- 10-14王鑫雷 The APVA-TURBO Approach to Question Answering in Knowledge Base
- 05-20李楠 Generalizing Biomedical Relation Classification with Neural Adversarial Domain Adaptation
- 04-23李青青 Deep learning for drug-drug interaction extraction
- 04-15刁宇峰 AAAI2018中的自注意力机制(Self-attention Mechanism)
- 04-15王治政 Label informed Attributed Network Embedding
- 03-24罗凌 自然语言处理中的自注意力机制（Self-attention Mechanism）
近日，接到编辑部通知，博士生刘晓霞的论文 "Identifying protein complexes based on node embeddings obtained from protein-protein interaction networks" 被BMC Bioinformatics录用，该期刊是影响因子为2.448的SCI期刊。论文摘要如下：
Background: Protein complexes are one of the keys to deciphering the behavior of a cell system. During the past decade, most computational approaches used to identify protein complexes have been based on discovering densely connected subgraphs in protein-protein interaction (PPI) networks. However, many true complexes are not dense subgraphs and these approaches show limited performances for detecting protein complexes from PPI networks.
Results: To solve these problems, in this paper we propose a supervised learning method based on network node embeddings which utilizes the informative properties of known complexes to guide the search process for new protein complexes. First, node embeddings are obtained from human protein interaction network. Then the protein interactions are weighted through the similarities between node embeddings. After that, the supervised learning method is used to detect protein complexes. Then the random forest model is used to filter the candidate complexes in order to obtain the final predicted complexes. Experimental results on real human and yeast protein interaction networks show that our method effectively improves the performance for protein complex detection.
Conclusions: We provided a new method for identifying protein complexes from human and yeast protein interaction networks, which has great potential to benefit the field of protein complex detection.