{"id":"W4312098799","doi":"10.1101/2022.12.19.520999","title":"Hybrid-DIA: Intelligent Data Acquisition for Simultaneous Targeted and Discovery Phosphoproteomics in Single Spheroids","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thermo Fisher Scientific (Canada)","funders":"Novo Nordisk; Novo Nordisk Fonden; Academia Sinica; European Commission","keywords":"Phosphoproteomics; Phosphopeptide; Proteomics; Computational biology; Quantitative proteomics; Chemistry; HeLa; Computer science; Peptide; Phosphorylation; Biology; Protein phosphorylation; Cell; Biochemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006717073,0.0006655714,0.0005061296,0.0005748415,0.0003026284,0.0008749615,0.0008659806,0.0005073567,0.002898643],"category_scores_gemma":[0.0005165691,0.0004895509,0.0003301757,0.00040611,0.000324,0.0008812009,0.000963276,0.0009052287,0.001240508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005104162,"about_ca_system_score_gemma":0.0004070247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003967906,"about_ca_topic_score_gemma":0.0006255044,"domain_scores_codex":[0.9996264,0.00003565739,0.00003092684,0.0001503166,0.0001230832,0.00003348475],"domain_scores_gemma":[0.9996449,0.0001016035,0.00004351297,0.0001001024,0.0000618958,0.00004805688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002153504,0.00003864338,0.0006803042,0.00006953541,0.00002839313,0.00006250305,0.00003616195,0.0006055043,0.9841349,0.0004286623,0.0009984511,0.01270153],"study_design_scores_gemma":[0.00002142056,0.00004968665,0.001784913,0.000003931192,0.000009301648,0.0001639393,0.0000145857,0.03082292,0.9618985,0.0003781582,0.004823191,0.00002947623],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3583828,0.0005470424,0.6101871,0.0003394564,0.0001629355,0.0003220147,0.005399597,0.02168184,0.002977228],"genre_scores_gemma":[0.4623015,0.000365253,0.5267469,0.0003017585,0.0000362254,0.000846619,0.003307587,0.00174308,0.004351144],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002898643,"threshold_uncertainty_score":0.009696901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01871516913757694,"score_gpt":0.2511818453015156,"score_spread":0.2324666761639387,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}