アブストラクト
| Date | 14:00-14:50 Jan 30, 2024 |
| Speaker | Hongmin Cai School of Future Technology, South China University of Technology, China |
| Title | Unsupervised clustering methods and applications for genes, cells and tissues |
| Abstract |
The emergence of complex diseases is a process of evolution from micro
to macro. Unsupervised clustering methods can analyze the evolution
patterns of complex diseases from three scales: genes, cells and
tissues. However, the traditional unsupervised clustering methods face
the challenges of single-omics sequencing data cannot be separated,
multi-omics network data are misaligned, and multi-modal imaging data
cannot be registered. To this end, we have developed a series of
single-omics high-dimensional clustering methods, multi-omics network
clustering methods, and multi-modal collaborative clustering methods. At
the gene level, single-omics sequences are separated to identify
differential genes; at the cellular level, multi-omics networks are
aligned to quantify spatial regulation and mine early screening targets;
at the tissue level, multi-modal images are registered to identify
common lesions. Provide in-depth and accurate whole-field analysis for
tumor diseases.
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