{"id":"W4206977250","doi":"10.1016/j.bioactmat.2022.01.023","title":"Proteomics as a tool to gain next level insights into photo-crosslinkable biopolymer modifications","year":2022,"lang":"en","type":"article","venue":"Bioactive Materials","topic":"Collagen: Extraction and Characterization","field":"Materials Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Bijzonder Onderzoeksfonds UGent; Centre National de la Recherche Scientifique; Interreg; Vlaamse regering; Universiteit Gent; Université de Lille; Fonds Wetenschappelijk Onderzoek; Infrastructures en Biologie Santé et Agronomie","keywords":"Biopolymer; Biomaterial; Materials science; Proteomics; Tissue engineering; Peptide; Nanotechnology; Protein engineering; Combinatorial chemistry; Chemistry; Polymer; Biomedical engineering; 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":[0.0004529153,0.0004866299,0.0003093855,0.0005782138,0.0002121734,0.000494599,0.0002350818,0.0005352234,0.0009590815],"category_scores_gemma":[0.0002782879,0.0002165994,0.0002263301,0.0003127094,0.0003524336,0.0006770971,0.0002995849,0.0008701787,0.000614426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003231844,"about_ca_system_score_gemma":0.0002828058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002807437,"about_ca_topic_score_gemma":0.0004950218,"domain_scores_codex":[0.999824,0.00003335978,0.00001002806,0.00004828123,0.00006260665,0.0000218384],"domain_scores_gemma":[0.9998407,0.00005120457,0.00004017568,0.00001991501,0.00002954745,0.00001843025],"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":[0.000009827099,0.000006560189,0.000058296,0.00004651966,0.000002494726,0.00001898473,0.00000800346,0.00003566354,0.9979814,0.0002156392,0.0000187704,0.001597894],"study_design_scores_gemma":[0.000002365363,0.00006820201,0.001171411,0.000008439887,0.0000103249,0.000208167,0.00002959808,0.0009423715,0.9944264,0.0003559123,0.002770616,0.000006223063],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5574206,0.02011711,0.4111138,0.001231045,0.0002807993,0.0001881904,0.001525956,0.001108382,0.007014163],"genre_scores_gemma":[0.7103507,0.01327312,0.2680701,0.0008541764,0.0001255272,0.0001868318,0.00106256,0.0001220019,0.005955011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009590815,"threshold_uncertainty_score":0.003208399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04491422003861727,"score_gpt":0.2846448537029811,"score_spread":0.2397306336643639,"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."}}