Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics. In this research, a systematic survey of GNNs and their advances in bioinformatics is presented from multiple perspectives. We first introduce some commonly used GNN models and their basic principles. Then, three representative tasks are proposed based on the three levels of structural information that can be learned by GNNs: node classification, link prediction, and graph generation. Meanwhile, according to the specific applications for various omics data, we categorize and discuss the related studies in three aspects: disease prediction, drug discovery, and biomedical imaging. Based on the analysis, we provide an outlook on the shortcomings of current studies and point out their developing prospect. Although GNNs have achieved excellent results in many biological tasks at present, they still face challenges in terms of low-quality data processing, methodology, and interpretability and have a long road ahead. We believe that GNNs are potentially an excellent method that solves various biological problems in bioinformatics research.

Machine learning models may not be able to effectively learn and predict from imbalanced data in the fields of machine learning and data mining. This study proposed a method for analyzing the performance impact of imbalanced binary data on machine learning models. It systematically analyzes 1. the relationship between varying performance in machine learning models and imbalance rate (IR); 2. the performance stability of machine learning models on imbalanced binary data. In the proposed method, the imbalanced data augmentation algorithms are first designed to obtain the imbalanced dataset with gradually varying IR. Then, in order to obtain more objective classification results, the evaluation metric AFG, arithmetic mean of area under the receiver operating characteristic curve (AUC), F-measure and G-mean are used to evaluate the classification performance of machine learning models. Finally, based on AFG and coefficient of variation (CV), the performance stability evaluation method of machine learning models is proposed. Experiments of eight widely used machine learning models on 48 different imbalanced datasets demonstrate that the classification performance of machine learning models decreases with the increase of IR on the same imbalanced data. Meanwhile, the classification performances of LR, DT and SVC are unstable, while GNB, BNB, KNN, RF and GBDT are relatively stable and not susceptible to imbalanced data. In particular, the BNB has the most stable classification performance. The Friedman and Nemenyi post hoc statistical tests also confirmed this result. The SMOTE method is used in oversampling-based imbalanced data augmentation, and determining whether other oversampling methods can obtain consistent results needs further research. In the future, an imbalanced data augmentation algorithm based on undersampling and hybrid sampling should be used to analyze the performance impact of imbalanced binary data on machine learning models.

Software knowledge community contains a large scale of software knowledge entity information, complex structure and rich semantic correlations. It is significant to recognize and extract software knowledge entity from software knowledge community, as it has great impact on entity-centric tasks such as software knowledge graph construction, software document generation and expert recommendation. Since the texts of the software knowledge community are unstructured by user-generated texts, it is difficult to apply the traditional entity extraction method in the domain of the software knowledge community due to the problems of entity variation, entity sparsity, entity ambiguity, out-of-vocabulary (OOV) words and the lack of annotated data sets. This paper proposes a novel software knowledge entity extraction model, named AttenSy-SNER, which integrates syntactic features and semantic augmentation information, to extract fine-grained software knowledge entities from unstructured user-generated content. The input representation layer utilizes Bidirectional Encoder Representations from Transformers (BERT) model to extract the feature representation of the input sequence. The contextual coding layer leverages the Bidirectional Long Short-Term Memory (BiLSTM) network and Graph Convolutional Network (GCN) for contextual information and syntactic dependency information, and a semantic augmentation strategy based on attention mechanism is introduced to enrich the semantic feature representation of sequences as well. The tag decoding layer leverages Conditional Random Fields (CRF) to solve the dependency between the output tags and obtain the global optimal label sequence. The results of model comparison experiments show that the proposed model has better performance than the benchmark model in software engineering domain.

User preference information plays an important role in knowledge graph-based recommender systems, which is reflected in users having different preferences for each entity–relation pair in the knowledge graph. Existing approaches have not modeled this fine-grained user preference feature well, as affecting the performance of recommender systems. In this paper, we propose a deep knowledge preference-aware reinforcement learning network (KPRLN) for the recommendation, which builds paths between user’s historical interaction items in the knowledge graph, learns the preference features of each user–entity–relation and generates the weighted knowledge graph with fine-grained preference features. First, we proposed a hierarchical propagation path construction method to address the problems of the pendant entity and long path exploration in the knowledge graph. The method expands outward to form clusters centered on items and uses them to represent the starting and target states in reinforcement learning. With the iteration of clusters, we can better learn the pendant entity preference and explore farther paths. Besides, we design an attention graph convolutional network, which focuses on more influential entity–relation pairs, to aggregate user and item higher order representations that contain fine-grained preference features. Finally, extensive experiments on two real-world datasets demonstrate that our method outperforms other state-of-the-art baselines.