Deep neural network models for cell type prediction based on single-cell Hi-C data
SCANN enhances the training speed and requires fewer resources for predicting cell types. In addition, when the number of cells in different cell types was extremely unbalanced, SCANN has higher stability and flexibility in solving cell classification and cell type prediction using the single-cell Hi-C data. This predication method can assist biologists to study the differences in the chromosome structure of cells between different cell types.
Authors
- Meili Wang (ORCID: https://orcid.org/0000-0001-7901-1789)
- Hao Wu (ORCID: https://orcid.org/0000-0003-2340-9258)
- Bing Zhou (ORCID: https://orcid.org/0000-0002-5872-8358)
- Quanzhong Liu (ORCID: https://orcid.org/0000-0002-6217-6285)
Institutions
- Shandong University (CN)
- North West Agriculture and Forestry University (CN)
Publication Details
- Journal
- BMC Genomics
- Published
- 2024-09-16
- DOI
- https://doi.org/10.1186/s12864-024-10764-7
- Citations
- 3
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.99
Funders
- Shandong University
- National Natural Science Foundation of China
- Natural Science Foundation of Shaanxi Province
- National Key Research and Development Program of China
- Basic and Applied Basic Research Foundation of Guangdong Province