Improving the ability to predict protein function can potentially facilitate research in the fields of drug discovery and precision medicine. Technically, the properties of proteins are directly or indirectly reflected in their sequence and structure information, especially as the protein function is largely determined by its spatial properties. Existing approaches mostly focus on protein sequences or topological structures, while rarely exploiting the spatial properties and ignoring the relevance between sequence and structure information. Moreover, obtaining annotated data to improve protein function prediction is often time-consuming and costly. To this end, this work proposes a novel contrast-aware pre-training framework, called SCOP, for protein function prediction. We first design a simple yet effective encoder to integrate the protein topological and spatial features under the structure view. Then a convolutional neural network is utilized to learn the protein features under the sequence view. Finally, we pretrain SCOP by leveraging two types of auxiliary supervision to explore the relevance between these two views and thus extract informative representations to better predict protein function. Experimental results on four benchmark datasets and one self-built dataset demonstrate that SCOP provides more specific results, while using less pre-training data.

For a clinical prescription, clarifying the molecular mechanisms of actions (MMOAs) of the drug-disease interaction is helpful to optimize treatment, suggest possible side effects, and realize individualized treatment. Considering the relations among multiple biomedical entities, such as drugs, diseases, targets (genes), and pathways, what paths can be extracted connecting these biomedical entities to resemble the real mechanisms of a specific drug to a particular disease? Answering this question is crucial for understanding the underlying molecular mechanisms behind complex drug actions and identifying key pathways that can facilitate effective therapeutic interventions. In this paper, we propose an approach DDTExplainer that constructs a path-based graph neural network (GNN) explainer to mine the drug-disease therapeutic mechanisms. Technically, DDTExplainer transforms the drug-disease therapeutic mechanisms mining task into a GNN-based link prediction model explanation task. Firstly, a GNN-based drug-disease therapeutic prediction model is trained and joint-optimized with a translation-based graph embedding model. Secondly, mask learning is utilized to find the most prediction-influential edges and generate the path-based explanations with the shortest path algorithm. Finally, we assess the efficacy of DDTExplainer on a ground-truth dataset that consists of labeled entries describing the drug-disease therapeutic mechanisms. These labels are derived from well-established drug-target interactions, disease-target interactions, as well as target-pathway relationships, which were verified by wet experiments.

Underlying medical conditions, such as cancer, kidney disease and heart failure, are associated with a higher risk for severe COVID-19. Accurate classification of COVID-19 patients with underlying medical conditions is critical for personalized treatment decision and prognosis estimation. In this study, we propose an interpretable artificial intelligence model termed VDJMiner to mine the underlying medical conditions and predict the prognosis of COVID-19 patients according to their immune repertoires. In a cohort of more than 1400 COVID-19 patients, VDJMiner accurately identifies multiple underlying medical conditions, including cancers, chronic kidney disease, autoimmune disease, diabetes, congestive heart failure, coronary artery disease, asthma and chronic obstructive pulmonary disease, with an average area under the receiver operating characteristic curve (AUC) of 0.961. Meanwhile, in this same cohort, VDJMiner achieves an AUC of 0.922 in predicting severe COVID-19. Moreover, VDJMiner achieves an accuracy of 0.857 in predicting the response of COVID-19 patients to tocilizumab treatment on the leave-one-out test. Additionally, VDJMiner interpretively mines and scores V(D)J gene segments of the T-cell receptors that are associated with the disease. The identified associations between single-cell V(D)J gene segments and COVID-19 are highly consistent with previous studies. The source code of VDJMiner is publicly accessible at https://github.com/TencentAILabHealthcare/VDJMiner. The web server of VDJMiner is available at https://gene.ai.tencent.com/VDJMiner/.

Disease similarity analysis impacts significantly in pathogenesis revealing, treatment recommending, and disease-causing genes predicting. Previous works study the disease similarity based on the semantics obtaining from biomedical ontologies (e.g., disease ontology) or the function of disease-causing molecules. However, such methods almost focus on a single perspective for obtaining disease features, which may lead to biased results for similar disease detection. To address this issue, we propose a disease information network-based integrative approach named MISSION for detecting similar diseases. By leveraging the associations between diseases and other biomedical entities, the disease information network is established first. Then, the disease similarity features extracted from the aspects of disease taxonomy, attributes, literature, and annotations are integrated into the disease information network. Finally, the top-k similar disease query is performed based on the integrative disease information. The experiments conducted on real-world datasets demonstrate that MISSION is effective and useful in similar disease detection.

Gene similar aspects provide reliable explanation in understanding the biological roles and gene functions. As the volume of biomedical data expands, most of the current methods for similar explanation among genes are no longer applicable. Limited by information sources and search effciency, these methods cannot be flexible and effcient for the similarity analysis. We hereby propose a flexible method VENUS to analyze gene similar aspect among multiple genes on heterogeneous information networks, which constructed from public biomedicine databases and literature. VENUS infers the semantic and structural similarity of the query genes by gene community search. In this way, VENUS narrows the search space when searching information network within an acceptable time cost. Besides, VENUS is not limited by inherent domain knowledge and is adaptive to large-scale networks. Through experiments on multiple different public data sources, it demonstrates that VENUS is effective and effcient.

Predicting molecular properties has significant implications for the discovery and generation of drugs and further research in the domain of medicinal chemistry. Learning representations of molecules plays a central role in deep learning-driven property prediction. However, the diversity of molecular features (e.g., chemical system languages, structure notations) brings inconsistency in molecular representation. Moreover, the scarcity of labeled molecular data limits the accuracy of the molecular property prediction model. To address the above issues, we proposed a two-stage method, named MORN, for learning molecular representations for molecular property prediction from a multi-view perspective. In the first stage, textual-topological-spatial multi-views were proposed to learn the molecular representations, so as to capture both chemical system language and structure notation features simultaneously. In the second stage, an adaptive strategy was used to fuse molecular representations learned from multi-views to predict molecular properties. To alleviate the limitation of the scarcity of labeled molecular data, the label restriction was introduced in both multi-view representation learning and fusion stages. The performance of MORN was assessed by seven benchmark molecular datasets and one self-built molecular dataset. Experimental results demonstrated that MORN is effective in molecular property prediction.

Analysis of gene similarity not only can provide information on the understanding of the biological roles and functions of a gene, but may also reveal the relationships among various genes. In this paper, we introduce a novel idea of mining similar aspects from a gene information network, i.e., for a given gene pair, we want to know in which aspects (meta paths) they are most similar from the perspective of the gene information network. We defined a similarity metric based on the set of meta paths connecting the query genes in the gene information network and used the rank of similarity of a gene pair in a meta path set to measure the similarity significance in that aspect. A minimal set of gene meta paths where the query gene pair ranks the highest is a similar aspect, and the similar aspect of a query gene pair is far from trivial. We proposed a novel method, SCENARIO, to investigate minimal similar aspects. Our empirical study on the gene information network, constructed from six public gene-related databases, verified that our proposed method is effective, efficient, and useful.

The emerging COVID-19 variants lead to a new wave of infections, spreading more rapidly with more severe illnesses. The adaptive immune system plays an essential role in the control and clearance of viral infection and influences clinical outcomes. However, the understanding of the adaptive immune responses to COVID-19 is not sufficient, which impedes the development progress of treatments and vaccines. To address this issue, we proposed a machine-learning-based method (termed as VDJ-Seg-Miner) to mine the underlying associations between the V(D)J gene segments of the T cell receptor in personalized immune repertoires and COVID-19 disease characteristics for immune system analysis. Our VDJ-Seg-Miner can interpretively reveal multiple associations between the V(D)J gene segments and COVID-19 disease characteristics and assign confidence scores to indicate its confidence in each revealed association. Furthermore, experimental results based on the real-world dataset suggested that the identified associations were highly consistent with those reported in previous work.

To identify similar diseases has significant implications for revealing the etiology and pathogenesis of diseases and further research in the domain of biomedicine. Currently, most methods for the measurement of disease similarity utilize either associations of ontological disease concepts or functional interactions between disease-related genes. These methods are heavily dependent on the ontology, which are not always available, and the selection of datasets. Moreover, many methods suffer from a drawback that they only use a single metric to evaluate disease similarity from an individual data source, which may result in biased conclusions without consideration of other aspects. In this study, we proposed a novel ontology-independent framework, namely RADAR, for learning representations for diseases to deduce their similarities from an integrative perspective. By leveraging the associations between diseases and disease-related biomedical entities, a disease similarity network was built under various metrics. Then, a multi-layer disease similarity network was constructed by integrating multiple disease similarity networks derived from multiple data sources, where the representation learning was derived to provide a comprehensive evaluation of disease similarities. The performance of RADAR was assessed by a benchmark disease set and 100 random disease sets. Experimental results demonstrated that RADAR can detect similar diseases effectively.

To detect similar diseases is meaningful for revealing pathogenesis, and predicting therapeutic drugs. Previous methods measure disease similarity almost according to the semantic on biomedical ontology or the function of disease-causing molecules. However, such methods mostly describe diseases from single information, which may lead to a biased description of the relationships among diseases. In this paper, we propose a novel approach, called MISSION, for measuring the disease similarity based on multimodal-information. MISSION enhances similar disease detection based on disease information network from three aspects, including disease ontology, attribute, and literature, therefore providing a comprehensive evaluation for disease similarity. Through experiments on real-world datasets, we demonstrate that MISSION is effective, efficient, and potentially useful. Further analysis shows that MISSION has the ability to detect similar diseases with varying degrees of rich information.

With the rapid development of anti-tumor biomaterials, biomedicine literature with respect to anti-tumor biomaterials has been leveraged for tumor treatment as it provides abundant and useful information. A large number of biomedicine literature contains unstructured data, making it difficult for researchers to obtain desired messages from it. Knowledge Graphs (KGs) provides structured relationships among entities and can be served as a solution. However, no existing tool can be found in constructing an anti-tumor biomaterial knowledge graph from biomedicine literature. To fill this gap, a novel approach, ATOM, was proposed to construct an anti-tumor biomaterial knowledge graph from biomedicine literature through a series of process including the recognition of anti-tumor entities, the simplification of sentences, the extraction of triples, and the predicate mapping. Experiments demonstrated that ATOM is able to effectively express the extracted anti-tumor entities and their relationships.

Gene similarity analysis not only provides information on understanding the biological roles and functions of a gene, but also reveals the relationships among different genes. In this paper, we identify the novel idea of mining similar aspects from gene information network, i.e., given a pair of genes, we want to know, in which aspects (meta paths) the two genes are mostly similar from the perspective of gene information network? We define a similarity metric based on the set of meta paths connecting the query genes in the gene information network, and use the rank of the similarity of a gene pair in a meta path set to measure the similarity significance in the aspect. A minimal set of meta paths where the query gene pair is ranked the best is a similar aspect. Computing the similar aspects of a query gene pair is far from trivial. In this paper, we propose a novel heuristic based-mining method, SCENARIO, to investigate minimal similar aspects. Our empirical study on the gene information network, constructed from seven public gene-related databases, verified that our proposed method is effective, efficient, and useful.