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To address this, we present ProteoAutoNet, a robotic experimental platform integrated with a computational workflow for high-throughput CF-MS analysis. This workflow increases the throughput of sample processing from protein complex to peptide by about two times. The integrated machine learning model incorporates targeted data augmentation to expand and diversify reliable protein interaction\u00a0data, thereby improving model\u00a0robustness. When applied to three thyroid cell lines, the model predicted 25,173 co-eluted proteins with an AUROC of 0.78. This analysis revealed significantly upregulated proteasome and prefoldin complexes in the\u00a0lung metastatic follicular thyroid carcinoma cell line FTC238 compared with the normal thyroid cell line Nthy-ori 3-1. Notably, we identified a protein interaction between TGM2 and HK1 that was significantly upregulated in the papillary thyroid carcinoma cell line TPC-1. 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