Cancer is a well-known killer of human beings, which has led to countless deaths and misery. Anticancer peptides open a promising perspective for cancer treatment, and they have various attractive advantages. Conventional wet experiments are expensive and inefficient for finding and identifying novel anticancer peptides. There is an urgent need to develop a novel computational method to predict novel anticancer peptides. In this study, we propose a deep learning long short-term memory (LSTM) neural network model, ACP-DL, to effectively predict novel anticancer peptides. More specifically, to fully exploit peptide sequence information, we developed an efficient feature representation approach by integrating binary profile feature and k-mer sparse matrix of the reduced amino acid alphabet. Then we implemented a deep LSTM model to automatically learn how to identify anticancer peptides and nonanticancer peptides. To our knowledge, this is the first time that the deep LSTM model has been applied to predict anticancer peptides. It was demonstrated by cross-validation experiments that the proposed ACP-DL remarkably outperformed other comparison methods with high accuracy and satisfied specificity on benchmark datasets. In addition, we also contributed two new anticancer peptides benchmark datasets, ACP740 and ACP240, in this work. The source code and datasets are available at https://github.com/haichengyi/ACP-DL.

As two different tools for earth observation, the optical and synthetic aperture radar (SAR) images can provide complementary information of the same land types for better land cover classification. However, because of the different imaging mechanisms of optical and SAR images, how to efficiently exploit the complementary information becomes an interesting and challenging problem. In this article, we propose a novel multimodal bilinear fusion network (MBFNet), which is used to fuse the optical and SAR features for land cover classification. The MBFNet consists of three components: the feature extractor, the second-order attention-based channel selection module (SACSM), and the bilinear fusion module. First, in order to avoid the network parameters tempting to ingratiate dominant modality, the pseudosiamese convolutional neural network (CNN) is taken as the feature extractor to extract deep semantic feature maps of optical and SAR images, respectively. Then, the SACSM is embedded into each stream, and the fine channel-attention maps with second-order statistics are obtained by bilinear integrating the global averagepooling and global max-pooling information. The SACSM can not only automatically highlight the important channels of feature maps to improve the representation power of networks, but also uses the channel selection mechanism to reconfigure compact feature maps with better discrimination. Finally, the bilinear pooling is used as the feature-level fusion method, which establishes the second-order association between two compact feature maps of the optical and SAR streams to obtain the low-dimension bilinear fusion features for land cover classification. Experimental results on three broad coregistered optical and SAR datasets demonstrate that our method achieves more effective land cover classification performance than the state-of-the-art methods. Index Terms-Attention mechanism, bilinear pooling model, convolutional neural network (CNN), feature fusion, land cover classification, multimodal learning. Xiao Li received the M.S. degrees in control science and engineering from Xiangtan University, Xiangtan, China, in 2018. He is currently working toward the Ph.D. degree in information and communication engineering from the

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