Fig. 2

The framework of PheSeq. a General model input in PheSeq involves p-values for association significance in sequence analysis and phenotypic embeddings for phenotype description from texts or graphs. The associations with p-values are graphically depicted in a Manhattan-style plot. A threshold line with a strict criterion (red line) or a less strict criterion (green line) is then applied. Concurrently, a DL perception module learns the association description of gene-disease association from text or graph. Genes exhibiting significant association descriptions tend to aggregate in the top-left region of the semantic space, as shown in the figure. Analogous patterns emerge in other scenarios. Finally, PheSeq learns the data distributions and performs data fusion for gene-disease associations. b/c Data fusion of association significance and phenotype description for a significant/non-significant gene-disease association by PheSeq. For each gene-disease association, two distinct types of observations, denote as L for phenotypic embedding and P for p-value, are considered for data fusion. Both sets of observations are input into the PGM inference module, facilitating the learning of dependency relationships among them in conjunction with latent variables. The phenotypic embedding L is initially processed through the DL perception module for semantic training, resulting in the generation of high-quality embeddings denoted as Z. The latent variable T serves a pivotal role in synchronizing the phenotypic embedding data with the p-value data, the latter adhering to a beta distribution indicative of a predisposition toward “small-p-value.” In addition, another latent variable F functions as an association score, establishing connnections among model parameters. Conceptually, the switch mechanism activates when both the association significance and phenotype description align, effectively bridging the above heterogeneous data modalities. Part c shows the converse situation, wherein the data indicate non-significance for the gene-disease association. In this case, a uniform distribution is employed to characterize the distribution of the p-value. The remaining configurations of the model remain consistent