{"id":"W4414737811","doi":"10.1002/aisy.202501159","title":"Adversarial Erasing Enhanced Multiple Instance Learning (siMILe): Discriminative Identification of Oligomeric Protein Structures in Single Molecule Localization Microscopy","year":2025,"lang":"en","type":"preprint","venue":"Advanced Intelligent Systems","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Alliance de recherche numérique du Canada; Western Canada Research Grid","keywords":"Discriminative model; Identification (biology); Pattern recognition (psychology); Deep learning; Microscopy; Protein structure","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002776255,0.0003944329,0.0005806809,0.0003294527,0.00007413862,0.0000648882,0.0004083014,0.0003713578,0.000003106099],"category_scores_gemma":[0.0006018082,0.0004409184,0.0001822879,0.0003523997,0.0001233111,0.00001896398,0.0003567582,0.0003628151,0.000001041904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002432953,"about_ca_system_score_gemma":0.0001223732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00030189,"about_ca_topic_score_gemma":0.0001288441,"domain_scores_codex":[0.9970716,0.000328526,0.00115835,0.0009011226,0.0002597831,0.0002805755],"domain_scores_gemma":[0.9977039,0.00003229601,0.00111112,0.0006667896,0.0004441692,0.000041754],"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":[0.000144743,0.00009311923,0.000235831,0.0005438906,0.00007840394,0.000001161694,0.0003186507,0.1665138,0.8297232,0.0000398979,0.00001641547,0.002290866],"study_design_scores_gemma":[0.0002663773,0.0001127079,0.00004210573,0.0008939286,0.00004694019,5.471426e-7,0.0007612667,0.01025277,0.9863408,0.0001600563,0.0007680486,0.0003544523],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3242276,0.001968953,0.6718488,0.000003619098,0.0002110199,0.001343217,0.00002518602,0.00003709285,0.0003345476],"genre_scores_gemma":[0.9948232,0.0004924054,0.002707805,0.00001458109,0.00007644364,0.0002822173,0.0009422582,0.00004116267,0.0006198891],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6705957,"threshold_uncertainty_score":0.9998043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009518245259676188,"score_gpt":0.2922229686196174,"score_spread":0.2827047233599412,"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."}}