Dataset Information
Comprehensive proteomics analysis to distinguish follicular adenoma and follicular thyroid carcinoma from indeterminate thyroid nodules.Comprehensive proteomics analysis to distinguish follicular adenoma and follicular thyroid carcinoma from indeterminate thyroid nodules
Ontology highlight
ABSTRACT:
INSTRUMENT(S):
ORGANISM(S): Homo Sapiens (human)
SUBMITTER: 
Hee-Sung Ahn
LAB HEAD: Kyunggon Kim
PROVIDER: PXD043154 | Pride | 2025-10-13
REPOSITORIES: Pride
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| 20190728_Thyroid_FFPE_FA_07.mgf | Mgf | |||
| 20190728_Thyroid_FFPE_FA_07.mzML | Mzml | |||
| 20190728_Thyroid_FFPE_FA_07.pepXML | Pepxml | |||
| 20190728_Thyroid_FFPE_FA_08.mgf | Mgf | |||
| 20190728_Thyroid_FFPE_FA_08.mzML | Mzml |
Items per page: 1 - 5 of 610 |
Publications
Comprehensive Proteomics and Machine Learning Analysis to Distinguish Follicular Adenoma and Follicular Thyroid Carcinoma from Indeterminate Thyroid Nodules.
Endocrinology and metabolism (Seoul, Korea) 20250410 4
<h4>Backgruound</h4>The preoperative diagnosis of follicular thyroid carcinoma (FTC) is challenging because it cannot be readily distinguished from follicular adenoma (FA) or benign follicular nodular disease (FND) using the sonographic and cytological features typically employed in clinical practice.<h4>Methods</h4>We employed comprehensive proteomics and machine learning (ML) models to identify novel diagnostic biomarkers capable of classifying three subtypes: FTC, FA, and FND. Bottom-up prote ...[more]
