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

Abstract

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.