Multi-modal clustering aims to integrate complementary information from different modalities to uncover latent consistent structures and improve clustering performance. However, existing methods mainly rely on predictive (result) uncertainty to improve robustness, while often neglecting aleatoric (data) uncertainty introduced by sample noise and epistemic (model) uncertainty induced by model parameters and structural variations. To this end, we propose a novel Full-Stage Trusted Multi-modal Clustering (FSTMC) method. To the best of trust, we jointly utilize aleatoric, epistemic, and predictive uncertainties to optimize the model and learn more reliable feature representations and clustering results. In the representation learning phase, probabilistic modeling is used to capture stable latent representations and estimate aleatoric uncertainty, while structured random perturbations are present to estimate epistemic uncertainty. In the clustering stage, instead of conventional feature-level fusion, we design an evidence-based fusion strategy, where soft labels from each modality are first mapped into categorical evidence while cluster distributions are parameterized via a Dirichlet model, with finally dynamic multi-modal fusion achieved by Dempster-Shafer theory. To mitigate overconfidence and modal conflicts, prior constraints guided by aleatoric and epistemic uncertainty are imposed, resulting in calibrated predictive uncertainty. Finally, we exploit predictive uncertainty to selectively incorporate pseudo labels for optimization. Benchmark experiments on a number of multi-modal datasets demonstrate that our approach significantly improves accuracy compared to state-of-the-art methods.

Matching molecular analogues is a computational chemistry and bioinformatics research issue which is used to identify molecules that are structurally or functionally similar to a target molecule. Recent studies on matching analogous molecules have predominantly concentrated on enhancing effectiveness, often sidelining computational efficiency, particularly in contexts of low computational resources. This oversight poses challenges in many real applications (e.g., drug discovery, catalyst generation and so forth). To tackle this issue, we propose a general strategy named MapLE, aiming to promptly match analogous molecules with low computational resources by multi-metrics evaluation. Experimental evaluation conducted on a public biomolecular dataset validates the excellent and efficient performance of the proposed strategy.

Gathering information from multi-perspective graphs is an essential issue for many applications especially for proteinligand binding affinity prediction. Most of traditional approaches obtained such information individually with low interpretability. In this paper, we harness the rich information from multi-perspective graphs with a general model, which abstractly represents protein-ligand complexes with better interpretability while achieving excellent predictive performance. In addition, we specially analyze the protein-ligand binding affinity problem, taking into account the heterogeneity of proteins and ligands. Experimental evaluations demonstrate the effectiveness of our data representation strategy on public datasets by fusing information from different perspectives.