FINER: Enhancing the Prediction of Tissue-specific Functions of Isoforms by Refining Isoform Interaction Networks

Annotating the functions of gene products is a mainstay in biology. A variety of databases have been established to record functional knowledge at the gene level. However, functional annotations at the isoform resolution are in great demand in many biological applications. Although critical information in biological processes such as protein-protein interactions (PPIs) is often used to study gene functions, it does not directly help differentiate the functions of isoforms, as the 'proteins' in the existing PPIs generally refer to 'genes'. On the other hand, the prediction of isoform functions and prediction of isoform-isoform interactions, though inherently intertwined, have so far been treated as independent computational problems in the literature. Here, we present FINER, a unified framework to jointly predict isoform functions and refine PPIs from the gene level to the isoform level, enabling both tasks to benefit from each other. Extensive computational experiments on human tissue-specific data demonstrate that FINER is able to gain at least 5.16% in AUC and 15.1% in AUPRC for functional prediction across multiple tissues by refining noisy PPIs, resulting in significant improvement over the state-of-the-art methods. Some in-depth analyses reveal consistency between FINER's predictions and the tissue specificity as well as subcellular localization of isoforms.