Understanding the pivotal role of the human microbiome in health necessitates accurate metabolite prediction, which is crucial for unraveling the intricate interplay between the gut microbiome and human health. This study introduces an innovative approach, Variational Bayesian Multi-Output Gaussian Process Regression (VBMOGPR), to address the challenges posed by the complex, high-dimensional nature of microbiome data. VBMOGPR predicts microbial metabolites, quantifies the model confidence, and incorporates uncertainty estimates. Employing a Bayesian framework with Automatic Relevance Determination (ARD) for feature selection enhances interpretability and performance. Comparative analysis across 14 datasets within a meta-database demonstrated the superiority of VBMOGPR, marking a significant advancement in metabolite prediction and its implications for microbiome impact on human health. In addition, we confirmed that VBMOGPR could tap the potential microbial metabolic association.

BACKGROUND: Identifying potential associations among food, gut microbiota and disease is fundamental for elucidating interaction mechanisms and advancing personalized healthy dietary strategies. While computational methods have been extensively applied to predict microbiota-disease associations, methods on predicting food-microbiota relationships remain limited, particularly regarding higher-order food-microbiota-disease interactions. RESULTS: In this work, we construct a food-microbe-disease (FMD) database encompassing 190 food items, 219 gut microbiota species, and 163 disease entities, resulting in 17,065 FMD associations. We then propose a lightweight single-view contrastive learning hypergraph neural network (LSCHNN) for FMD association prediction on the sparse FMD dataset. LSCHNN formulates ternary FMD interactions as a hypergraph, in which foods, microbes, and diseases are represented by nodes and FMD triplets are represented by hyperedges, and leverages the biological features of foods, microbes, and diseases as node attributes. Subsequently, a hypergraph neural network is designed to learn the embeddings of foods, microbes, and diseases from the hypergraph and predict potential ternary FMD associations. Additionally, we incorporate a single-view contrastive learning mechanism that enhances the model's ability to extract discriminative features and improves generalization on sparse data. Comprehensive comparison experiments demonstrate that LSCHNN outperforms other state-of-the-art methods in terms of the precision of predicting ternary FMD associations and discovering more potential FMD associations. Case studies on two microbes further confirm the effectiveness of LSCHNN in identifying potential FMD associations. CONCLUSIONS: A novel computational model, LSCHNN, is proposed, marking the first integration of hypergraph neural networks with lightweight single-view contrastive learning for ternary FMD association prediction, providing a groundbreaking framework for precision nutrition and personalized dietary interventions.

BACKGROUND: Understanding the metabolic activities of the gut microbiome is vital for deciphering its impact on human health. While direct measurement of these metabolites through metabolomics is effective, it is often expensive and time-consuming. In contrast, microbial composition data obtained through sequencing is more accessible, making it a promising resource for predicting metabolite profiles. However, current computational models frequently face challenges related to limited prediction accuracy, generalizability, and interpretability. METHOD: Here, we present the Deep Mixture of Variational Gaussian Process Experts (DMoVGPE) model, designed to overcome these issues. DMoVGPE utilizes a dynamic gating mechanism, implemented through a neural network with fully connected layers and dropout for regularization, to select the most relevant Gaussian Process experts. During training, the gating network refines expert selection, dynamically adjusting their contribution based on the input features. The model also incorporates an Automatic Relevance Determination (ARD) mechanism, which assigns relevance scores to microbial features by evaluating their predictive power. Features linked to metabolite profiles are given smaller length scales to increase their influence, while irrelevant features are down-weighted through larger length scales, improving both prediction accuracy and interpretability. CONCLUSIONS: Through extensive evaluations on various datasets, DMoVGPE consistently achieves higher prediction performance than existing models. Furthermore, our model reveals significant associations between specific microbial taxa and metabolites, aligning well with findings from existing studies. These results highlight DMoVGPE's potential to provide accurate predictions and to uncover biologically meaningful relationships, paving the way for its application in disease research and personalized healthcare strategies.

In recent years, impressive progress has been achieved in synthetic aperture radar (SAR)-based automatic target recognition (ATR) with the development of deep learning. In practice, a complete training SAR image dataset in all target-aspect domains is limited available for one measurement. When SAR images in the unseen aspect domains are newly acquired, direct retraining of the trained SAR-ATR models with them may lead to a significant performance decline for the seen aspect domains. In this article, we propose an aspect continual recognition model (ACRM) to address the learned feature forgetting problem when SAR images with different target aspects come sequentially in the real-world SAR-ATR. Considering the abundant variations of SAR images with target aspects, we introduce the Bayesian probabilistic frame to improve the model’s generalization of characterizing the varied target features across different aspects. To acquire a better solution for the posterior probability of the model parameters, we integrate an online Monte Carlo variational inference into the deep neural network in the ACRM. Furthermore, to mitigate the accumulation of estimation errors caused by the repetitive approximations in inference, we leverage the coreset method by retaining a small subset of important samples from previous tasks as a coreset. We conduct extensive experiments on the MSTAR and FUSARship datasets. Compared with a variety of baseline algorithms in continual learning, our methods exhibit excellent SAR-ATR performance and robustness, when the SAR images from different target aspects are acquired sequentially.

In real-world synthetic aperture radar (SAR)-based automatic target recognition (ATR), variations in the target-aspect lead to differences in the distribution of target’s scattering points, which will affect the model’s recognition performance, if the training SAR images are incomplete among target-aspect. Traditional deep learning methods for SAR-based recognition often suffer from catastrophic forgetting when online trained on SAR images from different target-aspect domains. To equip SAR-ATR models with the capability of recognizing SAR images online from subsequent target-aspect domains and retaining previously learned knowledge with minimal forgetting, we propose a target-aspect hard attention continual learning (THAT-CL) method, which applies a hard attention mechanism through embedding the indexes of different target-aspect recognition tasks as vectors in each network layer to memorize information of different tasks. By dynamically scaling network weight gradients, we ensure that weights containing more task-specific information undergo smaller updates, while weights with less relevant information experience larger updates. We evaluate THAT-CL using the moving and stationary target acquisition and recognition (MSTAR) dataset. Comparative analysis against other methods demonstrates that the network with THAT-CL achieves higher average accuracy of 93.58% and lower forgetting rate of 3.01%. The results highlight the excellent recognition capability of THAT-CL in generalizing across different SAR image target recognition tasks with varying target-aspects.

The gut microbiome has been regarded as one of the fundamental determinants regulating human health, and multi-omics data profiling has been increasingly utilized to bolster the deep understanding of this complex system. However, stemming from cost or other constraints, the integration of multi-omics often suffers from incomplete views, which poses a great challenge for the comprehensive analysis. In this work, a novel deep model named Incomplete Multi-Omics Variational Neural Networks (IMOVNN) is proposed for incomplete data integration, disease prediction application and biomarker identification. Benefiting from the information bottleneck and the marginal-to-joint distribution integration mechanism, the IMOVNN can learn the marginal latent representation of each individual omics and the joint latent representation for better disease prediction. Moreover, owing to the feature-selective layer predicated upon the concrete distribution, the model is interpretable and can identify the most relevant features. Experiments on inflammatory bowel disease multi-omics datasets demonstrate that our method outperforms several state-of-the-art methods for disease prediction. In addition, IMOVNN has identified significant biomarkers from multi-omics data sources.

A growing number of studies show that the human microbiome plays a vital role in human health and can be a crucial factor in predicting certain human diseases. However, microbiome data are often characterized by the limited samples and high-dimensional features, which pose a great challenge for machine learning methods. Therefore, this paper proposes a novel ensemble deep learning disease prediction method that combines unsupervised and supervised learning paradigms. First, unsupervised deep learning methods are used to learn the potential representation of the sample. Afterwards, the disease scoring strategy is developed based on the deep representations as the informative features for ensemble analysis. To ensure the optimal ensemble, a score selection mechanism is constructed, and performance boosting features are engaged with the original sample. Finally, the composite features are trained with gradient boosting classifier for health status decision. For case study, the ensemble deep learning flowchart has been demonstrated on six public datasets extracted from the human microbiome profiling. The results show that compared with the existing algorithms, our framework achieves better performance on disease prediction.

Intelligent Internet of Things (IoT) systems based on deep neural networks (DNNs) have been widely deployed in the real world. However, DNNs are found to be vulnerable to adversarial examples, which raises people’s concerns about intelligent IoT systems’ reliability and security. Testing and evaluating the robustness of IoT systems become necessary and essential. Recently, various attacks and strategies have been proposed, but the efficiency problem remains unsolved properly. Existing methods are either computationally extensive or time consuming, which is not applicable in practice. In this article, we propose a novel framework, called attack-inspired generative adversarial networks (AI-GAN) to generate adversarial examples conditionally. Once trained, it can generate adversarial perturbations efficiently given input images and target classes. We apply AI-GAN on different data sets in white-box settings, black-box settings, and targeted models protected by state-of-the-art defenses. Through extensive experiments, AI-GAN achieves high attack success rates, outperforming existing methods, and reduces generation time significantly. Moreover, for the first time, AI-GAN successfully scales to complex data sets, e.g., CIFAR-100 and ImageNet, with about 90% success rates among all classes.

Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding imperceptible perturbations to inputs. Recently different attacks and strategies have been proposed, but how to generate adversarial examples perceptually realistic and more efficiently remains unsolved. This paper proposes a novel framework called Attack-Inspired GAN (AI-GAN), where a generator, a discriminator, and an attacker are trained jointly. Once trained, it can generate adversarial perturbations efficiently given input images and target classes. Through extensive experiments on several popular datasets e.g., MNIST and CFAR-10, AI-GAN achieves high attack success rates and reduces generation time significantly in various settings. Moreover, for the first time, AI-GAN successfully scales to complicated datasets e.g., CFAR-100 with around 90% success rates among all classes.

Adversarial examples can be imperceptible to human eyes but can easily fool deep models. Such intrigue property has raised security issues for real-world industrial deep learning systems. To combat those malicious attacks, a novel defense strategy has been proposed based on the conditional variational autoencoder (CVAE) and Bayesian network (BN). The main contribution lies in the provided systematic dual-domain-based defense framework, which covers three modules named detection, diagnosis, and recovery. Specifically, the CVAE is first introduced for latent- and residual-domain generation. Subsequently, a composite and hierarchical BN detector is proposed to conduct the adversary detection through feature validation and output justification. Afterwards, a diagnosis strategy has been constructed for residual domain and different attacks can be evaluated in the unified framework. Finally, a two-step recovery mechanism is established on the CVAE that can effectively restore the feature representations and the network predictions from various adversaries. The feasibility of the entire defense diagram has been extensively demonstrated on three real-world recognition problems.

Independent component analysis (ICA) is widely used for modeling and monitoring non-Gaussian process. However, traditional ICA lacks probabilistic representation of process uncertainties. In this study, a probabilistic ICA (PICA) model is proposed for non-Gaussian process modeling and monitoring. The independent latent spaces are specified with Student’s${\rm t}$formulation to account for both Gaussian and non-Gaussian data characteristics while the additional noise term is further served as a complement for explaining underlying process uncertainties. The Student’s${\rm t}$distribution with adjustable tails is essentially an infinite mixture of Gaussians with various scaling variances. In order to monitor retained variations, the noise space is further extracted and analyzed with probabilistic principal component analysis (PPCA). Simulation results show that compared with the deterministic ICA-based method, the proposed two-stage probabilistic extraction method is more effective for monitoring non-Gaussian industrial processes.

In order to deal with the modeling and monitoring issue of large-scale industrial processes with big data, a distributed and parallel designed principal component analysis approach is proposed. To handle the high-dimensional process variables, the large-scale process is first decomposed into distributed blocks with a priori process knowledge. Afterward, in order to solve the modeling issue with large-scale data chunks in each block, a distributed and parallel data processing strategy is proposed based on the framework of MapReduce and then principal components are further extracted for each distributed block. With all these steps, statistical modeling of large-scale processes with big data can be established. Finally, a systematic fault detection and isolation scheme is designed so that the whole large-scale process can be hierarchically monitored from the plant-wide level, unit block level, and variable level. The effectiveness of the proposed method is evaluated through the Tennessee Eastman benchmark process.