Knowledge graph embedding (KGE) is an effective method for link prediction in knowledge graphs, with numerous models demonstrating significant success. However, many existing KGE models struggle to effectively represent complex relationships and often face challenges related to a substantial number of missing triples. While enhancing KGE models through analogical reasoning offers a potential solution, current approaches typically depend on positive samples that are similar to the target for learning analogy embeddings. In real-world applications, data imbalance and incompleteness hinder the identification of sufficient positive examples, resulting in suboptimal performance in analogical inference. To this end, we propose a novel enhanced framework called Ne_AnKGE, which is based on negative analogical reasoning. First, Ne_AnKGE uses negative sample analogical reasoning to mitigate the scarcity of similar positive samples in analogical reasoning, while enhancing the link prediction ability of the basic model. Additionally, to enhance the model’s ability to represent complex relationships and overcome the limitations of relying on a single KGE model, the Ne_AnKGE framework integrates the outputs of TransE and RotatE, both of which have been enhanced through negative sample analogical reasoning. Extensive experiments on the FB15K-237 and WN18RR datasets demonstrate the competitive performance of Ne_AnKGE.

Background: N6-methyladenine (6mA) is an important DNA methylation modification that serves a crucial function in various biological activities. Accurate prediction of 6mA sites is essential for elucidating its biological function and underlying mechanism. Although existing methods have achieved great success, there remains a pressing need for improved prediction accuracy and generalization cap ability across diverse species. This study aimed to develop a robust method to address these challenges. Methods: We proposed HD-6mAPred, a hybrid deep learning model that combines bidirectional gated recurrent unit (BiGRU), convolutional neural network (CNN) and attention mechanism, along with various DNA sequence coding schemes. Firstly, DNA sequences were encoded using four different ways: one-hot encoding, electron-ion interaction pseudo-potential (EIIP), enhanced nucleic acid composition (ENAC) and nucleotide chemical properties (NCP). Secondly, a hold-out search strategy was employed to identify the optimal features or feature combinations for both BiGRU and CNN. Finally, the attention mechanism was introduced to weigh the importance of features derived from the BiGRU and CNN. Results: A series of experiments on the Rosaceae, rice and Arabidopsis datasets were conducted to demonstrate the superiority of HD-6mAPred. In Rosaceae, the HD-6mAPred model achieved excellent performance: accuracy (ACC) of 0.996, Matthew correlation coefficient (MCC) of 0.993, sensitivity (SN) and specificity (SP) of 0.995 and 0.998, respectively. In rice, the evaluation metrics are 0.952 (ACC), 0.905 (MCC), 0.955 (SN), and 0.949 (SP). In Arabidopsis, the corresponding metrics are 0.937 (ACC), 0.875 (MCC), 0.927 (SN), and 0.948 (SP). Compared to existing methods, these results demonstrate that HD-6mAPred achieves state-of-the-art performance in predicting 6mA sites across three plant species. Furthermore, HD-6mAPred not only improves the accuracy of 6mA site prediction, but also shows excellent generalization capability across species. The source code utilized in this study is publicly accessible at https://doi.org/10.5281/zenodo.15355131.

Previous studies from oligonucleotides in the ribosomal protein (RP) genes of the yeast and fruitfly indicated that the potential transcriptional regulatory sites are located in the introns of the genes. The transcriptional regulatory sites in introns are still poorly understood. To explore the functional significance of transcriptional regulation of introns, we extracted over-represented oligonucleotides (also known as motifs) in the first introns of the human and mouse ribosomal protein genes by statistical comparative analysis, and found that over 85% of these oligonucleotides were consistent with the known transcriptional factor binding sites, which might be potential transcriptional regulatory elements. By analyzing the base compositions of these elements, we found that a majority (>95%) of the detected motifs were rich in C and G and only a few of them were rich in A and T. Moreover, the oligonucleotides were close to the 5'-ends of the first introns (the distances between the motifs and the transcriptional start sites or upstream regions of genes are short). We speculated that the properties of over-represented motifs in the first introns might be associated with the transcriptional control.