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ORCIDNeurocomputing, Volume 324
Volume 324, January 2019
- Fang-Xiang Wu
, Min Li:
Deep learning for biological/clinical data. 1-2 - Leyi Wei, Ran Su, Bing Wang, Xiuting Li, Quan Zou, Xing Gao:
Integration of deep feature representations and handcrafted features to improve the prediction of N6-methyladenosine sites. 3-9 - Long Zhang, Guoxian Yu, Dawen Xia, Jun Wang:
Protein-protein interactions prediction based on ensemble deep neural networks. 10-19 - Yang Guo, Xuequn Shang, Zhanhuai Li:
Identification of cancer subtypes by integrating multiple types of transcriptomics data with deep learning in breast cancer. 20-30 - Ye Yuan, Guangxu Xun, Qiuling Suo, Kebin Jia, Aidong Zhang:
Wave2Vec: Deep representation learning for clinical temporal data. 31-42 - Min Zeng, Min Li, Zhihui Fei, Ying Yu, Yi Pan, Jianxin Wang:
Automatic ICD-9 coding via deep transfer learning. 43-50 - Yifeng Li, François Fauteux, Jinfeng Zou, André Nantel, Youlian Pan:
Personalized prediction of genes with tumor-causing somatic mutations based on multi-modal deep Boltzmann machine. 51-62 - Yazhou Kong, Jianliang Gao, Yunpei Xu, Yi Pan, Jianxin Wang, Jin Liu:
Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier. 63-68 - Di Wu, Si-Jia Zheng, Wenzheng Bao, Xiao-Ping (Steven) Zhang, Chang-An Yuan, De-Shuang Huang:
A novel deep model with multi-loss and efficient training for person re-identification. 69-75
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