- 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）
- 01-04任玉琪 Capsule间的动态路由
2017年12月3日，接到编辑部通知，博士生刘晓霞的论文“The impact of protein interaction networks' characteristics on computational complex detection methods”被Journal of Theoretical Biology录用，该期刊是影响因子为2.113的SCI期刊。
Protein complexes of physically interacting proteins play an important role in organizing various biological processes in the cell. Therefore, correctly identifying complexes is useful for deciphering the cellular mechanisms underlying many biological processes. Since the existing high-throughput techniques have produced a large amount of protein interactions, computational methods are useful complements to the experimental methods for detecting protein complexes. In this paper, we analyze six protein interaction networks widely used for protein complex detection, and compare the performance of six classic computational methods on them in order to find the impacts of network characteristics on the performances of these complex detection methods. Furthermore, we change topological characteristics of six protein interaction networks, and verify the findings by testing performances of six methods on new ones. We hope our study will not only help recognize the relations between characteristics of protein interaction networks and computational complex detection methods, but also provide valuable insight to improve the performance in protein complex detection area.