Improving protein function prediction with synthetic feature samples created by generative adversarial networks
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[1] 拓海 杉山,et al. “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .
[2] Hayit Greenspan,et al. GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification , 2018, Neurocomputing.
[3] Concetto Spampinato,et al. Semi Supervised Semantic Segmentation Using Generative Adversarial Network , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[4] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[5] Silvio Savarese,et al. Adversarial Feature Augmentation for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[6] Xiaowo Wang,et al. Synthetic Promoter Design in Escherichia coli based on Generative Adversarial Network , 2019 .
[7] David T Jones,et al. Computational Methods for Annotation Transfers from Sequence. , 2016, Methods in molecular biology.
[8] Alexei A. Efros,et al. Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Christian Ledig,et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] David Lopez-Paz,et al. Revisiting Classifier Two-Sample Tests , 2016, ICLR.
[11] Rui Fa,et al. Predicting human protein function with multi-task deep neural networks , 2018, bioRxiv.
[12] Pierre Machart,et al. Realistic in silico generation and augmentation of single cell RNA-seq data using Generative Adversarial Neural Networks , 2018, bioRxiv.
[13] F. McCoy,et al. Janus-faced PIDD: a sensor for DNA damage-induced cell death or survival? , 2012, Molecular cell.
[14] Pieter Abbeel,et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.
[15] Heng Huang,et al. Semi-Supervised Generative Adversarial Network for Gene Expression Inference , 2018, KDD.
[16] James Zou,et al. Feedback GAN for DNA optimizes protein functions , 2019, Nature Machine Intelligence.
[17] Gregory D. Hager,et al. Adversarial deep structured nets for mass segmentation from mammograms , 2017, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[18] Nicholas M. Luscombe,et al. Generative adversarial networks simulate gene expression and predict perturbations in single cells , 2018, bioRxiv.
[19] Heng Huang,et al. Conditional generative adversarial network for gene expression inference , 2018, Bioinform..
[20] Luca Ambrogioni,et al. Generative adversarial networks for reconstructing natural images from brain activity , 2017, NeuroImage.
[21] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[22] Damiano Piovesan,et al. FFPred 2.0: Improved Homology-Independent Prediction of Gene Ontology Terms for Eukaryotic Protein Sequences , 2013, PloS one.
[23] Lin Yang,et al. Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[24] Daniel W. A. Buchan,et al. A large-scale evaluation of computational protein function prediction , 2013, Nature Methods.
[25] Guang Yang,et al. DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction , 2018, IEEE Transactions on Medical Imaging.
[26] Raymond Y. K. Lau,et al. Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[27] Christine A. Orengo,et al. Analysis of temporal transcription expression profiles reveal links between protein function and developmental stages of Drosophila melanogaster , 2017, PLoS Comput. Biol..
[28] Nicholas M. Luscombe,et al. Generative adversarial networks simulate gene expression and predict perturbations in single cells , 2018, bioRxiv.
[29] Danna Zhou,et al. d. , 1934, Microbial pathogenesis.
[30] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[31] Jung-Woo Ha,et al. StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[32] Léon Bottou,et al. Wasserstein GAN , 2017, ArXiv.
[33] Zengchang Qin,et al. Emotion Classification with Data Augmentation Using Generative Adversarial Networks , 2018, PAKDD.
[34] Rui Fa,et al. Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks , 2018 .
[35] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[36] Tapio Salakoski,et al. An expanded evaluation of protein function prediction methods shows an improvement in accuracy , 2016, Genome Biology.