MOTIVATION: High-resolution annotation of gene functions is a central task in functional genomics. Multiple proteoforms translated from alternatively spliced isoforms from a single gene are actual function performers and greatly increase the functional diversity. The specific functions of different isoforms can decipher the molecular basis of various complex diseases at a finer granularity. Multi-instance learning (MIL)-based solutions have been developed to distribute gene(bag)-level Gene Ontology (GO) annotations to isoforms(instances), but they simply presume that a particular annotation of the gene is responsible by only one isoform, neglect the hierarchical structures and semantics of massive GO terms (labels), or can only handle dozens of terms. RESULTS: We propose an efficacy approach IsofunGO to differentiate massive functions of isoforms by GO embedding. Particularly, IsofunGO first introduces an attributed hierarchical network to model massive GO terms, and a GO network embedding strategy to learn compact representations of GO terms and project GO annotations of genes into compressed ones, this strategy not only explores and preserves hierarchy between GO terms but also greatly reduces the prediction load. Next, it develops an attention-based MIL network to fuse genomics and transcriptomics data of isoforms and predict isoform functions by referring to compressed annotations. Extensive experiments on benchmark datasets demonstrate the efficacy of IsofunGO. Both the GO embedding and attention mechanism can boost the performance and interpretability. AVAILABILITYAND IMPLEMENTATION: The code of IsofunGO is available at http://www.sdu-idea.cn/codes.php?name=IsofunGO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-phenome association (GPA) play a crucial part in deciphering the complex pathology of phenotypes (i.e., traits and diseases). Heterogeneous network-based GPA solutions can model the complex connections between multi-types of molecules by viewing them as nodes, and give better results than using single/two-omics data alone, but they overwhelmingly need to project other molecules toward homogeneous gene/phenotype nodes for data fusion and prediction, such projections result in information loss. Matrix factorization based data fusion can avoid such projection by integrating multi-type data in a coherent way, but they typically perform linear factorization and cannot mine the nonlinear relationships between molecules, which compromise the GPA analysis. Furthermore, most of them can not synergy network topology and node attribution information in a principle way. In this paper, we propose a deep matrix factorization based solution (DeepGPA) to predict GPAs by fusing heterogeneous molecular network and diverse attributes of nodes. DeepGPA performs deep matrix factorization on the block adjacency matrices of heterogeneous network in a cooperative manner to obtain the nonlinear representations of different moleclues. In addition, it performs low-rank representation learning on the attribute data with the shared nonlinear representations. In this way, both the network topology and node attributes are jointly mined to explore the representations of molecules and complex interplays between molecules and phenotypes. DeepGPA then uses the representational vectors of gene and phenotype nodes to predict GPAs. Experimental results on Maize datasets confirm t hat Deep DPA out performs competitive methods by a large margin under different evaluation protocols.