The invention discloses a self-adaptive multi-channel fusion lncRNA
subcellular localization prediction method based on a
reinforcement learning agent, and relates to a long-chain non-coding
RNA subcellular localization prediction method. The method aims at solving the problems that sample heterogeneity is ignored and
RNA-
protein interaction characteristics are ignored due to the fact that a fixed
feature fusion strategy is adopted in an existing method. According to the method, firstly, multi-
modal feature extraction is carried out, original feature vectors are obtained through splicing, the original feature vectors are projected to a
reinforcement learning state, actions are obtained through an
intelligent agent obtained through PPO training based on near-end strategy optimization, and the actions comprise
feature selection mask codes, fusion weight guidance and dynamic hyper-parameter configuration. A personalized fusion strategy is obtained through action vectors generated by the
reinforcement learning agent, the fusion prediction network fuses the features in the multi-
modal feature set based on the personalized fusion strategy, and finally the positioning probability of the lncRNA at each subcell position is obtained.