{"id":"W2943517423","doi":"10.1039/c9lc00139e","title":"Profiling protein–protein interactions of single cancer cells with<i>in situ</i>lysis and co-immunoprecipitation","year":2019,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea","keywords":"Immunoprecipitation; In situ; Lysis; Profiling (computer programming); Chemistry; Cancer cell; Cell biology; Computational biology; Nanotechnology; Biophysics; Molecular biology; Cancer; Biology; Materials science; Biochemistry; Computer science; Gene; Genetics","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":[0.0003053547,0.0005577317,0.0005450997,0.0005723597,0.0003872385,0.0006239881,0.0004614147,0.0004500815,0.000922825],"category_scores_gemma":[0.0002916558,0.0003053952,0.0004166712,0.000631504,0.0003195248,0.0003269534,0.0004104878,0.0009037809,0.0006124761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003954515,"about_ca_system_score_gemma":0.0002157759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006085093,"about_ca_topic_score_gemma":0.00136402,"domain_scores_codex":[0.9995055,0.00004380212,0.00003820664,0.0002175789,0.0001225412,0.00007233025],"domain_scores_gemma":[0.9997976,0.00006168814,0.0000481615,0.00003868652,0.00003268867,0.00002115311],"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":[0.0000229181,0.00001665649,0.000235163,0.00003114817,0.00001045911,0.0000184203,0.00001438962,0.0000850386,0.9985331,0.00006005868,0.00004929214,0.0009231812],"study_design_scores_gemma":[0.000002773627,0.00003718749,0.003188364,0.000002051338,0.00001709639,0.00008978188,0.00001745514,0.002468753,0.9932947,0.00004978857,0.0008267048,0.000005445787],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8134881,0.001742799,0.1776296,0.0002466528,0.000104912,0.0002120578,0.002078922,0.0007514646,0.003745435],"genre_scores_gemma":[0.8997095,0.001695199,0.09143697,0.0002277918,0.0000451884,0.0004919186,0.002399502,0.000238057,0.003755887],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.000922825,"threshold_uncertainty_score":0.003087163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02239703356801941,"score_gpt":0.3120581414134806,"score_spread":0.2896611078454612,"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."}}