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  "Description": "Functional enrichment analysis methods such as gene set\nenrichment analysis (GSEA) have been widely used for analyzing\ngene expression data. GSEA is a powerful method to infer\nresults of gene expression data at a level of gene sets by\ncalculating enrichment scores for predefined sets of genes.\nGSEA depends on the availability and accuracy of gene sets.\nThere are overlaps between terms of gene sets or categories\nbecause multiple terms may exist for a single biological\nprocess, and it can thus lead to redundancy within enriched\nterms. In other words, the sets of related terms are\noverlapping. Using deep learning, this pakage is aimed to\npredict enrichment scores for unique tokens or words from text\nin names of gene sets to resolve this overlapping set issue.\nFurthermore, we can coin a new term by combining tokens and\nfind its enrichment score by predicting such a combined tokens.",
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