| Title | Machine learning integration identifying an eight-gene diagnostic signature for acute mountain sickness |
|---|---|
| Description | The expanding availability of modern transportation infrastructure has accelerated large-scale population migration to high-altitude regions, significantly increasing clinical demands for addressing acute mountain sickness (AMS). However, the diagnosis of AMS mainly depends on a self-questionnaire, revealing the need for reliable biomarkers for AMS. To address this issue, we constructed an advanced computational framework integrating longitudinal multi-omics profiling and ensemble machine learning models to delineate clinically actionable biomarkers with stable diagnostic trajectories. |
| Organism | Homo sapiens |
| Data Type | Condition-specific Biomarkers |
| Data Accessibility | Controlled-access |
| BioProject | PRJCA042779 |
| Release Date | 2025-07-09 |
| Submitter | Dongfeng Yin (ydf1112@163.com) |
| Organization | Shihezi University |
| Submission Date | 2025-07-08 |
HTTP download speed may be slow. It is highly recommended that you download the dataset using a dedicated FTP tool (such as FileZilla Client).
| File ID | File Title | Number/Samples | File Type | File Size | File Suffix | Download |
|---|---|---|---|---|---|---|
| OMIX010881-01 | Supplementary data | 5 | Condition-specific Biomarkers | 95.0 KB | zip | Controlled |
| Paper Title | Journal Name | Publish Time | Accession | Citing Type |
|---|---|---|---|---|
| Machine learning integration identifying an eight-gene diagnostic signature for acute mountain sickness | Frontiers in Medicine | 2025-11 | OMIX010881 | Deposit |