Integrated Knowledge Graph and Drug Molecular Graph Fusion via Adversarial Networks for Drug-Drug Interaction Prediction.

The Co-administration of multiple drugs can enhance the efficacy of disease treatment by reducing drug resistance and side effects. However, it also raises the risk of adverse drug interactions, presenting a challenging problem in healthcare. Various approaches have been developed to predict drug-drug interactions (DDIs) by leveraging both knowledge graphs and drug attribute information.

MRGCDDI: Multi-Relation Graph Contrastive Learning without Data Augmentation for Drug-Drug Interaction Events Prediction.

Predicting drug-drug interactions (DDIs) is a significant concern in the field of deep learning. It can effectively reduce potential adverse consequences and improve therapeutic safety. Graph neural network (GNN)-based models have made satisfactory progress in DDI event prediction.

A Carbene Relay Strategy for Cascade Insertion Reactions.

Insertion reactions that involve stabilized electrophilic metallocarbenes are of great importance for installing α-heteroatoms to carbonyl compounds. Nevertheless, the limited availability of carbene precursors restricts the introduction of only a single heteroatom. In this report, we describe a new approach based on an I /S reagent that promotes the cascade insertion of heteroatoms.

Improving data augmentation for low resource speech-to-text translation with diverse paraphrasing.

High quality end-to-end speech translation model relies on a large scale of speech-to-text training data, which is usually scarce or even unavailable for some low-resource language pairs. To overcome this, we propose a target-side data augmentation method for low-resource language speech translation. In particular, we first generate large-scale target-side paraphrases based on a paraphrase generation model which incorporates several statistical machine translation (SMT) features and the commonly used recurrent neural network (RNN) feature.

Improving Loanword Identification in Low-Resource Language with Data Augmentation and Multiple Feature Fusion.

Loanword identification is studied in recent years to alleviate data sparseness in several natural language processing (NLP) tasks, such as machine translation, cross-lingual information retrieval, and so on. However, recent studies on this topic usually put efforts on high-resource languages (such as Chinese, English, and Russian); for low-resource languages, such as Uyghur and Mongolian, due to the limitation of resources and lack of annotated data, loanword identification on these languages tends to have lower performance. To overcome this problem, we first propose a lexical constraint-based data augmentation method to generate training data for low-resource language loanword identification; then, a loanword identification model based on a log-linear RNN is introduced to improve the performance of low-resource loanword identification by incorporating features such as word-level embeddings, character-level embeddings, pronunciation similarity, and part-of-speech (POS) into one model.