The development of single-cell multi-omics sequencing technologies has enabled the simultaneous analysis of multi-omics data within the same cell. Accurate clustering of these cells is crucial for downstream analyses of complex biological functions. Despite significant advances in multi-omics integration approaches, current methodologies exhibit two major limitations. First, they inadequately incorporate prior biological knowledge from various omic layers. Second, these methods often conduct independent dimensionality reduction on individual omic datasets, thereby failing to capture the intrinsic complementary information and potentially overlooking crucial cross-platform interactions. Motivated by these, this study investigates a non-negative matrix factorization model called PLNMFG, which integrates the unified latent representation learning that retains the features between and within omics and the cluster structure learning that retains the intrinsic structure of the data into one joint framework. Specially, PLNMFG performs adaptive imputation to handle dropout events and uses prior pseudo-labels as constraints during the process of collective non-negative matrix factorization, as a result, a more robust latent representation that preserves the double similarity information is obtained. Graph Laplacian constraint is applied during clustering which further preserves structure characteristic of multi-omics data. In addition, the weight of each omic is adaptively learned based on the omic contribution. A series of experiments on 8 benchmark datasets show that our model performs well in terms of clustering accuracy and computational efficiency.

Chronic diseases, because of insidious onset and long latent period, have become the major global disease burden. However, the current chronic disease diagnosis methods based on genetic markers or imaging analysis are challenging to promote completely due to high costs and cannot reach universality and popularization. This study analyzed massive data from routine blood and biochemical test of 32 448 patients and developed a novel framework for cost-effective chronic disease prediction with high accuracy (AUC 87.32%). Based on the best-performing XGBoost algorithm, 20 classification models were further constructed for 17 types of chronic diseases, including 9 types of cancers, 5 types of cardiovascular diseases and 3 types of mental illness. The highest accuracy of the model was 90.13% for cardia cancer, and the lowest was 76.38% for rectal cancer. The model interpretation with the SHAP algorithm showed that CREA, R-CV, GLU and NEUT% might be important indices to identify the most chronic diseases. PDW and R-CV are also discovered to be crucial indices in classifying the three types of chronic diseases (cardiovascular disease, cancer and mental illness). In addition, R-CV has a higher specificity for cancer, ALP for cardiovascular disease and GLU for mental illness. The association between chronic diseases was further revealed. At last, we build a user-friendly explainable machine-learning-based clinical decision support system (DisPioneer: http://bioinfor.imu.edu.cn/dispioneer) to assist in predicting, classifying and treating chronic diseases. This cost-effective work with simple blood tests will benefit more people and motivate clinical implementation and further investigation of chronic diseases prevention and surveillance program.

Unsupervised Domain Adaptation (UDA) aims to leverage knowledge of a well-labeled source domain to learn an effective classifier for an unlabeled target domain. However, a common scenario in real-world applications is that the target domain contains unknown categories that are not observed in the source domain. This setting is termed as open set domain adaptation (OSDA). Most existing approaches of OSDA can only classify known classes well but fail to recognize unknown samples effectively. In this paper, we propose an effective method, named manifold regularized joint transfer (MRJT), for OSDA. MRJT learns new feature representations by simultaneously reducing distribution discrepancy between domains, increasing compactness of within-class, discriminating different known classes, and distinguishing the unknown from the known. The learned new features are projected onto reproducing kernel Hilbert space. In this space, a weighted structural risk minimization method is integrated with manifold regularization to utilize geometric information sufficiently to learn an effective classifier. Extensive experimental results on four real-world datasets verify the superiority of our method. It can not only classify known samples into the right known classes but also recognize unknown samples effectively.

The recent development of deep learning has brought breakthroughs in image denoising. However, the recovery of image detail, especially high-frequency weak information, still needs to be improved. Firstly, the noise mainly concentrates on the high-frequency signal, and the high-frequency signal is easy to be disturbed, which makes it difficult to recover; Secondly, in the process of image denoising with deep learning, feature extraction of model is used to smooth the noise for image restoration, resulting in a poor recovery effect of high-frequency signal. To solve the above problems and improve the overall image denoising performance, we propose a denoising network for complex frequency band signal processing (CFPNet), which contains three insights: 1) the image input node uses a cosine transform to segment the image noise frequency and divides different image features into signals in different frequency bands for targeted noise reduction; 2) targeted noise reduction is carried out for different frequency band signals via a fine-grained scheme; 3) different frequency band signals are fused and high-frequency signals are enhanced to improve the recovery of detailed signals. The experimental results show that the proposed CFPNet can achieve state-of-the-art performance on both real-world datasets and Gaussian noise fitting datasets.

Abstract With their proliferation and increasing capabilities, mobile devices with local wireless interfaces can be organized into delay‐tolerant networks (DTNs) that exploit communication opportunities arising out of the movement of their users. As the mobile devices are usually carried by people, these DTNs can also be viewed as social networks. Unfortunately, most existing routing algorithms for DTNs rely on relatively simple mobility models that rarely consider these social network characteristics, and therefore, the mobility models in these algorithms cannot accurately describe users’ real mobility traces. In this paper, we propose two predict and spread (PreS) message routing algorithms for DTNs. We employ an adapted Markov chain to model a node's mobility pattern and capture its social characteristics. A comparison with state‐of‐the‐art algorithms demonstrates that PreS can yield better performance in terms of delivery ratio and delivery latency, and it can provide a comparable performance with the epidemic routing algorithm with lower resource consumption. Copyright © 2013 John Wiley & Sons, Ltd.

Mobile devices with local wireless interfaces can be organized into opportunistic networks which exploit communication opportunities arising from the movement of their users. With the proliferation and increasing capabilities of these devices, it is significant to investigate message dissemination over opportunistic networks to maximize the potential of those networks. In this paper, we analyze the performance of copy-limited flooding over opportunistic networks, where each node can only send no more than k copies of the same message. For this purpose, we propose a network model called Markov and Random graph Hierarchic Model (MRHM), where a node transfers among different Main-areas (places frequently visited by nodes) according to the Markov rule, and two different nodes in the same Main-area can establish a connection with a certain probability. We theoretically analyze the performance of k-copy limited flooding over MRHM in terms of delivery rate and delay. Our extensive experiments over MRHM and real traces reveal that when k equals 3, the performance of k-copy limited flooding is very close to that of Epidemic Routing.