Identification of new drug–target interactions (DTIs) is an important but a time-consuming and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers have sought to identify DTIs using computational approaches. However, most existing methods construct drug networks and target networks separately, and then predict novel DTIs based on known associations between the drugs and targets without accounting for associations between drug–protein pairs (DPPs). To incorporate the associations between DPPs into DTI modeling, we built a DPP network based on multiple drugs and proteins in which DPPs are the nodes and the associations between DPPs are the edges of the network. We then propose a novel learning-based framework, ‘graph convolutional network (GCN)-DTI’, for DTI identification. The model first uses a graph convolutional network to learn the features for each DPP. Second, using the feature representation as an input, it uses a deep neural network to predict the final label. The results of our analysis show that the proposed framework outperforms some state-of-the-art approaches by a large margin.

Spatial transcriptomics unveils the complex dynamics of cell regulation and transcriptomes, but it is typically cost-prohibitive. Predicting spatial gene expression from histological images via artificial intelligence offers a more affordable option, yet existing methods fall short in extracting deep-level information from pathological images. In this paper, we present THItoGene, a hybrid neural network that utilizes dynamic convolutional and capsule networks to adaptively sense potential molecular signals in histological images for exploring the relationship between high-resolution pathology image phenotypes and regulation of gene expression. A comprehensive benchmark evaluation using datasets from human breast cancer and cutaneous squamous cell carcinoma has demonstrated the superior performance of THItoGene in spatial gene expression prediction. Moreover, THItoGene has demonstrated its capacity to decipher both the spatial context and enrichment signals within specific tissue regions. THItoGene can be freely accessed at https://github.com/yrjia1015/THItoGene.

SC2disease (http://easybioai.com/sc2disease/) is a manually curated database that aims to provide a comprehensive and accurate resource of gene expression profiles in various cell types for different diseases. With the development of single-cell RNA sequencing (scRNA-seq) technologies, uncovering cellular heterogeneity of different tissues for different diseases has become feasible by profiling transcriptomes across cell types at the cellular level. In particular, comparing gene expression profiles between different cell types and identifying cell-type-specific genes in various diseases offers new possibilities to address biological and medical questions. However, systematic, hierarchical and vast databases of gene expression profiles in human diseases at the cellular level are lacking. Thus, we reviewed the literature prior to March 2020 for studies which used scRNA-seq to study diseases with human samples, and developed the SC2disease database to summarize all the data by different diseases, tissues and cell types. SC2disease documents 946 481 entries, corresponding to 341 cell types, 29 tissues and 25 diseases. Each entry in the SC2disease database contains comparisons of differentially expressed genes between different cell types, tissues and disease-related health status. Furthermore, we reanalyzed gene expression matrix by unified pipeline to improve the comparability between different studies. For each disease, we also compare cell-type-specific genes with the corresponding genes of lead single nucleotide polymorphisms (SNPs) identified in genome-wide association studies (GWAS) to implicate cell type specificity of the traits.

Motivation: The functional changes of the genes, RNAs and proteins will eventually be reflected in the metabolic level. Increasing number of researchers have researched mechanism, biomarkers and targeted drugs by metabolites. However, compared with our knowledge about genes, RNAs, and proteins, we still know few about diseases-related metabolites. All the few existed methods for identifying diseases-related metabolites ignore the chemical structure of metabolites, fail to recognize the association pattern between metabolites and diseases, and fail to apply to isolated diseases and metabolites. Results: In this study, we present a graph deep learning based method, named Deep-DRM, for identifying diseases-related metabolites. First, chemical structures of metabolites were used to calculate similarities of metabolites. The similarities of diseases were obtained based on their functional gene network and semantic associations. Therefore, both metabolites and diseases network could be built. Next, Graph Convolutional Network (GCN) was applied to encode the features of metabolites and diseases, respectively. Then, the dimension of these features was reduced by Principal components analysis (PCA) with retainment 99% information. Finally, Deep neural network was built for identifying true metabolite-disease pairs (MDPs) based on these features. The 10-cross validations on three testing setups showed outstanding AUC (0.952) and AUPR (0.939) of Deep-DRM compared with previous methods and similar approaches. Ten of top 15 predicted associations between diseases and metabolites got support by other studies, which suggests that Deep-DRM is an efficient method to identify MDPs. Contact: liangcheng@hrbmu.edu.cn. Availability and implementation: https://github.com/zty2009/GPDNN-for-Identify-ing-Disease-related-Metabolites.

Metabolites disrupted by abnormal state of human body are deemed as the effect of diseases. In comparison with the cause of diseases like genes, these markers are easier to be captured for the prevention and diagnosis of metabolic diseases. Currently, a large number of metabolic markers of diseases need to be explored, which drive us to do this work. The existing metabolite-disease associations were extracted from Human Metabolome Database (HMDB) using a text mining tool NCBO annotator as priori knowledge. Next we calculated the similarity of a pair-wise metabolites based on the similarity of disease sets of them. Then, all the similarities of metabolite pairs were utilized for constructing a weighted metabolite association network (WMAN). Subsequently, the network was utilized for predicting novel metabolic markers of diseases using random walk. Totally, 604 metabolites and 228 diseases were extracted from HMDB. From 604 metabolites, 453 metabolites are selected to construct the WMAN, where each metabolite is deemed as a node, and the similarity of two metabolites as the weight of the edge linking them. The performance of the network is validated using the leave one out method. As a result, the high area under the receiver operating characteristic curve (AUC) (0.7048) is achieved. The further case studies for identifying novel metabolites of diabetes mellitus were validated in the recent studies. In this paper, we presented a novel method for prioritizing metabolite-disease pairs. The superior performance validates its reliability for exploring novel metabolic markers of diseases.

Interactions between proteins and small molecule metabolites play vital roles in regulating protein functions and controlling various cellular processes. The activities of metabolic enzymes, transcription factors, transporters and membrane receptors can all be mediated through protein–metabolite interactions (PMIs). Compared with the rich knowledge of protein–protein interactions, little is known about PMIs. To the best of our knowledge, no existing database has been developed for collecting PMIs. The recent rapid development of large-scale mass spectrometry analysis of biomolecules has led to the discovery of large amounts of PMIs. Therefore, we developed the PMI-DB to provide a comprehensive and accurate resource of PMIs. A total of 49 785 entries were manually collected in the PMI-DB, corresponding to 23 small molecule metabolites, 9631 proteins and 4 species. Unlike other databases that only provide positive samples, the PMI-DB provides non-interaction between proteins and metabolites, which not only reduces the experimental cost for biological experimenters but also facilitates the construction of more accurate algorithms for researchers using machine learning. To show the convenience of the PMI-DB, we developed a deep learning-based method to predict PMIs in the PMI-DB and compared it with several methods. The experimental results show that the area under the curve and area under the precision-recall curve of our method are 0.88 and 0.95, respectively. Overall, the PMI-DB provides a user-friendly interface for browsing the biological functions of metabolites/proteins of interest, and experimental techniques for identifying PMIs in different species, which provides important support for furthering the understanding of cellular processes. The PMI-DB is freely accessible at http://easybioai.com/PMIDB.