## 1 Introduction  
Interleukin-10 (IL-10), a pleiotropic cell-signaling cytokine, contributes to immune modulation and inflammation [1]. Its production occurs in various lymphocyte subtypes, including macrophages, B cells, granulocytes, dendritic cells, and multiple T cell subsets [2, 3]. IL-10 was initially discovered by [PERSON] and [PERSON] through cloning of T helper (Th) 2 cells, and was shown to inhibit cytokines produced by Th1 cells [4]. As an immunosuppressive molecule, IL-10 can restrict immune responses against pathogens and microbial communities, which is a key mechanism underlying its anti-inflammatory properties. Evidence suggests that IL-10 is expressed not only in bone marrow and lymphoid lineage cells but also in tumor-associated macrophages [5], epithelial cells [6], and innate immune cells of the central nervous system [7]. This broad anti-inflammatory profile has significant effects in preventing autoimmune diseases [8], balancing neuroimmune responses [7], and cancer therapy [9]. Identifying IL-10 target proteins through wet laboratory experiments is time-consuming and costly. Additionally, the complexity of the immune system and the workload involved in prediction are high. With the rapid development of immunoinformatics tools and immune epitope databases, combining machine learning (ML) with massive epitope data to construct direct or indirect peptide prediction models has become an area of increased focus [10, 11, 12, 13, 14, 15, 16]. Some methods to predict IL-10-induced epitopes have been developed based on ML and the largest immune epitope database, IEDB (Immune Epitope Database) [17, 18]. [PERSON] et al. first developed a computational model, IL-10 Pred, in 2017 to predict peptidesthat can induce IL-10 production. IL-10 Pred is a cytokine-specific prediction method [19] that extracts features of IL-10-induced peptides using singular amino acid sequences to construct models using support vector machines (SVM). A model based on random forest developed using DPC achieved the best performance, with accuracy (ACC) = 0.812. Subsequently, in 2021, [PERSON] et al. proposed ILeukin10 Pred, another prediction model for IL-10-induced peptides developed based on amino acid sequence features [20]. Independent testing and five-fold cross-validation both indicated that ILeukin10 Pred showed improved predictive performance compared to IL-10 Pred. Although significant progress has been made in the prediction of IL-10-induced peptides, the performance of ML-based models utilizing sequence information still requires further improvement. Many research methods have made it feasible. In other fields, such as image processing, certain algorithmic models can offer assistance for biomedical issues. For instance, generative adversarial networks [21], deformation models [22], gated recurrent units [23], and dual-level representation enhancement networks [24] exhibit strengths in data handling, feature representation, and model optimization. As considered by ILeukin10 Pred, it is essential to extract and encode features based on a wider range of amino acid characteristics for accurately predicting amino acids. However, the key to enhancing model performance lies in effectively fusing different types of data [25; 26]. Additionally, [PERSON] et al. mentioned in their study that using ensemble models may improve predictive performance [20]. To address these issues, this research proposes a novel IL-10-induced peptide recognition method, IL10-Stack, based on unified deep representation learning and the stacking algorithm. Unified deep representation learning can harmonize diverse data types, facilitating comprehensive analysis after data fusion [27]. Meanwhile, stacking, through integrating different models, demonstrates outstanding performance in improving prediction accuracy and generalization [28; 29]. The Amino Acid Index (AAindex) is a numerical index database that provides information on the physicochemical and biological properties of the 20 amino acids [30].