wp.doc.ic.ac.uk/bglocker/project/semanticimaging/
  • Leung, M.K.K., Andrew, D., Babak, A. & Frey, B.J. Machine learning in genomic medicine: a review of computational problems and data sets. Proc. IEEE 104, 176–197 (2016).
  • Mamoshina, P., Vieira, A., Putin, E. & Zhavoronkov, A. Applications of deep learning in biomedicine. Mol. Pharm. 13, 1445–1454 (2016).
  • Gawehn, E., Hiss, J.A. & Schneider, G. Deep learning in drug discovery. Mol. Inform. 35, 3–14 (2016).
  • Jurtz, V.I. et al. An introduction to deep learning on biological sequence data: examples and solutions. Bioinformatics 33, 3685–3690 (2017).
  • Zou, J., et al., A primer on deep learning in genomics. Nat Genet, 2019. 51(1): p. 12-18.
  • Eraslan, G., et al., Deep learning: new computational modelling techniques for genomics. Nat Rev Genet, 2019.
  • Wainberg M, Merico D, Delong A, Frey BJ. Deep learning in biomedicine. Nat Biotechnol. 2018;36(9):829-838. doi:10.1038/nbt.4233
  • Ching, T., et al., Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface, 2018. 15(141).
  • Min, S., B. Lee, and S. Yoon, Deep learning in bioinformatics. Brief Bioinform, 2017. 18(5): p. 851-869.
  • Jones, W., et al., Computational biology: deep learning. Emerging Topics in Life Sciences, 2017. 1(3): p. 257-274.
  • Angermueller, C., et al., Deep learning for computational biology. Mol Syst Biol, 2016. 12(7): p. 878.
  • Zhou, J., et al., Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat Genet, 2018. 50(8): p. 1171-1179.
  • Sundaram, L., et al., Predicting the clinical impact of human mutation with deep neural networks. Nat Genet, 2018.
  • Libbrecht, M.W. and W.S. Noble, Machine learning applications in genetics and genomics. Nat Rev Genet, 2015. 16(6): p. 321-32.
  • Camacho, D.M., et al., Next-Generation Machine Learning for Biological Networks. Cell, 2018. 173(7): p. 1581-1592.
  • Baldi, P. Deep learning in biomedical data science. Annu. Rev. Biomed. Data Sci. 1, 181–205 (2018).
  • Tan, J., Ung, M., Cheng, C. & Greene, C.S. Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders. Pac. Symp. Biocomput. 2015, 132–143 (2015).
  • Gómez-Bombarelli, R. et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci. 4, 268–276 (2018).
  • Visscher, P.M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).
  • Boyle, E.A., Li, Y.I. & Pritchard, J.K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).
  • Alipanahi, B., Delong, A., Weirauch, M.T. & Frey, B.J. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol. 33, 831–838 (2015).
  • Kelley, D.R., Snoek, J. & Rinn, J.L. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res. 26, 990–999 (2016).
  • Zhou, J. & Troyanskaya, O.G. Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12, 931–934 (2015).
  • Quang, D. & Xie, X. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res. 44, e107 (2016).
  • Angermueller, C., Lee, H.J., Reik, W. & Stegle, O. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol. 18, 67 (2017).
  • Zhang, S., Hu, H., Jiang, T., Zhang, L. & Zeng, J. TITER: predicting translation initiation sites by deep learning. Bioinformatics 33, i234–i242 (2017).
  • Ma, J. et al. Using deep learning to model the hierarchical structure and function of a cell. Nat. Methods 15, 290–298 (2018).
  • Duvenaud, D. et al. Convolutional networks on graphs for learning molecular fingerprints. Preprint at https://arxiv.org/abs/1509.09292 (2015)
  • Ramsundar, B. et al. Massively multitask networks for drug discovery. Preprint at https://arxiv.org/abs/1502.02072 (2015).
  • Wallach, I., Dzamba, M. & Heifets, A. AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery. Preprint at https://arxiv. org/abs/1510.02855 (2015).
  • Liu, Y. et al. Detecting cancer metastases on gigapixel pathology images. Preprint at https://arxiv.org/abs/1703.02442 (2017).
  • Wang, D., Khosla, A., Gargeya, R., Irshad, H. & Beck, A.H. Deep learning for identifying metastatic breast cancer. Preprint at https://arxiv.org/abs/1606.05718 (2016).
  • Lakhani, P. & Sundaram, B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284, 574–582 (2017)
  • Kraus, O.Z. et al. Automated analysis of high-content microscopy data with deep learning. Mol. Syst. Biol. 13, 924 (2017).
  • Carpenter, A.E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).
  • Bruno, M.A., Walker, E.A. & Abujudeh, H.H. Understanding and confronting our mistakes: the epidemiology of error in radiology and strategies for error reduction. Radiographics 35, 1668–1676 (2015).
  • Leinonen, R., Sugawara, H. & Shumway, M. The Sequence Read Archive. Nucleic Acids Res. 39, D19–D21 (2011).
  • Mamoshina P, Vieira A, Putin E, Zhavoronkov A. Applications of Deep Learning in Biomedicine. Mol Pharm. 2016;13(5):1445-1454. doi:10.1021/acs.molpharmaceut.5b00982
  • Cao C, Liu F, Tan H, et al. Deep Learning and Its Applications in Biomedicine. Genomics Proteomics Bioinformatics. 2018;16(1):17-32. doi:10.1016/j.gpb.2017.07.003
  • Nussinov, R. Advancements and Challenges in Computational Biology. PLoS Comput. Biol. 2015, 11 (1), e1004053.
  • Mnih, V.; Kavukcuoglu, K.; Silver, D.; Rusu, A.; Veness, J.; Bellemare, M.; Graves, A.; Riedmiller, M.; Fidjeland, A.; Ostrovski, G.; Petersen, S.; Beattie, C.; Sadik, A.; Antonoglou, I.; King, H.; Kumaran, D.; Wierstra, D.; Legg, S.; Hassabis, D. Human-Level Control through Deep Reinforcement Learning. Nature 2015, 518 (7540), 529−533.
  • Schmidhuber, J. Deep Learning in Neural Networks: An Overview. Neural Networks 2015, 61, 85−117.
  • Bakhtiar, R. Biomarkers in Drug Discovery and Development. J. Pharmacol. Toxicol. Methods 2008, 57 (2), 85−91.
  • Lezhnina, K.; Kovalchuk, O.; Zhavoronkov, A. A.; Korzinkin, M. B.; Zabolotneva, A. A.; Shegay, P. V.; Sokov, D. G.; Gaifullin, N. M.; Rusakov, I. G.; Aliper, A. M.; Roumiantsev, S. A.; Alekseev, B. Y.; Borisov, N. M.; Buzdin, A. A. Novel Robust Biomarkers for Human Bladder Cancer Based on Activation of Intracellular Signaling Pathways. Oncotarget 2014, 5 (19), 9022−9032.
  • Jarvinen, A.-K.; Hautaniemi, S.; Edgren, H.; Auvinen, P.; Saarela, ̈ J.; Kallioniemi, O.-P.; Monni, O. Are Data from Different Gene Expression Microarray Platforms Comparable? Genomics 2004, 83 (6), 1164−1168
  • Hira, Z. M.; Gillies, D. F. A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data. Adv. Bioinf. 2015, 2015 (1), 198363.
  • Buzdin, A. A.; Zhavoronkov, A. A.; Korzinkin, M. B.; Roumiantsev, S. A.; Aliper, A. M.; Venkova, L. S.; Smirnov, P. Y.; Borisov, N. M. The OncoFinder Algorithm for Minimizing the Errors Introduced by the High-Throughput Methods of Transcriptome Analysis. Front. Mol. Biosci. 2014, DOI: 10.3389/fmolb.2014.00008.
  • Ibrahim, R.; Yousri, N. A.; Ismail, M. A.; El-Makky, N. M. MultiLevel gene/MiRNA Feature Selection Using Deep Belief Nets and Active Learning. Eng. Med. Biol. Soc. (EMBC), 2014 36th Annu. Int. Conf. IEEE 2014, 3957−3960.
  • Fakoor, R.; Huber, M. Using Deep Learning to Enhance Cancer Diagnosis and Classification. In Proceeding 30th Int. Conf. Mach. Learn. Atlanta, GA, 2013, Vol. 28
  • Jones, A. L. Segmenting Microarrays with Deep Neural Networks 2015, DOI: 10.1101/020404.
  • Zeng, T.; Li, R.; Mukkamala, R.; Ye, J.; Ji, S. Deep Convolutional Neural Networks for Annotating Gene Expression Patterns in the Mouse Brain. BMC Bioinf. 2015, 16 (1), 147.
  • Xiong, H. Y.; Alipanahi, B.; Lee, L. J.; Bretschneider, H.; Merico, D.; Yuen, R. K. C.; Hua, Y.; Gueroussov, S.; Najafabadi, H. S.; Hughes, T. R.; Morris, Q.; Barash, Y.; Krainer, Ad. R.; Jojic, N.; Scherer, S. W.; Blencowe, B. J.; Frey, B. J. The human splicing code reveals new insights into the genetic determinants of disease. Science 2015, 347 (6218), 1254806.
  • Leung, M. K. K.; Xiong, H. Y.; Lee, L. J.; Frey, B. J. Deep Learning of the Tissue-Regulated Splicing Code. Bioinformatics 2014, 30 (12), i121−i129.
  • Cech, T. R.; Steitz, J. A. The Noncoding RNA Revolution-Trashing Old Rules to Forge New Ones.pdf. Cell 2014, 157 (1), 77− 94.
  • Fan, X.-N.; Zhang, S.-W. lncRNA-MFDL: Identification of Human Long Non-Coding RNAs by Fusing Multiple Features and Using Deep Learning. Mol. BioSyst. 2015, 11 (3), 892−897
  • Witteveen, M. J. Identification and Elucidation of Expression Quantitative Trait Loci (eQTL) and Their Regulating Mechanisms Using Decodive Deep Learning; 2014; pp 1−17.
  • Chen, L.; Cai, C.; Chen, V.; Lu, X. Trans-Species Learning of Cellular Signaling Systems with Bimodal Deep Belief Networks. Bioinformatics 2015, 31, 3008−3015.
  • Spencer, M.; Eickholt, J.; Cheng, J. A Deep Learning Network Approach to ab Initio Protein Secondary Structure Prediction. IEEE/ ACM Trans. Comput. Biol. Bioinf. 2015, 12 (1), 103−112.
  • Di Lena, P.; Nagata, K.; Baldi, P. Deep Architectures for Protein Contact Map Prediction. Bioinformatics 2012, 28 (19), 2449−2457.
  • Eickholt, J.; Cheng, J. DNdisorder: Predicting Protein Disorder Using Boosting and Deep Networks. BMC Bioinf. 2013, 14 (1), 88.
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  • Zhang, S.; Zhou, J.; Hu, H.; Gong, H.; Chen, L.; Cheng, C.; Zeng, J. A Deep Learning Framework for Modeling Structural Features of RNA-Binding Protein Targets. Nucleic Acids Res. 2016, 44 (4), e32
  • Schirle, M.; Jenkins, J. L. Identifying Compound Efficacy Targets in Phenotypic Drug Discovery. Drug Discovery Today 2015, 21 (1), 82.
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  • Xu, Y.; Dai, Z.; Chen, F.; Gao, S.; Pei, J.; Lai, L. Deep Learning for Drug-Induced Liver Injury. J. Chem. Inf. Model. 2015, 55, 2085− 2093.
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  • Alipanahi, B.; Delong, A.; Weirauch, M. T.; Frey, B. J. Predicting the Sequence Specificities of DNA- and RNA-Binding Proteins by Deep Learning. Nat. Biotechnol. 2015, 33, 831−838.
  • Zhou, J.; Troyanskaya, O. G. Predicting Effects of Noncoding Variants with Deep Learning−based Sequence Model. Nat. Methods 2015, 12 (10), 931−934.
  • Kelley, D. R.; Snoek, J.; Rinn, J. Basset: Learning the Regulatory Code of the Accessible Genome with Deep Convolutional Neural Networks; 2015.
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  • Ashburner, M.; Ball, C. A.; Blake, J. A.; Botstein, D.; Butler, H.; Cherry, J. M.; Davis, A. P.; Dolinski, K.; Dwight, S. S.; Eppig, J. T.; Harris, M. A.; Hill, D. P.; Issel-Tarver, L.; Kasarskis, A.; Lewis, S.; Matese, J. C.; Richardson, J. E.; Ringwald, M.; Rubin, G. M.; Sherlock, G. Gene Ontology: Tool for the Unification of Biology. Nat. Genet. 2000, 25 (1), 25−29.
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Dataset

Framework & Model

  • MedicalNet: a Pytorch implementation of Med3D: Transfer Learning for 3D Medical Image Analysis.
  • MONAI: a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of PyTorch Ecosystem.
  • NiftyNet: a TensorFlow-based open-source convolutional neural networks (CNN) platform for research in medical image analysis and image-guided therapy

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A curated list of resources of machine learning related datasets and open-source models for biomedicine.

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bioinformatics deep-learning medcine

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