Multi-omics profiling and AI-driven clinically deployable risk models in MGUS and smoldering myeloma
Yanyun Wu, Dongliang Zhang, Jingyao Jiang, Linghui Zheng, Zhiming Zhou, Zhenxing Zhang, Sina Nouri · 2025
Read Multi-omics profiling and AI-driven clinically deployable risk models in MGUS and smoldering myeloma by Yanyun Wu, Dongliang Zhang, Jingyao Jiang, Linghui Zheng, Zhiming Zhou, Zhenxing Zhang, Sina Nouri (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12769579.txt.
Description
Yanyun Wu and colleagues review how multi-omics profiling and AI could sharpen risk prediction in the early states of multiple myeloma. MGUS and smoldering myeloma precede myeloma on one spectrum, but progression is hard to predict. Current risk models rest on clinical, laboratory, and cytogenetic markers and miss the molecular complexity driving progression, capping accuracy.
Genomics, transcriptomics, proteomics, and metabolomics now expose molecular drivers in depth. Machine learning can merge these layers into live risk prediction for each person, finding new biomarkers and forecasting outcomes far better. The authors argue such tools could transform stratification, letting doctors watch each person and treat earlier. Uptake faces real obstacles: merging complex data types, one protocol set, data privacy, bias in the math, and shifting regulation. The clinical stake is timing: treat too early and patients endure toxicity for nothing; too late and a curable window closes.
Catalog record
| CREATOR | Yanyun Wu, Dongliang Zhang, Jingyao Jiang, Linghui Zheng, Zhiming Zhou, Zhenxing Zhang, Sina Nouri |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 76 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |