Over recent years, EEG-based brain decoding of perceived multimedia is emerging to be an important multidisciplinary research area. Lack of data sets with multimedia stimuli, however, presents a significant challenge for its further advancement. In this paper, we establish a facial-image stimulated EEG dataset, named as EEG-Face, to address the challenge and provide a crucial support for relevant research, such as brain-computer interface (BCI), face recognition via brain-perceived EEGs, and multimedia content analysis via brain perception activities. As facial images not only distinguish between genders but also dive deeper into individual differences, our proposed EEG-Face provides larger scope, more focus, and greater potential for dedicated research on brain perception of human faces. As shown in Figure 1, the proposed EEG-Face essentially consists of 20,000 brain responded EEG trials stimulated with 40 individual faces, all of whom are Chinese film stars. Following the establishment of the dataset, a range of experiments over EEG-Face is carried out to demonstrate its usability and feasibility, which include: (i) neural correlation of gender perceptions; (ii) EEG-Stimulus pairing verification; and (iii) face recognition via classification of randomized EEG trials. The dataset and the codes for all reported experiments are available from: https://github.com/eeg-wx2024/EEG-Face.

The biological targets of traditional Chinese medicine (TCM) are the core effectors mediating the interaction between TCM and the human body. Identification of TCM targets is essential to elucidate the chemical basis and mechanisms of TCM for treating diseases. Given the chemical complexity of TCM, both in silico high-throughput compound-target interaction predicting models and biological profile-based methods have been commonly applied for identifying TCM targets based on the structural information of TCM chemical components and biological information, respectively. However, the existing methods lack the integration of TCM chemical and biological information, resulting in difficulty in the systematic discovery of TCM action pathways. To solve this problem, we propose a novel target identification model NP-TCMtarget to explore the TCM target path by combining the overall chemical and biological profiles. First, NP-TCMtarget infers TCM effect targets by calculating associations between herb/disease inducible gene expression profiles and specific gene signatures for 8233 targets. Then, NP-TCMtarget utilizes a constructed binary classification model to predict binding targets of herbal ingredients. Finally, we can distinguish TCM direct and indirect targets by comparing the effect targets and binding targets to establish the action pathways of herbal component-direct target-indirect target by mapping TCM targets in the biological molecular network. We apply NP-TCMtarget to the formula XiaoKeAn to demonstrate the power of revealing the action pathways of herbal formula. We expect that this novel model could provide a systematic framework for exploring the molecular mechanisms of TCM at the target level. NP-TCMtarget is available at http://www.bcxnfz.top/NP-TCMtarget.

Visual foundation models (VFMs) have been widely applied in the field of remote sensing (RS). However, they still face two main challenges when applied to precise remote sensing change detection (RSCD) tasks in complex scenes. Firstly, the nonnegligible domain shift between natural scene and RS scene limits the direct application of VFMs to the RSCD task. Second, most of existing RSCD methods may suffer from the boundary displacement problem due to the inadequate exploration of temporal differences for bi-temporal features. To address the above issues, this study proposes a SAM2-based domain adaptive and spatial difference aggregation network (DA2-Net) for RSCD. The proposed DA2-Net has two main advantages. First, a hierarchical low-rank adaptation (LoRA) strategy is presented by introducing low-rank matrices at key positions of SAM2, which can inject inductive biases from the RS domain into the network and alleviate the domain shift problem. Second, a difference adaptive enhancement module (DAEM) is designed to explore temporal differences for hierarchical bi-temporal features. The DAEM provides respective attention weights for different information through a dual branch of global difference awareness and local detail optimization. Experimental results on SYSU-CD, WHU-CD, and LEVIR-CD datasets demonstrate the superiority of DA2-Net. Code is available at https://github.com/xuptheqi-hash/ DA2Net.

Semi-supervised change detection methods with consistency regularization, which overcome the lack of labeled samples by using unlabeled samples and enforcing consistent predictions under weak perturbations. However, current consistency regularization methods lack randomness in their perturbation settings and treat all samples uniformly, limiting the model’s ability to leverage sample diversity to improve generalization. In contrast, meta-learning methods shift focus from individual samples to learning patterns across similar tasks, thereby enhancing information efficiency and model generalization. Inspired by these principles, we propose a Meta-Learning-based Semi-supervised Change Detection (MLSCD) method for remote sensing images, which aims to explore and leverage meta-learning methods to enhance the generalization capabilities of consistency regularization-based semi-supervised change detection. First, we set the degree of weak perturbation and the combination of different types of perturbations as random parameters to generate diverse and randomized weak perturbations. Second, we redefine consistency regularization-based semi-supervised change detection from a meta-learning perspective, which learns patterns from diverse perturbation tasks to improve sample utilization efficiency, thereby enhancing the model’s generalization capability. Third, we balance accuracy and efficiency by using AdamW for cross-task updates in the outer loop and SGD for single-task optimization in the inner loop, which experimental results demonstrate is an ideal method for applying meta-learning to remote sensing change detection. Finally, the superiority of the proposed method is validated on two datasets. The extensive experimental results demonstrate the superior performance of the proposed method compared to state-of-the-art methods.

Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Here, we show TranscriptionNet, a deep learning model that integrates multiple biological networks to systematically predict transcriptional profiles to three types of genetic perturbations based on transcriptional profiles induced by genetic perturbations in the L1000 project: RNA interference, clustered regularly interspaced short palindromic repeat, and overexpression. TranscriptionNet performs better than existing approaches in predicting inducible gene expression changes for all three types of genetic perturbations. TranscriptionNet can predict transcriptional profiles for all genes in existing biological networks and increases perturbational gene expression changes for each type of genetic perturbation from a few thousand to 26 945 genes. TranscriptionNet demonstrates strong generalization ability when comparing predicted and true gene expression changes on different external tasks. Overall, TranscriptionNet can systemically predict transcriptional consequences induced by perturbing genes on a genome-wide scale and thus holds promise to systemically detect gene function and enhance drug development and target discovery.

BACKGROUND: Identification of drug-target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target proteins have pushed this prediction performance improvement to a new level, these methods are still difficult to adapt to novel structures. Alternatively, large-scale biological and pharmacological data provide new ways to accelerate drug-target interaction prediction. METHODS: Here, we propose DrugMAN, a deep learning model for predicting drug-target interaction by integrating multiplex heterogeneous functional networks with a mutual attention network (MAN). DrugMAN uses a graph attention network-based integration algorithm to learn network-specific low-dimensional features for drugs and target proteins by integrating four drug networks and seven gene/protein networks collected by a certain screening conditions, respectively. DrugMAN then captures interaction information between drug and target representations by a mutual attention network to improve drug-target prediction. RESULTS: DrugMAN achieved the best performance compared with cheminformation-based methods SVM, RF, DeepPurpose and network-based deep learing methods DTINet and NeoDT in four different scenarios, especially in real-world scenarios. Compared with SVM, RF, deepurpose, DTINet, and NeoDT, DrugMAN showed the smallest decrease in AUROC, AUPRC, and F1-Score from warm-start to Both-cold scenarios. This result is attributed to DrugMAN's learning from heterogeneous data and indicates that DrugMAN has a good generalization ability. Taking together, DrugMAN spotlights heterogeneous information to mine drug-target interactions and can be a powerful tool for drug discovery and drug repurposing.

Meta-learning methods try to enhance the generalization of the meta-learning model by various tasks. Diverse tasks can provide sufficient knowledge to assist the model in understanding and capturing different types of information. The sufficient number of meta-training tasks is a crucial factor in ensuring the generalization of meta-learning algorithms. However, obtaining a sufficient number of labeled meta-training tasks is often a challenging endeavor in real-world scenarios. Some scholars have addressed the issue of inadequate tasks only by interpolating between two tasks, but they do not take into account the problem of task diversity. To resolve this concern, we propose a Multi-task Convex Combination Interpolation (MCCI) method that simultaneously considers both task quantity and task diversity issues. First, we increase the number of tasks based on interpolation techniques, which provides the possibility to extend it from 2-task interpolation to n-task interpolation. Second, we randomly select multiple tasks and perform convex combination interpolation in the n-task space, in order to increase the task diversity. Third, we prove the positive effect of the convex combination interpolation on the generalization ability and noise resistance of meta-learning algorithms in theory. Finally, we verify the generalization ability and noise resistance of the proposed MCCI on seven datasets. The extensive experimental results show the superior performance of the proposed method compared to the state-of-art methods.

The multiple change detection (MCD) of hyperspectral images (HSIs) is the process of detecting change areas and providing “from–to” change information of HSIs obtained from the same area at different times. HSIs have hundreds of spectral bands and contain a large amount of spectral information. However, current deep-learning-based MCD methods do not pay special attention to the interspectral dependency and the effective spectral bands of various land covers, which limits the improvement of HSIs’ change detection (CD) performance. To address the above problems, we propose a spectrum-induced transformer-based feature learning (STFL) method for HSIs. The STFL method includes a spectrum-induced transformer-based feature extraction module (STFEM) and an attention-based detection module (ADM). First, the 3D-2D convolutional neural networks (CNNs) are used to extract deep features, and the transformer encoder (TE) is used to calculate self-attention matrices along the spectral dimension in STFEM. Then, the extracted deep features and the learned self-attention matrices are dot-multiplied to generate more discriminative features that take the long-range dependency of the spectrum into account. Finally, ADM mines the effective spectral bands of the difference features learned from STFEM by the attention block (AB) to explore the discrepancy of difference features and uses the softmax function to identify multiple changes. The proposed STFL method is validated on two hyperspectral datasets, and their experiments illustrate the superiority of the proposed STFL method over the currently existing MCD methods.

Change detection is the process of detecting and evaluating differences from bitemporal remote sensing images. Deep-learning-based change detection methods have become the mainstream approaches due to their discriminative features and good change detection performance. However, most of the existing deep-learning-based change detection methods did not perform well in detecting subtle changes and did not fully explore the underlying information of features learned by deep neural networks. To address the above-mentioned problems, we propose an end-to-end deep neural network for multispectral change detection, named difference-enhancement triplet network (DETNet). DETNet mainly includes two modules: the triplet feature extraction module and the difference feature learning module. First, the triplet feature extraction module uses the triple CNN as the backbone to extract representative spatial–spectral features. Second, the difference feature learning module mines the underlying information of difference representations of learned spatial–spectral features to detect subtle changes. Finally, the model uses a compound loss function, which includes triplet loss, contrastive loss, and cross-entropy loss, to guide DETNet toward learning more discriminative features. Extensive experimental results of the proposed DETNet and other state-of-the-art methods on four datasets demonstrate its superiority.

Change detection in the HyperSpectral Imagery (HSI) detects the changed pixels or areas in bi-temporal images. HSIs contain hundreds of spectral bands, including a large amount of spectral information. However, most of deep learning-based change detection methods did not focus on the spectral dependency of spectral information in the spectral dimension and just adopted the difference strategy to represent the correlation of learned features, which limited the improvement of the change detection performance. To address the above-mentioned problems, we propose an end-to-end change detection network for HSIs, named Spectrum-Aware Transformer Network (SATNet), which includes SETrans feature extraction module, the transformer-based correlation representation module and the detection module. First, SETrans feature extraction module is employed to extract deep features of HSIs. Then, the transformer-based correlation representation module is presented to explore the spectral dependency of spectral information and capture the correlation of learned features of bi-temporal HSIs from both the perspective of difference and dot-product operations, so as to obtain more discriminative features. Finally, the decision fusion strategy in the detection module is utilized to the learned discriminative features to generate the final change map for better change detection performance. Experimental results on three hyperspectral datasets show that the proposed SATNet is superior to the existing change detection methods.

One of the most difficult problems that hinder the development and application of herbal medicine is how to illuminate the global effects of herbs on the human body. Currently, the chemo-centric network pharmacology methodology regards herbs as a mixture of chemical ingredients and constructs the 'herb-compound-target-disease' connections based on bioinformatics methods, to explore the pharmacological effects of herbal medicine. However, this approach is severely affected by the complexity of the herbal composition. Alternatively, gene-expression profiles induced by herbal treatment reflect the overall biological effects of herbs and are suitable for studying the global effects of herbal medicine. Here, we develop an online transcriptome-based multi-scale network pharmacology platform (TMNP) for exploring the global effects of herbal medicine. Firstly, we build specific functional gene signatures for different biological scales from molecular to higher tissue levels. Then, specific algorithms are designed to measure the correlations of transcriptional profiles and types of gene signatures. Finally, TMNP uses pharmacotranscriptomics of herbal medicine as input and builds associations between herbs and different biological scales to explore the multi-scale effects of herb medicine. We applied TMNP to a single herb Astragalus membranaceus and Xuesaitong injection to demonstrate the power to reveal the multi-scale effects of herbal medicine. TMNP integrating herbal medicine and multiple biological scales into the same framework, will greatly extend the conventional network pharmacology model centering on the chemical components, and provide a window for systematically observing the complex interactions between herbal medicine and the human body. TMNP is available at http://www.bcxnfz.top/TMNP.

Hyperspectral anomaly detection is an important task in the remote sensing domain. Recently, researchers have shown great interest in deep learning-based methods because they can learn hierarchical, abstract, and high-level representations. However, the latent features learned from the autoencoder (AE) are not always able to reflect the intrinsic structure of hyperspectral data because the locality property is not considered during the learning process. In order to address this problem, a novel manifold constrained AE network (MC-AEN)-based hyperspectral anomaly detection method is proposed in this article. First, the manifold learning method is employed to learn the embedding manifold. Then, the latent representations are learned by an AE network with the learned embedding manifold constraints to preserve the intrinsic structure of hyperspectral data. Finally, the reconstruction errors are calculated to detect anomalies. The global reconstruction error from MC-AEN and the local reconstruction error from the learned latent representations are combined to fully utilize the learned knowledge for better detection performance. We test our proposed algorithm on three different real data sets. Experimental results on these three data sets show the superiority of our proposed method.

Hyperspectral anomaly detection is very important in the remote sensing domain. The representation-based anomaly method is one of the most important hyperspectral anomaly detection methods, which uses reconstruction errors (REs) to detect anomalies. REs are affected by the basis matrix and its corresponding coefficient matrix. Mixed pixels exist because of the low-spatial resolution of hyperspectral images. The RE is not large enough to correctly distinguish the pixel difficult to classify when the basis matrix is composed of pixels. Moreover, its corresponding coefficients cannot indicate whether pixels are pure or mixed and the abundances of mixed pixels. To address the above-mentioned problems, endmembers referring to pure or relatively pure spectral signatures are explored to build the basis matrix. The RE based on the basis matrix of endmembers is much larger for the anomalous pixel difficult to correctly classify. Furthermore, its corresponding coefficient matrix of endmembers has physical meanings. Hence, a novel hyperspectral anomaly detection based on similarity constrained convex nonnegative matrix factorization is proposed from the perspective of endmembers for the first time. First, convex nonnegative matrix factorization (CNMF) is employed to obtain endmembers of background. Then, CNMF is constrained by the similarity regularization that considers different contributions of endmembers to the pixel under test to acquire the more accurate and meaningful coefficient matrix. Finally, anomalies are detected by calculating REs. The proposed algorithm is verified on both simulated and real data sets. Experimental results show that our proposed algorithm outperforms other state-of-the-art algorithms.

Hyperspectral target detection is an approach which tries to locate targets in a hyperspectral image on the condition of given targets spectrum. Many classical target detectors are based on the linear mixing model (LMM) and sparsity model. The LMM has a poor performance in dealing with the spectral variability. Therefore, more studies focus on the sparsity-based detectors, most of which are based on residual reconstruction. Owing to the fact that the impure dictionary for the test pixel weakens the detection performance and the discrimination ability of residual function has direct influence on the detecting accuracy, the dictionary purity and discriminative residual function are two most important factors affecting the accuracy of sparsity-based target detectors. In order to obtain more purified dictionary and discriminative residual function, this paper proposes a novel sparsity-based detector named the hybrid sparsity and distance-based discrimination (HSDD) detector for target detection in hyperspectral imagery. The residual function is constrained by the discrimination information during the dictionary construction, which enhances the dictionary purification. Only background samples are used to construct the dictionary because it is easier to remove the target pixel than to select it on the condition that majority of pixels are the background pixels. Hence, a purification process is applied for background training samples in order to construct an effective competition between the residual term and discriminative term. Extensive experimental results with four hyperspectral data sets demonstrate that the proposed HSDD algorithm has a better performance than the state-of-the-art algorithms.

Change detection is an important technique providing insights to urban planning, resources monitoring, and environmental studies. For multispectral images, most semi-supervised change detection methods focus on improving the contribution of training samples hard to be classified to the trained classifier. However, hard training samples will weaken the discrimination of the training model for multispectral change detection. Besides, these methods only use the spectral information, while the limited spectral information cannot represent objects very well. In this paper, a method named as coarse-to-fine semi-supervised change detection is proposed to solve the aforementioned problems. First, a novel multiscale feature is exploited by concatenating the spectral vector of the pixel to be detected and its adjacent pixels by different scales. Second, the enhanced metric learning is proposed to acquire more discriminant metric by strengthening the contribution of training samples easy to be classified and weakening the contribution of training samples hard to be classified to the trained model. Finally, a coarse-to-fine strategy is adopted to detect testing samples from the viewpoint of distance metric and label information of neighborhood in spatial space. The coarse detection result obtained from the enhanced metric learning is used to guide the final detection. The effectiveness of our proposed method is verified on two real-life operating scenarios, Taizhou and Kunshan data sets. Extensive experimental results demonstrate that our proposed algorithm has better performance than those of other state-of-the-art algorithms.

Accurate image segmentation is important for many medical imaging applications, whereas it remains challenging due to the complexity in medical images, such as the complex shapes and varied neighbor structures. This paper proposes a new hierarchical 3D image segmentation method based on patient-specific shape prior and surface patch shape statistics (SURPASS) model. In the segmentation process, a coarse-to-fine, two-stage strategy is designed, which contains global segmentation and local segmentation. In the global segmentation stage, patient-specific shape prior is estimated by using manifold learning techniques to achieve the overall segmentation. In the second stage, SURPASS is computed to solve the problem of poor segmentation at certain surface patches. The effectiveness of the proposed 3D image segmentation method has been demonstrated by the experiments on segmenting the prostate from a series of MR images.