Gene-CWGAN: một phương pháp tăng cường dữ liệu cho hồ sơ biểu hiện gen dựa trên CWGAN-GP cải tiến

Wang Y, Li X, Ruiz R (2019) Weighted general group lasso for gene selection in cancer classification. IEEE Transactions Cybern 49(8):2860–2873 Benjamin LE, Todd RG (2004) Genomic approaches to hematologic malignancies. Blood 104(4):923–932 Nguyen T, Nahavandi S (2016) Modified AHP for gene selection and cancer classification using type-2 fuzzy logic. IEEE Trans Fuzzy Syst 24(2):273–287 Mallick BK, Ghosh D, Ghosh M (2005) Bayesian classification of tumours by using gene expression data. J Roy Stat Soc 67(2):219–234 Ghosh SK, Ghosh A (2021) Classification of gene expression patterns using a novel type-2 fuzzy multigranulation-based SVM model for the recognition of cancer mediating biomarkers. Neural Comput Appl 33:4263–4281 Sun Z, Wang H, Lau W, Seet G, Wang D, Lam K (2014) Microarray data classification using the spectral-feature-based TLS ensemble algorithm. IEEE Trans Nanobiosci 13(3):289–299 Mondal M, Semwal R, Raj U et al (2020) An entropy-based classification of breast cancerous genes using microarray data. Neural Comput Appl 32:2397–2404 Nagpal A, Singh V (2018) Identification of significant features using random forest for high dimensional microarray data. J Eng Technol 13(8):2446–2463 Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machine. Mach Learn 46(1):389–422 Zhou X, Mao KZ (2005) LS bound based gene selection for DNA microarray data. Bioinformatics 21(8):1559–1564 Han F, Chen WT, Ling QH, Han H (2021) Multi-objective particle swarm optimization with adaptive strategies for feature selection. Swarm Evol Comput 62:100847 Nagra AA, Han F, Ling QH, Abubaker M et al (2020) Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems. Connect Sci 32(1):16–36 Dwivedi AK (2018) Artificial neural network model for effective cancer classification using microarray gene expression data. Neural Comput Appl 29:1545–1554 Shah SH, Iqbal MJ, Ahmad I et al (2020) Optimized gene selection and classification of cancer from microarray gene expression data using deep learning. Neural Comput Appli. https://doi.org/10.1007/s00521-020-05367-8 Mudiyanselage BTK, Xiao X, Zhang Y, Pan Y (2020) Deep fuzzy neural networks for biomarker selection for accurate cancer detection. IEEE Trans Fuzzy Syst 28(12):3219–3228 Elbashir MK, Ezz M, Mohammed M, Saloum SS (2019) Lightweight convolutional neural network for breast cancer classification using RNA-seq gene expression data. IEEE Access 7:185338–185348 Park S, Hao W, Leung CS (2012) Reconstruction of uniformly sampled sequence from nonuniformly sampled transient sequence using symmetric extension. IEEE Trans Signal Process 60(3):1498–1501 Eldar YC (2006) Mean-squared error sampling and reconstruction in the presence of noise. IEEE Trans Signal Process 54(12):4619–4633 Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16(1):321–357 Han H, Wang WY, Mao BH (2005) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. Lect Notes Comput Sci 3644:878–887 Liang XW, Jiang AP, Li T, Xue YY, Wang GT (2020) LR-SMOTE — an improved unbalanced data set oversampling based on K-means and SVM. Knowl-Based Syst 196:105845 Li JN, Zhu QS, Wu QW, Zhang ZY, Gong YL et al (2021) SMOTE-NAN-DE: addressing the noisy and borderline examples problem in imbalanced classification by natural neighbors and differential evolution Knowl-Based Syst 223: 107056 Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets Adv Neural Information Process Syst pp 2672–2680 Mirza M, Osindero S (2014) Conditional generative adversarial nets. Comput Sci 156:2672–2680 Huang YF, Liu WD (2021) Choreography CGAN: generating dances with music beats using conditional generative adversarial networks. Neural Comput Appl 33:9817–9833 Ma Y, Zhong G, Liu W et al (2021) ML-CGAN: conditional generative adversarial network with a meta-learner structure for high-quality image generation with few training data. Cogn Comput 13:418–430 Chen LY, Liu YF, Xiao WD, Wang YX, Xie HY (2020) SpeakerGAN: speaker identification with conditional generative adversarial network. Neurocomputing 418(22):211–220 Lee MB, Kim YH, Park KR (2019) Conditional generative adversarial network- based data augmentation for enhancement of iris recognition accuracy. IEEE Access 7:122134–122152 Wang M, Zhu W, Yu K, Chen ZY, Shi F et al (2021) Semi-supervised capsule cgan for speckle noise reduction in retinal OCT images. IEEE Trans Med Imaging 40(4):1168–1183 Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks In: Proceedings of the 25th international conference on Machine learning (ICML) pp 214–223 Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks In: Proceeding of international conference on learning representations arXiv:1511.06434 Mao X, Li Q, Xie H, Lau RY, Wang Z, Paul Smolley S (2017) Least squares generative adversarial networks In: Proceedings of the IEEE international conference on computer vision, pp 2794–2802 Berthelot D, Schumm T, Metz L (2017) Began: boundary equilibrium generative adversarial networks arXiv:1703.10717 Lin Z, Khetan A, Fanti G, Oh S (2020) PacGAN: the power of two samples in generative adversarial networks. IEEE J Select Areas Information Theory 1(1):324–335 Huang ZX, Liu XF, Wang RP, Chen JC et al (2021) Considering anatomical prior information for low-dose CT image enhancement using attribute-augmented Wasserstein generative adversarial networks. Neurocomputing 428(7):104–115 Hu SY, Lei BY, Wang SQ, Feng ZG et al (2022) Bidirectional mapping generative adversarial networks for brain MR to PET synthesis. IEEE Trans Med Imaging 41(1):145–157 Yu W, Lei BY, Ng MK, Cheung AC et al (2022) Tensorizing GAN with high-order pooling for alzheimer’s disease assessment. IEEE Transactions Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2021.3063516 Wang SQ, Wang XY, Hu Y, Shen YY et al (2021) Diabetic retinopathy diagnosis using multichannel generative adversarial network with semisupervision. IEEE Trans Autom Sci Eng 18(2):574–585 Zhu SJ, Han F (2021) A data enhancement method for gene expression profile based on improved WGAN-GP In: Proceedings of the 2nd International Conference on Neural Computing for Advanced Applications (NCAA 2021), pp 242–254 Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville A (2017) Improved training of wasserstein gans Adv Neural Information Process Syst (NIPS) 5767–5777 Panwar RP, Jung TP, Huang Y (2020) Modeling EEG data distribution with a Wasserstein generative adversarial network to predict RSVP events. IEEE Trans Neural Syst Rehabil Eng 28(8):1720–1730 Ma W, Pan ZX, Yuan F, Lei B (2019) Super-resolution of remote sensing images via a dense residual generative adversarial network. Remote Sens 11(21):2578 Gao X, Deng F, Yue XH (2020) Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty. Neurocomputing 396:487–494 Zheng M, Li T, Zhu R, Tang YH et al (2020) Conditional Wasserstein generative adversarial network-gradient penalty-based approach to alleviating imbalanced data classification. Inf Sci 512:1009–1023 Yan K, Su J, Huang J, Mo YC (2020) Chiller fault diagnosis based on VAE-enabled generative adversarial networks. IEEE Trans Autom Sci Eng 19(1):387–395 Zhu ZX, Ong YS, Dash M (2007) Markov blanket-embedded genetic algorithm for gene selection. Pattern Recogn 40(11):3236–3248 Genuer R, Poggi JM, Tuleau-Malot C (2010) Variable selection using random forests. Pattern Recogn Lett 31(14):2225–2236 Kodali N, Abernethy J, Hays J, Kira Z (2017) On convergence and stability of GANs. arXiv:1705.07215 Mao XD, Li Q, Xie HR, Lau RYK et al (2019) On the effectiveness of least squares generative adversarial networks. IEEE Trans Pattern Anal Mach Intell 41(12):2947–2960