The recent interest in peptides incorporating non-canonical amino acids has surged within the scientific community, driven by their enhanced stability and resistance to proteolytic degradation. These so-called non-canonical peptides offer significant potential for modifying biological, pharmacological, and physiochemical characteristics in both native and synthetic contexts. Despite their advantages, there remains a notable gap in the availability of an efficient pre-trained model capable of effectively capturing feature representations from such intricate peptide sequences. This study herein introduces PepLand, a novel pre-training framework designed for the comprehensive representation and analysis of peptides, encompassing both canonical and non-canonical amino acids. PepLand leverages a general-purpose multi-view heterogeneous graph neural network to unveil the subtle structural representations of peptides. Our empirical evaluations demonstrate PepLand's proficiency in a range of peptide property prediction tasks, including cell penetrability, solubility, and protein-peptide binding affinity. These rigorous assessments affirm PepLand's superior capability in discerning critical representations of peptides with both canonical and non-canonical amino acids, and provide a robust foundation for transformative advances in peptide-focused pharmaceutical research. We have made the entire source code and datasets available at http://www.healthinformaticslab.org/supp/resources.php or https://github.com/zhangruochi/PepLand.

With the proliferation of Web services, it is very difficult for inexperienced users to select the most appropriate service among numerous functionally identical or similar candidates, thus prediction of Quality of Service (QoS) becomes a growing concern in service discovery, selection and recommendation. However, a huge challenge is that in the reality of the existence of untrustworthy users, how the Web service recommendation system keeps the robustness of the QoS prediction while maintaining high accuracy. To address this problem, a Reputation Integrated Graph Convolution Network (RIGCN) is developed in this paper to realize robust and accurate QoS prediction. RIGCN has three main parts: Reputation Extraction, Multi-source Feature Extraction and GCN-based QoS Prediction. First, an Outlier and Pattern Measure (OPM) method is proposed to extract the real reputation of users based on both outliers and the distribution patterns of the historical QoS interaction records. Second, deep features of users and services are captured by multi-source feature extraction with an attention mechanism to make full use of the contextual information in service invocation. On this basis, a graph convolution network is specially designed to integrate multi-source features and user reputation in the message propagation process to complete final QoS prediction. Experimental results demonstrate that our RIGCN approach can not only extract and utilize implicit and explicit features of various multi-source data, but also can reduce the negative influence of untrustworthy users. Therefore, it is very robust and effective in improving the accuracy of QoS prediction with sparse and noisy data.

With the explosive growth of Web services, the increasing sparse and dynamic QoS (quality of service) data pose a great challenge to QoS prediction in service recommendation. How to fully utilize the contextual information of service invocation and their potential relationships becomes the key to improving the accuracy of QoS prediction. In this paper, a spatial context-aware time series forecasting (SCATSF) framework is proposed for QoS prediction by considering both the temporal and the spatial context of users and services. SCATSF has three main parts: time-aware neighbors selection, spatial context-aware interaction learning and time series forecasting for QoS prediction. First, a novel time series similarity (TSS) is proposed to measure the similarity of users or services based on time-varying QoS fluctuation, in order to select appropriate neighbors for the target user or service. Furthermore, the contextual information of neighbors, as well as the contextual information of users and services, are both integrated to enrich the spatial context of service invocation, and a pairwise multi-layer deep network (PMLDN) is developed to consolidate multiple features and to learn feature interactions in pairs. With the help of spatial contextual information and time-aware QoS information, a spatial context-aware GRU model (SCA-GRU) is finally present to complete the time series forecasting for QoS prediction. Experimental results on the prediction of response time in a real-world dataset demonstrate that our SCATSF approach can effectively utilize spatio-temporal contextual information, so that it can achieve higher accuracy than many existing methods.