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Motivation: DNA N6-methyladenine (6mA) is a pivotal DNA modification for various biological processes. More accurate prediction of 6mA methylation sites plays an irreplaceable part in grasping the internal rationale of related biological activities. However, the existing prediction methods only extract information from a single dimension, which has some limitations. Therefore, it is very necessary to obtain the information of 6mA sites from different dimensions, so as to establish a reliable prediction method., Results: In this study, a neural network based bioinformatics model named GC6mA-Pred is proposed to predict N6-methyladenine modifications in DNA sequences. GC6mA-Pred extracts significant information from both sequence level and graph level. In the sequence level, GC6mA-Pred uses a three-layer convolution neural network (CNN) model to represent the sequence. In the graph level, GC6mA-Pred employs graph neural network (GNN) method to integrate various information contained in the chemical molecular formula corresponding to DNA sequence. In our newly built dataset, GC6mA-Pred shows better performance than other existing models. The results of comparative experiments have illustrated that GC6mA-Pred is capable of producing a marked effect in accurately identifying DNA 6mA modifications., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Motivation: DNA methylation plays an important role in epigenetic modification, the occurrence, and the development of diseases. Therefore, identification of DNA methylation sites is critical for better understanding and revealing their functional mechanisms. To date, several machine learning and deep learning methods have been developed for the prediction of different DNA methylation types. However, they still highly rely on manual features, which can largely limit the high-latent information extraction. Moreover, most of them are designed for one specific DNA methylation type, and therefore cannot predict multiple methylation sites in multiple species simultaneously. In this study, we propose iDNA-ABT, an advanced deep learning model that utilizes adaptive embedding based on Bidirectional Encoder Representations from Transformers (BERT) together with transductive information maximization (TIM)., Results: Benchmark results show that our proposed iDNA-ABT can automatically and adaptively learn the distinguishing features of biological sequences from multiple species, and thus perform significantly better than the state-of-the-art methods in predicting three different DNA methylation types. In addition, TIM loss is proven to be effective in dichotomous tasks via the comparison experiment. Furthermore, we verify that our features have strong adaptability and robustness to different species through comparison of adaptive embedding and six handcrafted feature encodings. Importantly, our model shows great generalization ability in different species, demonstrating that our model can adaptively capture the cross-species differences and improve the predictive performance. For the convenient use of our method, we further established an online webserver as the implementation of the proposed iDNA-ABT., Availability and Implementation: Our proposed iDNA-ABT and data are freely accessible via http://server.wei-group.net/iDNA_ABT and our source codes are available for downloading in the GitHub repository (https://github.com/YUYING07/iDNA_ABT)., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In this paper, we propose a novel multi-scale attention based network (called MSA-Net) for feature matching problems. Current deep networks based feature matching methods suffer from limited effectiveness and robustness when applied to different scenarios, due to random distributions of outliers and insufficient information learning. To address this issue, we propose a multi-scale attention block to enhance the robustness to outliers, for improving the representational ability of the feature map. In addition, we also design a novel context channel refine block and a context spatial refine block to mine the information context with less parameters along channel and spatial dimensions, respectively. The proposed MSA-Net is able to effectively infer the probability of correspondences being inliers with less parameters. Extensive experiments on outlier removal and relative pose estimation have shown the performance improvements of our network over current state-of-the-art methods with less parameters on both outdoor and indoor datasets. Notably, our proposed network achieves an 11.7% improvement at error threshold 5° without RANSAC than the state-of-the-art method on relative pose estimation task when trained on YFCC100M dataset. [ABSTRACT FROM AUTHOR]

Predicting the response of cancer patients to a particular treatment is a major goal of modern oncology and an important step toward personalized treatment. In the practical clinics, the clinicians prefer to obtain the most-suited drugs for a particular patient instead of knowing the exact values of drug sensitivity. Instead of predicting the exact value of drug response, we proposed a deep learning-based method, named Siamese Response Deep Factorization Machines (SRDFM) Network, for personalized anti-cancer drug recommendation, which directly ranks the drugs and provides the most effective drugs. A Siamese network (SN), a type of deep learning network that is composed of identical subnetworks that share the same architecture, parameters and weights, was used to measure the relative position (RP) between drugs for each cell line. Through minimizing the difference between the real RP and the predicted RP, an optimal SN model was established to provide the rank for all the candidate drugs. Specifically, the subnetwork in each side of the SN consists of a feature generation level and a predictor construction level. On the feature generation level, both drug property and gene expression, were adopted to build a concatenated feature vector, which even enables the recommendation for newly designed drugs with only chemical property known. Particularly, we developed a response unit here to generate weighted genetic feature vector to simulate the biological interaction mechanism between a specific drug and the genes. For the predictor construction level, we built this level integrating a factorization machine (FM) component with a deep neural network component. The FM can well handle the discrete chemical information and both low-order and high-order feature interactions could be sufficiently learned. Impressively, the SRDFM works well on both single-drug recommendation and synergic drug combination. Experiment result on both single-drug and synergetic drug data sets have shown the efficiency of the SRDFM. The Python implementation for the proposed SRDFM is available at at https://github.com/RanSuLab/SRDFM Contact: ran.su@tju.edu.cn , gbx@mju.edu.cn and weileyi@sdu.edu.cn. [ABSTRACT FROM AUTHOR]

Establishing reliable correspondences by a deep neural network is an important task in computer vision, and it generally requires permutation-equivariant architecture and rich contextual information. In this paper, we design a Permutation-Equivariant Split Attention Network (called PESA-Net), to gather rich contextual information for the feature matching task. Specifically, we propose a novel "Split–Squeeze–Excitation–Union" (SSEU) module. The SSEU module not only generates multiple paths to exploit the geometrical context of putative correspondences from different aspects, but also adaptively captures channel-wise global information by explicitly modeling the interdependencies between the channels of features. In addition, we further construct a block by fusing the SSEU module, Multi-Layer Perceptron and some normalizations. The proposed PESA-Net is able to effectively infer the probabilities of correspondences being inliers or outliers and simultaneously recover the relative pose by essential matrix. Experimental results demonstrate that the proposed PESA-Net relative surpasses state-of-the-art approaches for pose estimation and outlier rejection on both outdoor scenes and indoor scenes (i.e., YFCC100M and SUN3D). Source codes: https://github.com/x-gb/PESA-Net. • We propose an iterative network to establish reliable correspondences. • We develop an attention mechanism to capture rich contextual information. • We construct a novel block to exploit the global context of unordered data. • The proposed network achieves the state-of-the-art performance. [ABSTRACT FROM AUTHOR]

In this paper, we solve the problem of feature matching by designing an end-to-end network (called TSSN-Net). Given putative correspondences of feature points in two views, existing deep learning based methods formulate the feature matching problem as a binary classification problem. In these methods, a normalizer plays an important role in the networks. However, they adopt the same normalizer in all normalization layers of the entire networks, which will result in suboptimal performance. To address this problem, we propose a Two-step Sparse Switchable Normalization Block, which involves the advantage of adaptive normalization for different convolution layers from Sparse Switchable Normalization and robust global context information from Context Normalization. Moreover, to capture local information of correspondences, we propose a Multi-Scale Correspondence Grouping algorithm, by defining a multi-scale neighborhood representation, to search for consistent neighbors of each correspondence. Finally, with a series of convolution layers, the end-to-end TSSN-Net is proposed to learn correspondences with heavy outliers for feature matching. Our experimental results have shown that our network achieves the state-of-the-art performance on benchmark datasets. [ABSTRACT FROM AUTHOR]

In this article, we propose an infrared and visible image fusion network based on the salient target detection, termed STDFusionNet, which can preserve the thermal targets in infrared images and the texture structures in visible images. First, a salient target mask is dedicated to annotating regions of the infrared image that humans or machines pay more attention to, so as to provide spatial guidance for the integration of different information. Second, we combine this salient target mask to design a specific loss function to guide the extraction and reconstruction of features. Specifically, the feature extraction network can selectively extract salient target features from infrared images and background texture features from visible images, while the feature reconstruction network can effectively fuse these features and reconstruct the desired results. It is worth noting that the salient target mask is only required in the training phase, which enables the proposed STDFusionNet to be an end-to-end model. In other words, our STDFusionNet can fulfill salient target detection and key information fusion in an implicit manner. Extensive qualitative and quantitative experiments demonstrate the superiority of our fusion algorithm over the current state of the arts, where our algorithm is much faster and the fusion results look like high-quality visible images with clear highlighted infrared targets. Moreover, the experimental results on the public datasets reveal that our algorithm can improve the entropy (EN), mutual information (MI), visual information fidelity (VIF), and spatial frequency (SF) metrics with about 1.25%, 22.65%, 4.3%, and 0.89% gains, respectively. Our code is publicly available at https://github.com/jiayi-ma/STDFusionNet. [ABSTRACT FROM AUTHOR]

Feature matching aims to identify reliable correspondences between two sets of given initial feature points, which is of considerable importance to photogrammetry and computer vision. In this study, we propose an innovative sparse semantic learning-based network, named SSL-Net, for feature matching. Specifically, SSL-Net includes a novel sparsity constraint (SC) block, which builds a sparse graph for sparse semantic learning. The SC block adopts a region-to-whole learning strategy to measure the confidence of nodes in the sparse graph. It helps the sparse graph preserve the semantic information of positive influence while rejecting unnecessary ones, thereby suppressing the negative influence of incorrect correspondences. In addition, SSL-Net also includes a channel-spatial attention feature gathering block, which gathers features along the spatial direction and channel dimension of correspondences. To mitigate the existence of label ambiguity, we incorporate the accommodation factor into the loss function of SSL-Net for feature matching. As a result, our network outperforms the state-of-the-art method by a considerable margin. Notably, SSL-Net achieves a 9.05% improvement under an error threshold of 5 ° over the state-of-the-art method for the relative pose estimation task on the YFCC100M dataset. Our code will be available at https://github.com/guobaoxiao/SSL-Net. • We present a novel sparse semantic learning network for feature matching. • We introduce a channel-spatial attention feature gathering block to extract relevant information while suppress irrelevant context. • SSL-Net outperforms state-of-the-art methods for outlier removal and relative pose estimation. [ABSTRACT FROM AUTHOR]

• We design a three layer deep learning framework for outlier rejection. • We propose a novel joint representation attention mechanism. • We design an innovative weight function to improve the generalization ability. • Experimental results show the proposed network is superior to state of the art networks. In this paper, we propose a Joint Representation Attention Network (JRA-Net), an end-to-end network, to establish reliable correspondences for image pairs. The initial correspondences generated by the local feature descriptor usually suffer from heavy outliers, which makes the network unable to learn a powerful enough representation for distinguishing inliers and outliers. To this end, we design a novel attention mechanism. The proposed attention mechanism not only takes into account the correlations between global context and geometric information, but also introduces the joint representation of different scales to suppress trivial correspondences and highlight crucial correspondences. In addition, to improve the generalization ability of attention mechanism, we present an innovative weight function, to effectively adjust the importance of the attention mechanism in a learning manner. Finally, by combining the above components, the proposed JRA-Net is able to effectively infer the probabilities of correspondences being inliers. Empirical experiments on challenging datasets demonstrate the effectiveness and generalization of JRA-Net. We achieve remarkable improvements compared with the current state-of-the-art approaches on outlier rejection and relative pose estimation. [ABSTRACT FROM AUTHOR]

Multimodal image matching, which refers to identifying and then corresponding the same or similar structure/content from two or more images that are of significant modalities or nonlinear appearance difference, is a fundamental and critical problem in a wide range of applications, including medical, remote sensing and computer vision. An increasing number and diversity of methods have been proposed over the past decades, particularly in this deep learning era, due to the challenges in eliminating modality variance and geometrical deformation that intrinsically exist in multimodal image matching. However, a comprehensive review and analysis of traditional and recent trainable methods and their applications in different research fields are lacking. To this end and in this survey, we first introduce two general frameworks, saying area- and feature-based, in terms of their core components, taxonomy, and procedure details. Second, we provide a comprehensive review of multimodal image matching methods from handcrafted to deep methods for each research field according to their imaging nature, including medical, remote sensing and computer vision. Extensive experimental comparisons of interest point detection, description and matching, and image registration are performed on various datasets containing common types of multimodal image pairs that we collected and annotated. Finally, we briefly introduce and analyze several typical applications to reveal the significance of multimodal image matching and provide insightful discussions and conclusions to these multimodal image matching approaches, and simultaneously deliver their future trends for researchers and engineers in related research areas to achieve further breakthroughs. • We introduce two general matching frameworks and their extensions to multimodal case. • The multimodal matching methods and applications are comprehensively reviewed. • A multimodal benchmark containing 18 common types of data is constructed. • Extensive experiments on feature matching and image registration are performed. • Insightful discussions and prospects for future work are delivered. [ABSTRACT FROM AUTHOR]

Matrix factorization has always been an encouraging field, which attempts to extract discriminative features from high-dimensional data. However, it suffers from negative generalization ability and high computational complexity when handling large-scale data. In this paper, we propose a learnable deep matrix factorization via the projected gradient descent method, which learns multi-layer low-rank factors from scalable metric distances and flexible regularizers. Accordingly, solving a constrained matrix factorization problem is equivalently transformed into training a neural network with an appropriate activation function induced from the projection onto a feasible set. Distinct from other neural networks, the proposed method activates the connected weights not just the hidden layers. As a result, it is proved that the proposed method can learn several existing well-known matrix factorizations, including singular value decomposition, convex, nonnegative and semi-nonnegative matrix factorizations. Finally, comprehensive experiments demonstrate the superiority of the proposed method against other state-of-the-arts. • Propose a learnable method to approximate several popular matrix factorizations. • Construct an easy-built end-to-end architecture for low-rank learning. • Provide a new perspective for computing hierarchical low-rank matrix factors. • Validate its superiority to matrix factorizations and deep clustering approaches. [ABSTRACT FROM AUTHOR]

• We propose an innovative network for feature matching. • We introduce attention mechanism to extract global context information. • We exploit the Overlay Attention block, to capture local and global context information. • Experimental results show the proposed network is superior to state of the art networks. Establishing reliable correspondences is a fundamental task in computer vision, and it requires rich contextual information. In this paper, we propose a Channel-Spatial Difference Augment Network (CSDA-Net), by selectively aggregating information from spatial and channel aspects, to seek reliable correspondences for feature matching. Specifically, we firstly introduce the spatial and channel attention mechanism to construct a simple yet effective block for discriminately extracting the global context. After that, we design a Overlay Attention block by further exploiting the spatial and channel attention mechanism with different squeeze operations, to gather more comprehensive contextual information. Finally, the proposed CSDA-Net is able to achieve feature maps with a strong representative ability for feature matching due to the integration of the two novel blocks. Extensive experiments on outlier rejection and relative pose estimation have shown better performance improvements of our CSDA-Net over current state-of-the-art methods on both outdoor and indoor datasets. [ABSTRACT FROM AUTHOR]