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MOTIVATION: In recent years, a large number of biological experiments have strongly shown that miRNAs play an important role in understanding disease pathogenesis. The discovery of miRNA-disease associations is beneficial for disease diagnosis and treatment. Since inferring these associations through biological experiments is time-consuming and expensive, researchers have sought to identify the associations utilizing computational approaches. Graph Convolutional Networks (GCNs), which exhibit excellent performance in link prediction problems, have been successfully used in miRNA-disease association prediction. However, GCNs only consider 1st-order neighborhood information at one layer but fail to capture information from high-order neighbors to learn miRNA and disease representations through information propagation. Therefore, how to aggregate information from high-order neighborhood effectively in an explicit way is still challenging. RESULTS: To address such a challenge, we propose a novel method called mixed neighborhood information for miRNA-disease association (MINIMDA), which could fuse mixed high-order neighborhood information of miRNAs and diseases in multimodal networks. First, MINIMDA constructs the integrated miRNA similarity network and integrated disease similarity network respectively with their multisource information. Then, the embedding representations of miRNAs and diseases are obtained by fusing mixed high-order neighborhood information from multimodal network which are the integrated miRNA similarity network, integrated disease similarity network and the miRNA-disease association networks. Finally, we concentrate the multimodal embedding representations of miRNAs and diseases and feed them into the multilayer perceptron (MLP) to predict their underlying associations. Extensive experimental results show that MINIMDA is superior to other state-of-the-art methods overall. Moreover, the outstanding performance on case studies for esophageal cancer, colon tumor and lung cancer further demonstrates the effectiveness of MINIMDA. AVAILABILITY AND IMPLEMENTATION: https://github.com/chengxu123/MINIMDA and http://120.79.173.96/.
Assuntos
Neoplasias do Colo , MicroRNAs , Algoritmos , Biologia Computacional/métodos , Humanos , MicroRNAs/genética , Redes Neurais de ComputaçãoRESUMO
This survey is for the remembrance of one of the creators of the information bottleneck theory, Prof. Naftali Tishby, passing away at the age of 68 on August, 2021. Information bottleneck (IB), a novel information theoretic approach for pattern analysis and representation learning, has gained widespread popularity since its birth in 1999. It provides an elegant balance between data compression and information preservation, and improves its prediction or representation ability accordingly. This survey summarizes both the theoretical progress and practical applications on IB over the past 20-plus years, where its basic theory, optimization, extensive models and task-oriented algorithms are systematically explored. Existing IB methods are roughly divided into two parts: traditional and deep IB, where the former contains the IBs optimized by traditional machine learning analysis techniques without involving any neural networks, and the latter includes the IBs involving the interpretation, optimization and improvement of deep neural works (DNNs). Specifically, based on the technique taxonomy, traditional IBs are further classified into three categories: Basic, Informative and Propagating IB; While the deep IBs, based on the taxonomy of problem settings, contain Debate: Understanding DNNs with IB, Optimizing DNNs Using IB, and DNN-based IB methods. Furthermore, some potential issues deserving future research are discussed. This survey attempts to draw a more complete picture of IB, from which the subsequent studies can benefit.
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Deep multiview clustering (MVC) is to learn and utilize the rich relations across different views to enhance the clustering performance under a human-designed deep network. However, most existing deep MVCs meet two challenges. First, most current deep contrastive MVCs usually select the same instance across views as positive pairs and the remaining instances as negative pairs, which always leads to inaccurate contrastive learning (CL). Second, most deep MVCs only consider learning feature or cluster correlations across views, failing to explore the dual correlations. To tackle the above challenges, in this article, we propose a novel deep MVC framework by pseudo-label guided CL and dual correlation learning. Specifically, a novel pseudo-label guided CL mechanism is designed by using the pseudo-labels in each iteration to help removing false negative sample pairs, so that the CL for the feature distribution alignment can be more accurate, thus benefiting the discriminative feature learning. Different from most deep MVCs learning only one kind of correlation, we investigate both the feature and cluster correlations among views to discover the rich and comprehensive relations. Experiments on various datasets demonstrate the superiority of our method over many state-of-the-art compared deep MVCs. The source implementation code will be provided at https://github.com/ShizheHu/Deep-MVC-PGCL-DCL.
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In this paper, we address the problem of multi-view clustering (MVC), integrating the close relationships among views to learn a consistent clustering result, via triplex information maximization (TIM). TIM works by proposing three essential principles, each of which is realized by a formulation of maximization of mutual information. 1) Principle 1: Contained. The first and foremost thing for MVC is to fully employ the self-contained information in each view. 2) Principle 2: Complementary. The feature-level complementary information across pairwise views should be first quantified and then integrated for improving clustering. 3) Principle 3: Compatible. The rich cluster-level shared compatible information among individual clustering of each view is significant for ensuring a better final consistent result. Following these principles, TIM can enjoy the best of view-specific, cross-view feature-level, and cross-view cluster-level information within/among views. For principle 2, we design an automatic view correlation learning (AVCL) mechanism to quantify how much complementary information across views by learning the cross-view weights between pairwise views automatically, instead of view-specific weights as most existing MVCs do. Specifically, we propose two different strategies for AVCL, i.e., feature-based and cluster-based strategy, for effective cross-view weight learning, thus leading to two versions of our method, TIM-F and TIM-C, respectively. We further present a two-stage method for optimization of the proposed methods, followed by the theoretical convergence and complexity analysis. Extensive experimental results suggest the effectiveness and superiority of our methods over many state-of-the-art methods.
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In many practical applications, massive data are observed from multiple sources, each of which contains multiple cohesive views, called hierarchical multiview (HMV) data, such as image-text objects with different types of visual and textual features. Naturally, the inclusion of source and view relationships offers a comprehensive view of the input HMV data and achieves an informative and correct clustering result. However, most existing multiview clustering (MVC) methods can only process single-source data with multiple views or multisource data with single type of feature, failing to consider all the views across multiple sources. Observing the rich closely related multivariate (i.e., source and view) information and the potential dynamic information flow interacting among them, in this article, a general hierarchical information propagation model is first built to address the above challenging problem. It describes the process from optimal feature subspace learning (OFSL) of each source to final clustering structure learning (CSL). Then, a novel self-guided method named propagating information bottleneck (PIB) is proposed to realize the model. It works in a circulating propagation fashion, so that the resulting clustering structure obtained from the last iteration can "self-guide" the OFSL of each source, and the learned subspaces are in turn used to conduct the subsequent CSL. We theoretically analyze the relationship between the cluster structures learned in the CSL phase and the preservation of relevant information propagated from the OFSL phase. Finally, a two-step alternating optimization method is carefully designed for optimization. Experimental results on various datasets show the superiority of the proposed PIB method over several state-of-the-art methods.
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Weighted multi-view clustering (MVC) aims to combine the complementary information of multi-view data (such as image data with different types of features) in a weighted manner to obtain a consistent clustering result. However, when the cluster-wise weights across views are vastly different, most existing weighted MVC methods may fail to fully utilize the complementary information, because they are based on view-wise weight learning and can not learn the fine-grained cluster-wise weights. Additionally, extra parameters are needed for most of them to control the weight distribution sparsity or smoothness, which are hard to tune without prior knowledge. To address these issues, in this paper we propose a novel and effective Cluster-weighted mUlti-view infoRmation bottlEneck (CURE) clustering algorithm, which can automatically learn the cluster-wise weights to discover the discriminative clusters across multiple views and thus can enhance the clustering performance by properly exploiting the cluster-level complementary information. To learn the cluster-wise weights, we design a new weight learning scheme by exploring the relation between the mutual information of the joint distribution of a specific cluster (containing a group of data samples) and the weight of this cluster. Finally, a novel draw-and-merge method is presented to solve the optimization problem. Experimental results on various multi-view datasets show the superiority and effectiveness of our cluster-wise weighted CURE over several state-of-the-art methods.