Phosphorylation site prediction based on kinase-substrate interaction plays a vital role in understanding cellular signaling pathways and disease mechanisms. Computational methods for this task can be categorized into kinase-family-focused and individual kinase-targeted approaches. Individual kinase-targeted methods have gained prominence for their ability to explore a broader protein space and provide more precise target information for kinase inhibitors. However, most existing individual kinase-based approaches focus solely on sequence inputs, neglecting crucial structural information. To address this limitation, we introduce SAGEPhos (Structure-aware kinAse-substrate bio-coupled and bio-auGmented nEtwork for Phosphorylation site prediction), a novel framework that modifies the semantic space of main protein inputs using auxiliary inputs at two distinct modality levels. At the inter-modality level, SAGEPhos introduces a Bio-Coupled Modal Fusion method, distilling essential kinase sequence information to refine task-oriented local substrate feature space, creating a shared semantic space that captures crucial kinase-substrate interaction patterns. Within the substrate's intra-modality domain, it focuses on Bio-Augmented Fusion, emphasizing 2D local sequence information while selectively incorporating 3D spatial information from predicted structures to complement the sequence space. Moreover, to address the lack of structural information in current datasets, we contribute a new, refined phosphorylation site prediction dataset, which incorporates crucial structural elements and will serve as a new benchmark for the field. Experimental results demonstrate that SAGEPhos significantly outperforms baseline methods, notably achieving almost 10\% and 12\% improvements in prediction accuracy and AUC-ROC, respectively. We further demonstrate our algorithm's robustness and generalization through stable results across varied data partitions and significant improvements in zero-shot scenarios. These results underscore the effectiveness of constructing a larger and more precise protein space in advancing the state-of-the-art in phosphorylation site prediction. We release the SAGEPhos models and code at https://github.com/ZhangJJ26/SAGEPhos.

In biological research, fluorescence staining is a key technique to reveal the locations and morphology of subcellular structures. However, it is slow, expensive, and harmful to cells. In this paper, we model it as a deep learning task termed subcellular structure prediction (SSP), aiming to predict the 3D fluorescent images of multiple subcellular structures from a 3D transmitted-light image. Unfortunately, due to the limitations of current biotechnology, each image is partially labeled in SSP. Besides, naturally, subcellular structures vary considerably in size, which causes the multi-scale issue of SSP. To overcome these challenges, we propose Re-parameterizing Mixture-of-Diverse-Experts (RepMode), a network that dynamically organizes its parameters with task-aware priors to handle specified single-label prediction tasks. In RepMode, the Mixture-of-Diverse-Experts (MoDE) block is designed to learn the generalized parameters for all tasks, and gating re-parameterization (GatRep) is performed to generate the specialized parameters for each task, by which RepMode can maintain a compact practical topology exactly like a plain network, and meanwhile achieves a powerful theoretical topology. Comprehensive experiments show that RepMode can achieve state-of-the-art overall performance in SSP.

Image retrieval with text feedback is an emerging research topic with the objective of integrating inputs from multiple modalities as queries. In this paper, queries contain a reference image plus text feedback that describes modifications between this image and the desired image. The existing work for this task mainly focuses on designing a new fusion network to compose the image and text. Still, little research pays attention to the modality gap caused by the inconsistent distribution of features from different modalities, which dramatically influences the feature fusion and similarity learning between queries and the desired image. We propose a Distribution-Aligned Text-based Image Retrieval (DATIR) model, which consists of attention mutual information maximization and hierarchical mutual information maximization, to bridge this gap by increasing non-linear statistic dependencies between representations of different modalities. More specifically, attention mutual information maximization narrows the modality gap between different input modalities by maximizing mutual information between the text representation and its semantically consistent representation captured from the reference image and the desired image by the difference transformer. For hierarchical mutual information maximization, it aligns distributions of features from the image modality and the fusion modality by estimating mutual information between a single-layer representation in the fusion network and the multi-level representations in the desired image encoder. Extensive experiments on three large-scale benchmark datasets demonstrate that we can bridge the modality gap between different modalities and achieve state-of-the-art retrieval performance.

Distance metric learning has been shown to be an effective and efficient method which can lead to significant improvements in classification, clustering, and retrieval. Conventional metric learning methods which apply a global metric to capture the input features and their correlations are unsuitable for heterogeneous data, whose features vary according to different clusters. Thus, a number of local metric learning methods have been proposed to overcome this limitation. Unfortunately, most of them suffer from over-fitting or need the data set to be under a strong assumption, such as Gaussian distribution. To avoid over-fitting caused by learning local metric matrices separately in the previous researches, we propose a novel local metric learning method in which we learn a metric matrix for each pair of instances as linear combinations of basis metric matrices defined on different clusters of the input space without any assumption for data set. We get the parameters of the linear combinations by using the relationship between direct distance and anchor~distance between a pair of data points. Additionally, we design a novel expectation-maximization based optimization method to learn the linear combinations and basis metric matrices. Experimental results show that our method outperforms state-of-the-art methods on various data sets.