基于多示例多标记迁移学习的蛋白质功能预测

Protein function prediction through multi-instance multi-label transfer learning

随着各种基因组测序计划的推出,不断有很多物种被新测序完成,需要对这些物种的蛋白质功能进行注释.这些物种中已知功能的蛋白质数量少,可以考虑使用亲缘关系近、已知功能蛋白质数量多的物种来帮助这些物种进行蛋白质功能预测.本文把这个任务抽象为多示例多标记迁移学习问题,并提出了第一个多示例多标记迁移学习框架TR-MIML来解决此任务.TR-MIML通过最小化投影空间上加权源域样本中心点与目标域样本中心点的距离,给源域样本赋予不同权值,并基于目标域和源域样本训练多示例多标记学习模型.在两个新完成测序物种上,实验结果证明了迁移学习有助于它们的蛋白质功能预测.另外,亲缘关系越近的物种作为源域进行迁移学习帮助越大.

With the release of various genome sequencing projects, there are many species whose genomic sequences have been recently completed. It is essential to annotate the protein functions of these species. Owing to the lack of proteins with known functions, it is important to exploit their relative species with a large number of proteins whose functions are known to assist in predicting the protein functions of these species. In this paper,we treat this task as a multi-instance multilabel transfer learning problem and propose the first multi-instance multilabel transfer learning framework to perform this task. Experiments on two newly completed sequencing species demonstrate that transfer learning contributes to protein function prediction. Moreover, the closer the polygenetic relationship between the source domain species and target domain species, the better the performance of transfer learning.