Title: motifNet: Functional motif interactions discovered in mRNA sequences with implicit neural representation learning. Abstract: Predicting the functional sequence patterns in mRNA, or motifs, is an important way to uncover the mechanisms of cell life cycle in clinical research and drug discovery. Despite emerging studies trying to build neural networks for mRNA event prediction with high accuracy, most of these models were black boxes where the relationship between internal network components and the mRNA events could not be explained. Hence, we propose motifNet with a generalized framework for position specific patterns extraction and visualization. MotifNet is a lightweight neural network using position coordinates to represent various motif interaction patterns in human mRNA sequences. By navigating sequence and positional inputs in the encoded latent space, we can interactively generate motif patterns or the positional effect score in mRNA activities constrained on defined input. Furthermore, we also found out that violation of some motif patterns from motifNet in real human mRNA variants will significantly cause disease related cell dysfunction.