[1] Shen Y, Zhang L, Zhang J, et al. CBN:constructing a clinical Bayesian network based on data from the electronic medical record[J]. Journal of Biomedical Informatics, 2018, 88:1-10. [2] Gao X T, Stephen L, Wong Y T. Automatic feature learning to grade nuclear cataracts based on deep learning[J]. IEEE Transactions on Biomedical Engineering, 2015, 62(11):2693-2701. [3] Cirean D C, Giusti A, Gambardella L M, et al. Mitosis detection in breast cancer histology images with deep neural networks[C]//2013 International Conference on Medical Image Computing and Computer-Assisted Intervention, 2018:1-8. [4] Suk H I, Lee S W, Shen D, et al. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis[J]. Neuroimage, 2014, 101:569-582. [5] Ithapu V K, Singh V, Okonkwo O C, et al. Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment[J]. The Journal of the Alzheimers Association, 2015, 11(12):1489-1499. [6] Suk H II, Lee S W, Shen D. Deep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis[J]. Brain Structure & Function, 2016, 221(5):2569-2587. [7] Suk H I, Lee S W, Shen D. Latent feature representation with stacked auto-encoder for AD/MCI diagnosis[J]. Brain Structure & Function, 2013, 220(2):841-859. [8] Wee C Y, Liu C, Lee A, et al. Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations[J]. NeuroImage:Clinical, 2019, 23:19-29. [9] Agah A. Medical applications of artificial intelligence[EB/OL]. (2013-10-30)[2022-10-15]. https://doi.org/10.1201/b15618. [10] Xu J, Lei X, Hang R L, et al. Stacked sparse autoencoder for nuclei detection on breast cancer histopathology images[J]. IEEE Transactions on Medical Imaging, 35(1):19-30. [11] Fakoor R, Ladhak F, Nazi A, et al. Using deep learning to enhance cancer diagnosis and classification[R/OL]. (2013-06-10)[2022-10-15]. https://www.researchgate.net/publication/281857285_Using_deep_learning_to_enhance_cancer_diagnosis_and_classification. [12] Zuluaga G J, Almasry Z, Benaggoune K, et al. A CNN-based methodology for breast cancer diagnosis using thermal images[J]. Computer Methods in Biomechanics and Biomedical Engineering:Imaging & Visualization, 2021, 9(2):131-145. [13] Shamshirband S, Fathi M, Dehzangi A, et al. A review on deep learning approaches in healthcare systems:taxonomies, challenges, and open issues[J]. Journal of Biomedical Informatics, 2021, 113:27-36. [14] Liu G P, Yan J J, Wang Y Q, et al. Deep learning based syndrome diagnosis of chronic gastritis[J]. Computational and Mathematical Methods in Medicine, 2014:1-8. [15] Ikenoyama Y, Hirasawa T, Ishioka M, et al. Detecting early gastric cancer:comparison between the diagnostic ability of convolutional neural networks and endoscopists[J]. Digestive Endoscopy, 2021, 33(1):141-150. [16] Kumar D, Wong A, Clausi D A. Lung nodule classification using deep features in CT images[J]. Wireless Personal Communications, 2015, 116(2021):655-690. [17] Ma J, Sheridan R P, Liaw A, et al. Deep neural nets as a method for quantitative structure-activity relationships[J]. Journal of Chemical Information & Modeling, 2015, 55(2):263-274. [18] Bhinder B, Gilvary C, Madhukar N S, et al. Artificial intelligence in cancer research and precision medicine[J]. Cancer Discovery, 2021, 11(4):900-915. [19] Prasoon A, Petersen K, Igel C, et al. Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network[C]//2013 International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Heidelberg:Springer, 2013:246-253. [20] Song Y, Zhang L, Chen S, et al. A deep learning based framework for accurate segmentation of cervical cytoplasm and nuclei[C]//The 36th Annual International Conference of IEEE Engineering in Medicine and Biology Society, 2014:2903. [21] Majumdar A. Real-time dynamic MRI reconstruction using stacked denoising autoencoder[R/OL]. (2015-03-22)[2022-10-15]. https://arxiv.org/ftp/arxiv/papers/1503/1503.06383.pdf. [22] Chaudhari A S, Sandino C M, Cole E K, et al. Prospective deployment of deep learning in MRI:a framework for important considerations, challenges, and recommendations for best practices[J]. Journal of Magnetic Resonance Imaging, 2021, 54(2):357-371. [23] Zhang W, Li R, Deng H, et al. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation[J]. Neuroimage, 2015, 108:214-224. [24] Zeng T, Li R J, Mukkamala R, et al. Deep convolutional neural networks for annotating gene expression patterns in the mouse brain[J]. BMC Bioinformatics, 2015, 16:147-157. [25] Wang D, Shang Y. Modeling physiological data with deep belief networks[J]. International Journal of Information and Education Technology, 2013, 3(5):505-511. [26] Spencer M, Eickholt J, Jianlin C. A deep learning network approach to ab initio protein secondary structure prediction[J]. IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2015, 12(1):103-112. [27] Heffernan R, Paliwal K, Lyons J, et al. Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning[J]. Scientific Reports, 2015, 5:47-53. [28] Zhou J, Troyanskaya O G. Deep supervised and convolutional generative stochastic network for protein secondary structure prediction[J]. Journal of Machine Learning Research, 2014, 32(1):745-753. [29] Lena P D, Nagata K, Baldi P. Deep architectures for protein contact map prediction[J]. Bioinformatics, 2012, 28(19):2449-2457. [30] Yuvaraj N, Srihari K, Chandragandhi S, et al. Analysis of protein-ligand interactions of SARS-Cov-2 against selective drug using deep neural networks[J]. Big Data Mining and Analytics, 2021, 4(2):76-83. [31] Quang D, Chen Y, Xie X. DANN:a deep learning approach for annotating the pathogenicity of genetic variants[J]. Bioinformatics, 2014, 31(5):761-763. [32] Li J, Pu Y, Tang J, et al. A hybrid category attention neural network for identifying functional effects of DNA sequences[J]. Briefings in Bioinformatics, 2021, 22(3):159-170. [33] Leung M K K, Xiong H Y, Lee L J, et al. Deep learning of the tissue-regulated splicing code[J]. Bioinformatics, 2014, 30(12):121-129. [34] Xiong H Y, Alipanahi B, Lee L J, et al. The human splicing code reveals new insights into the genetic determinants of disease[J]. Science, 2015, 347(6218):1254806. [35] Bhattacharjee S, Ghosh A, Saha B, et al. Machine learning and systems biology in genomics and health[M].[S.l.]:Springer, 2022:69-90. [36] Tibshirani R, Walther G, Hastie T. Estimating the number of clusters in a data set via the gap statistic[J]. Journal of the Royal Statistical Society:Series B (Statistical Methodology), 2001, 63(2):411-423. |