RNA inverse folding, designing sequences to form specific 3D structures, is critical for therapeutics, gene regulation, and synthetic biology. Current methods, focused on sequence recovery, struggle to address structural objectives like secondary structure consistency (SS), minimum free energy (MFE), and local distance difference test (LDDT), leading to suboptimal structural accuracy. To tackle this, we propose a reinforcement learning (RL) framework integrated with a latent diffusion model (LDM). Drawing inspiration from the success of diffusion models in RNA inverse folding, which adeptly model complex sequence-structure interactions, we develop an LDM incorporating pre-trained RNA-FM embeddings from a large-scale RNA model. These embeddings capture co-evolutionary patterns, markedly improving sequence recovery accuracy. However, existing approaches, including diffusion-based methods, cannot effectively handle non-differentiable structural objectives. By contrast, RL excels in this task by using policy-driven reward optimization to navigate complex, non-gradient-based objectives, offering a significant advantage over traditional methods. In summary, we propose the Step-wise Optimization of Latent Diffusion Model (SOLD), a novel RL framework that optimizes single-step noise without sampling the full diffusion trajectory, achieving efficient refinement of multiple structural objectives. Experimental results demonstrate SOLD surpasses its LDM baseline and state-of-the-art methods across all metrics, establishing a robust framework for RNA inverse folding with profound implications for biotechnological and therapeutic applications.

Recently, Multi-View Graph Clustering (MVGC) methods have achieved significant progress, leading to their wide adoption in various applications. However, most MVGC methods merely pursue consistent information by simply fusing multi-view graphs, ignoring the cross-view interactions among them, which limits the ceiling of their performance. To make up for this deficiency, we design a credible cross-view graph enhancement module to explore the credible topological structure, while accomplishing cross-view interactions, to boost clustering performance in multi-view graph scenarios. Besides, we reconsider the graph clustering task from the perspective of graph signal processing. From this novel perspective, we adapt the high-order Graph Trend Filter to reveal the inhomogeneities in graph smoothness levels and further consider the brand-new local preference in MVGC, which provides theoretical guidance for graph clustering. Building on these insights, we propose the Enhanced Graph Trend Filter Clustering (EGTFC) method and present an effective algorithm accompanied by corresponding theoretical analyses to tackle the optimization problem inherent in EGTFC. Finally, substantial experimental results on twelve benchmark datasets demonstrate the effectiveness of our proposals and the superiority over thirteen state-of-the-art MVGC methods.

Multi-view Graph Clustering (MGC) is a crucial approach for uncovering complex data structures by leveraging multiple perspectives of data. However, existing MGC methods face two key challenges: (1) limitations in graph structure that neglect long-range dependencies, and (2) overlooking the view-cluster local structure when mining view discrepancies. To address these issues, we propose a Multi-view Graph Clustering approach based on Dual View-Cluster-Order Interactivity (DVCOI-MGC). This approach consists of three modules: (1) Multi-View Multi-Order Graph Construction, where high-order graphs are generated using matrix exponentiation to capture long-range dependencies; (2) Dual View-Cluster-Order Interactivity, which utilizes a discrete graph cut model to separately learn order-specific and view-specific clustering results from the sets of order-specific multi-view graphs and view-specific multi-order graphs, with a separate View-Cluster-Order tensor weight for each learning direction; and (3) Bidirectional Truncation Consistency Learning, which applies a sparse boolean weight vector to locally select and integrate clustering results while preserving both the view-cluster and order-cluster local structures. Additionally, we introduce an efficient iterative optimization method to solve the discrete graph cut problem and provide a theoretical analysis of its convergence and computational complexity. Extensive experiments on 8 real-world datasets demonstrate that our approach significantly improves clustering performance over 11 state-of-the-art methods.

In recent years, multi-view graph clustering (MVGC) has attracted increasing attention from researchers. However, many existing MVGC methods focus on view-level integration through strategies like assigning weights to different views, for example, ignoring cross-view interactions between nodes. In fact, cross-view interactions at node level are crucial for extraction and fusion of semantic information. Additionally, some methods separate representation learning from clustering, which results in suboptimal clustering performance. To address these problems, we propose a novel unsupervised cross-view message passing method for MVGC. The kernel of our method is the cross-view interaction mechanism, which dynamically constructs node-specific cross-view edges based on node features and structural information. The mechanism enables adaptive interactions of informative nodes from different views, which promotes the extraction and propagation of complementary information. Besides, our method unifies representation learning and hyperspherical clustering in an end-to-end framework, which projects node representations into a hypersphere space, thereby enabling direct acquisition of balanced clustering results without dependence on external clustering methods. We provide comprehensive analyses on our method, and evaluate our method on six multi-view datasets. The results show that our method consistently achieves superior performance than existing state-of-the-art multi-view clustering methods.

In this paper, we present a novel Self-Supervised Learning (SSL) framework tailored for Multi-View Clustering (MVC), which learns cross-view semantic representations with clear clustering boundaries and derives balanced clustering in an end-to-end manner. Concretely, we propose a generative SSL module that learns high-level semantic representations by recovering randomly masked views from observed views. Then the extracted representations are unified via a sample-level local fusion mechanism and projected into a unit-hypersphere space with evenly distributed cluster prototypes such that the pseudo labels can be directly retrieved using cosine similarity. For each sample, we define highly credible positive pairs of the same cluster and negative pairs of different clusters and design a contrastive SSL module to force the sample to move toward its cluster prototype while farther from the other prototypes in the embedding space. Consequently, the representations exhibit clearer clustering boundaries, and the two SSL modules benefit each other. Finally, we further introduce a clustering regularizer to prevent trivial solutions and derive balanced clustering with theoretical guarantees. Comprehensive evaluations over eight benchmark datasets validate the effectiveness of our proposals against ten state-of-the-art MVC methods.

Deep Multi-View Clustering (MVC) methods partition multi-view data into disjoint clusters in an unsupervised manner, showing significant promise across various domains. However, current MVC methods primarily focus on capturing the consistency information shared across all views and undervalue the specificity information inherent in each view that reflects its unique characteristics. Furthermore, the underexploration of the separability of learned representations limits the overall clustering performance of existing MVC methods and leads to undesirable clustering results. In this paper, we propose a fully differentiable and end-to-end deep MVC framework, named Comprehensive Information Extraction with Separable Representation Learning (CIRSEL), to address these issues. CIRSEL recasts specificity information extraction as a high-order graph pooling process to capture the view-specific characteristics of individual views. Utilizing the cross-attention mechanism, CIRSEL adaptively fuses the consistent and view-specific representations to achieve comprehensive information extraction. Subsequently, CIRSEL maps representations into a unit hypersphere space with evenly distributed prototypes and maximizes the variational estimation of Mutual Information, which enhances the inter-cluster separability and intra-cluster compactness in the embedding space and further benefits the following clustering learning. Finally, CIRSEL introduces a nuclear norm-based balance regularization, which ensures balanced clustering results can be directly retrieved by the cosine similarity between the representations and prototypes. Extensive experiments on ten benchmark datasets demonstrate the effectiveness of CIRSEL compared to sixteen current MVC methods.

Most existing multi-view graph clustering models focus on integrating the topological structure of different views directly, which cannot efficiently stimulate the collaboration between multiple views. To alleviate this problem, this paper proposes a Triangle Topology Enhancement (T2E) module, which expands two topological structures based on the raw topology of each view, including the self-triangle enhanced topology that highlights the local view information and the cross-view triangle enhanced topology containing the global-local view information. Afterward, this paper designs a novel multi-view graph clustering model, named MGC-T2E, to integrate both the raw and derived topological structures and directly induce consistent clustering indicators based on a self-supervised clustering module. In the simulation, the experimental results demonstrate that MGC-T2E achieves state-of-the-art performances compared with a mass of current competitors.

Most existing multi-view graph clustering models either seek consistent clustering results from similarity matrices and spectral embeddings respectively or follow direct bidirectional integration of them, which ignores the interaction between them. To make up for this flaw, this paper designs a novel multi-view clustering model that performsBidirectionalFusion withCross-viewGraphFilter (BF-CGF). To be specific, BF-CGF first learns a consistent graph embedding via performing the interaction between multi-view graphs and spectral embeddings with the perspective of the graph spectral domain and then considers seeking a consistent indicator matrix via the graph cut model from the consistent graph embedding and the similarity matrices. To solve the optimization problem of BF-CGF, we propose an efficient iterative algorithm and provide the corresponding convergence and complexity analyses. Extensive experimental results demonstrate that the proposed BF-CGF outperforms state-of-the-art competitors in most benchmark datasets.

Recently, graph-based multi-view clustering (GMC) has attracted extensive attention from researchers, in which multi-view clustering based on structured graph learning (SGL) can be considered as one of the most interesting branches, achieving promising performance. However, most of the existing SGL methods suffer from sparse graphs lacking useful information, which normally appears in practice. To alleviate this problem, we propose a novel multi-view and multi-order SGL ( [Formula: see text]SGL) model which introduces multiple different orders (multi-order) graphs into the SGL procedure reasonably. To be more specific, [Formula: see text]SGL designs a two-layer weighted-learning mechanism, in which the first layer truncatedly selects part of views in different orders to retain the most useful information, and the second layer assigns smooth weights into retained multi-order graphs to fuse them attentively. Moreover, an iterative optimization algorithm is derived to solve the optimization problem involved in [Formula: see text]SGL, and the corresponding theoretical analyses are provided. In experiments, extensive empirical results demonstrate that the proposed [Formula: see text]SGL model achieves the state-of-the-art performance in several benchmarks.

With the proliferation of multimedia applications, data is frequently derived from multiple sources, leading to the accelerated advancement of multi-view clustering (MVC) methods. In this paper, we propose a novel MVC method, termed GLSEF, to handle the inconsistency existing in multiple spectral embeddings. To this end, GLSEF contains a two-level learning mechanism. Specifically, on the global level, GLSEF considers the diversity of features and selectively assigns smooth weights to partial more discriminative features that are conducive to clustering. On the local level, GLSEF resorts to the Grassmann manifold to maintain spatial and topological information and local structure in each view, thereby enhancing its suitability and accuracy for clustering. Moreover, unlike most previous methods that learn a low-dimension embedding and perform the k-means algorithm to obtain the final cluster labels, GLSEF directly acquires the discrete indicator matrix to prevent potential information loss during post-processing. To address the optimization involved in GLSEF, we present an efficient alternating optimization algorithm accompanied by convergence and time complexity analyses. Extensive empirical results on nine real-world datasets demonstrate the effectiveness and efficiency of GLSEF compared to existing state-of-the-art MVC methods.

BACKGROUND: One of the major challenges in precision medicine is accurate prediction of individual patient's response to drugs. A great number of computational methods have been developed to predict compounds activity using genomic profiles or chemical structures, but more exploration is yet to be done to combine genetic mutation, gene expression, and cheminformatics in one machine learning model. RESULTS: We presented here a novel deep-learning model that integrates gene expression, genetic mutation, and chemical structure of compounds in a multi-task convolutional architecture. We applied our model to the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets. We selected relevant cancer-related genes based on oncology genetics database and L1000 landmark genes, and used their expression and mutations as genomic features in model training. We obtain the cheminformatics features for compounds from PubChem or ChEMBL. Our finding is that combining gene expression, genetic mutation, and cheminformatics features greatly enhances the predictive performance. CONCLUSION: We implemented an extended Graph Neural Network for molecular graphs and Convolutional Neural Network for gene features. With the employment of multi-tasking and self-attention functions to monitor the similarity between compounds, our model outperforms recently published methods using the same training and testing datasets.