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Here, we propose a novel strategy to integrate omics datasets of single-cell ribonucleic acid sequencing and single-cell Assay for Transposase-Accessible Chromatin using sequencing, and using an unsupervised learning neural network to divide the samples with high copy number variation scores, which are used to infer the GRN in each gene block. Accuracy validation of proposed strategy shows that approximately 80% of transcription factors are directly associated with cancer, colorectal cancer, malignancy and disease by TRRUST; and most transcription factors are prone to produce multiple transcript variants and lead to tumorigenesis by RegNetwork database, respectively. The source code access are available at: https:\/\/github.com\/Cuily-v\/Colorectal_cancer.<\/jats:p>","DOI":"10.1093\/bib\/bbad011","type":"journal-article","created":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T00:18:01Z","timestamp":1675297081000},"source":"Crossref","is-referenced-by-count":8,"title":["Unsupervised construction of gene regulatory network based on single-cell multi-omics data of colorectal cancer"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4221-8430","authenticated-orcid":false,"given":"Lingyu","family":"Cui","sequence":"first","affiliation":[{"name":"College of Life Science, Northeast Forestry University , Harbin, 150040 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4104-3966","authenticated-orcid":false,"given":"Hongfei","family":"Li","sequence":"additional","affiliation":[{"name":"College of Life Science, Northeast Forestry University , Harbin, 150040 , 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