Improving Multimodal Protein Function Prediction Using Bidirectional Interaction and Dynamic Selection Mechanisms
Keywords: Multimodal, protein function prediction, multi-label classification
Abstract: Protein function prediction is pivotal for uncovering the mechanisms of life processes. Protein function prediction is a multi-label classification task with numerous functional labels that exhibit hierarchical relationships. Relying solely on unimodal protein features is insufficient for computational models to capture complex protein functions adequately. Recently, several methods for protein function prediction have enhanced the performance by integrating multimodal protein features. However, since multimodal protein features describe protein functions from different perspectives, it is challenging to capture the intricate relationships among these multimodal features with different meanings and heterogeneity. Therefore, we propose a multimodal method for protein function prediction that can effectively utilize the intricate internal relationships between spatial structure features (i.e., protein-protein interaction network, subcellular location, and protein domains) and sequence features (i.e., amino acid sequence). In this work, we introduce the Bidirectional Interaction Module (BInM) to facilitate interactive learning between multimodal features by mapping spatial structure and sequence features of proteins to each other. Moreover, to deal with the difficulty of hierarchical multi-label classification in this task, a multi-branch Dynamic Selection Module (DSM) is designed to select the feature representation that is most favorable for current protein function prediction. Comprehensive experiments on human datasets demonstrate that our model outperforms state-of-the-art multimodal-based methods such as Graph2GO, DeepGraphGO, and CFAGO. Furthermore, we assess the efficacy of the features through Davies-Bouldin scores and t-SNE visualization experiments. The experimental results show that our method constructs more useful protein representations through bidirectional interaction and dynamic selection mechanisms, leading to improved accuracy in protein function prediction. The code in this work will be made public after its acceptance.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 11120
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