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To address this limitation, we introduced CellGO. CellGO tackles this challenge by leveraging the visible neural network (VNN) and single-cell gene expressions to mimic cell-type-specific signaling propagation along the Gene Ontology tree within a cell. This design enables a novel scoring system to calculate the cell-type-specific gene-pathway paired active scores, based on which, CellGO is able to identify cell-type-specific active pathways associated with single genes. In addition, by aggregating the activities of single genes, CellGO extends its capability to identify cell-type-specific active pathways for a given gene set. To enhance biological interpretation, CellGO offers additional features, including the identification of significantly active cell types and driver genes and community analysis of pathways. To validate its performance, CellGO was assessed using a gene set comprising mixed cell-type markers, confirming its ability to discern active pathways across distinct cell types. Subsequent benchmarking analyses demonstrated CellGO\u2019s superiority in effectively identifying cell types and their corresponding cell-type-specific pathways affected by gene knockouts, using either single genes or sets of genes differentially expressed between knockout and control samples. Moreover, CellGO demonstrated its ability to infer cell-type-specific pathogenesis for disease risk genes. Accessible as a Python package, CellGO also provides a user-friendly web interface, making it a versatile and accessible tool for researchers in the field.<\/jats:p>","DOI":"10.1093\/bib\/bbad417","type":"journal-article","created":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T12:40:31Z","timestamp":1699447231000},"source":"Crossref","is-referenced-by-count":6,"title":["CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7411-3676","authenticated-orcid":false,"given":"Peilong","family":"Li","sequence":"first","affiliation":[{"name":"Huashan Hospital, Fudan University State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, , Shanghai 200032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junfeng","family":"Wei","sequence":"additional","affiliation":[{"name":"Huashan Hospital, Fudan University State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, , Shanghai 200032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Zhu","sequence":"additional","affiliation":[{"name":"Huashan Hospital, Fudan University State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, , Shanghai 200032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,11,22]]},"reference":[{"key":"2024011119352885500_ref1","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41576-018-0005-2","article-title":"High-throughput mouse phenomics for 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