{"id":"W4412163734","doi":"10.1158/1557-3265.aimachine-a031","title":"Abstract A031: Unsupervised graph-based visualization of variational autoencoder latent spaces reveals hidden multiple myeloma subtypes","year":2025,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoencoder; Visualization; Multiple myeloma; Artificial intelligence; Graph; Pattern recognition (psychology); Computer science; Computational biology; Medicine; Biology; Theoretical computer science; Artificial neural network; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007327317,0.0006479118,0.0003289889,0.001197369,0.0003511683,0.0009228768,0.000539822,0.0005739921,0.003539855],"category_scores_gemma":[0.002094958,0.0002347109,0.0008257916,0.0005628808,0.000380631,0.0007065015,0.0006901385,0.0009230817,0.0004637316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006634036,"about_ca_system_score_gemma":0.0006344776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007214653,"about_ca_topic_score_gemma":0.007186265,"domain_scores_codex":[0.9998406,0.00005219223,0.000007084567,0.00004890715,0.00002682629,0.00002445977],"domain_scores_gemma":[0.9993881,0.0003164127,0.00005597521,0.00007766113,0.0001077398,0.00005410866],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000976785,0.0002792779,0.03681487,0.0002391015,0.0003005018,0.0004706156,0.0009132453,0.6800885,0.05721474,0.03053283,0.02141309,0.1707565],"study_design_scores_gemma":[0.000009503869,0.00001952654,0.002522415,0.00001005524,0.000006057527,0.00002832289,0.00005133238,0.9869207,0.002464026,0.006991528,0.0009659341,0.00001066781],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4397379,0.0003367197,0.5471585,0.001085742,0.0001043311,0.00008512122,0.003685689,0.004948423,0.002857582],"genre_scores_gemma":[0.8967063,0.0001586331,0.0972596,0.0001041808,0.00003348759,0.00006764266,0.003212472,0.0004056318,0.002052075],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007214653,"threshold_uncertainty_score":0.01434535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1465657502516099,"score_gpt":0.4695591570653139,"score_spread":0.322993406813704,"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."}}