Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks
暂无分享,去创建一个
[1] 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..
[2] Zhangxin Chen,et al. ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network , 2017, Molecules.
[3] Jari Björne,et al. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens , 2019, Genome Biology.
[4] Daniel W. A. Buchan,et al. A large-scale evaluation of computational protein function prediction , 2013, Nature Methods.
[5] Tapio Salakoski,et al. An expanded evaluation of protein function prediction methods shows an improvement in accuracy , 2016, Genome Biology.
[6] Heng Huang,et al. Semi-Supervised Generative Adversarial Network for Gene Expression Inference , 2018, KDD.
[7] James Zou,et al. Feedback GAN for DNA optimizes protein functions , 2019, Nature Machine Intelligence.
[8] 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).
[9] Yi Xiong,et al. GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank , 2017, bioRxiv.
[10] Ye Wang,et al. Synthetic promoter design in Escherichia coli based on a deep generative network , 2020, Nucleic acids research.
[11] Concetto Spampinato,et al. Semi Supervised Semantic Segmentation Using Generative Adversarial Network , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[12] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[13] Pieter Abbeel,et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.
[14] Guang Yang,et al. DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction , 2018, IEEE Transactions on Medical Imaging.
[15] Raymond Y. K. Lau,et al. Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[16] Damiano Piovesan,et al. FFPred 2.0: Improved Homology-Independent Prediction of Gene Ontology Terms for Eukaryotic Protein Sequences , 2013, PloS one.
[17] Heng Huang,et al. Conditional generative adversarial network for gene expression inference , 2018, Bioinform..
[18] Fengzhu Sun,et al. NetGO: improving large-scale protein function prediction with massive network information , 2019, Nucleic acids research.
[19] Silvio Savarese,et al. Adversarial Feature Augmentation for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[20] Nicholas M. Luscombe,et al. Generative adversarial networks simulate gene expression and predict perturbations in single cells , 2018, bioRxiv.
[21] Zengchang Qin,et al. Emotion Classification with Data Augmentation Using Generative Adversarial Networks , 2018, PAKDD.
[22] Alexei A. Efros,et al. Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[24] Nicholas M. Luscombe,et al. Generative adversarial networks simulate gene expression and predict perturbations in single cells , 2018, bioRxiv.
[25] Luca Ambrogioni,et al. Generative adversarial networks for reconstructing natural images from brain activity , 2017, NeuroImage.
[26] Alexei A. Efros,et al. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[27] 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.
[28] Fernando Nogueira,et al. Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning , 2016, J. Mach. Learn. Res..
[29] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[30] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[31] 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).
[32] David T. Jones,et al. Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks , 2018, bioRxiv.
[33] David Lopez-Paz,et al. Revisiting Classifier Two-Sample Tests , 2016, ICLR.
[34] Rui Fa,et al. Predicting human protein function with multi-task deep neural networks , 2018, bioRxiv.
[35] 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.
[36] Valeria Vitelli,et al. Probabilistic preference learning with the Mallows rank model , 2014, J. Mach. Learn. Res..
[37] David T Jones,et al. Computational Methods for Annotation Transfers from Sequence. , 2016, Methods in molecular biology.
[38] Hayit Greenspan,et al. GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification , 2018, Neurocomputing.
[39] Pierre Machart,et al. Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks , 2020, Nature Communications.