Applying computational methods to predict drug-protein interactions can accelerate drug discovery. An ideal computational method should identify false negative samples within the covered space of its dataset, known as introspection, and exhibit good generalizability and scalability beyond the covered space. However, current computational methods have struggled to achieve both objectives simultaneously. In this paper, we propose CombDPI, which leverages similarity reconstruction and an adaptive combination algorithm. It comprises two parts. The first part focuses on predicting false negative samples within the dataset's covered space. CombDPI assesses similarity from various perspectives, enhances reliability by reconstructing similarity relationships, and utilizes an adaptive combination algorithm to complete the predictions. The second part of CombDPI is dedicated to predicting interactions for novel drugs or proteins. In this part, the model utilizes the similarity between novel drugs or proteins and known drugs or proteins to create their respective representations. By leveraging these representations for prediction, CombDPI becomes less dependent on the prediction of other drug-protein pairs, thereby greatly enhancing its generalizability and scalability. Experimental results on three benchmark datasets, with both in-space and out-space settings, demonstrate that CombDPI outperforms existing interaction prediction methods. Furthermore, case studies highlight CombDPI's ability to discover potential drug-protein interactions.

Yinhao Bai, Yalan Xie, Xiaoyi Liu, Yuhua Zhao, Zhixin Han, Mengting Hu, Hang Gao, Renhong Cheng. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Rui Ying, Mengting Hu, Jianfeng Wu, Yalan Xie, Xiaoyi Liu, Zhunheng Wang, Ming Jiang, Hang Gao, Linlin Zhang, Renhong Cheng. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Mengting Hu, Shiwan Zhao, Honglei Guo, Chao Xue, Hang Gao, Tiegang Gao, Renhong Cheng, Zhong Su. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Aspect-based sentiment analysis (ABSA) is to predict the sentiment polarity towards a particular aspect in a sentence.Recently, this task has been widely addressed by the neural attention mechanism, which computes attention weights to softly select words for generating aspect-specific sentence representations.The attention is expected to concentrate on opinion words for accurate sentiment prediction.However, attention is prone to be distracted by noisy or misleading words, or opinion words from other aspects.In this paper, we propose an alternative hard-selection approach, which determines the start and end positions of the opinion snippet, and selects the words between these two positions for sentiment prediction.Specifically, we learn deep associations between the sentence and aspect, and the long-term dependencies within the sentence by leveraging the pre-trained BERT model.We further detect the opinion snippet by selfcritical reinforcement learning.Especially, experimental results demonstrate the effectiveness of our method and prove that our hardselection approach outperforms soft-selection approaches when handling multi-aspect sentences.

Mengting Hu, Yike Wu, Shiwan Zhao, Honglei Guo, Renhong Cheng, Zhong Su. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Mengting Hu, Shiwan Zhao, Li Zhang, Keke Cai, Zhong Su, Renhong Cheng, Xiaowei Shen. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.