Artificial intelligence driven exposome and multi omics integration for biomarker discovery in liver cancer: a literature review
Xuelian Wang, Yunjing Gao, Ran Xu, Xin Lan, Qiushi Huang · 2026
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Description
Xuelian Wang and colleagues review how smart computer models merge life exposure and gene-scale data to find liver cancer marks in a 2026 paper. Liver cancer kills many people worldwide, and the cell type called HCC leads the count. Known drivers span virus liver illness, drink-linked liver harm, and fat-linked liver disease, each shaped by life exposures.
The review weighs mark hunts that join exposure records with gene, protein, and fuel data, plus maps of where cells sit in tissue. Clear models that show their logic get praise for early catch, outlook sorting, and care choice. Gaps get blunt talk: exposure records stay thin, data tools vary, most work looks back, and few models face outside tests. New layers such as single-cell reads may sharpen marks, the authors say, if checks grow stricter.
Catalog record
| CREATOR | Xuelian Wang, Yunjing Gao, Ran Xu, Xin Lan, Qiushi Huang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 47532 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |