{"id":"W4406920648","doi":"10.1093/nsr/nwae451","title":"A deep learning framework for <i>in silico</i> screening of anticancer drugs at the single-cell level","year":2024,"lang":"en","type":"article","venue":"National Science Review","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pancreas Centre (Canada)","funders":"Fundamental Research Funds for the Central Universities; Zhejiang University; National Natural Science Foundation of China","keywords":"Mathematics education; Artificial intelligence; Psychology; Computer science","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":[0.0008510111,0.0006775018,0.0007033015,0.0004886118,0.0002649685,0.0006981561,0.00126629,0.001075798,0.0017464],"category_scores_gemma":[0.001024478,0.0003824731,0.0009142735,0.0003537122,0.0005682562,0.0004524905,0.0007604184,0.001051158,0.0003432041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001326475,"about_ca_system_score_gemma":0.001531391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01199966,"about_ca_topic_score_gemma":0.01324374,"domain_scores_codex":[0.9998295,0.00005005585,0.000008919927,0.00004416082,0.00003825695,0.00002897961],"domain_scores_gemma":[0.999626,0.0002124071,0.00003649315,0.00002583999,0.00007047306,0.00002880312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003977773,0.00003938733,0.0006832615,0.00005319118,0.00005340354,0.00004704512,0.000009703967,0.9627309,0.003295525,0.004715782,0.000819148,0.02751282],"study_design_scores_gemma":[0.000001404702,0.000005923738,0.00002470188,0.000001308724,0.000002481199,0.000002071574,7.241168e-7,0.9985192,0.0003042874,0.001012546,0.0001241984,0.000001064914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02105837,0.0004578893,0.9740607,0.000409842,0.00003572003,0.00004341043,0.0002912981,0.001706179,0.001936607],"genre_scores_gemma":[0.6523126,0.0006227565,0.3399514,0.0006201097,0.00006446605,0.0003393724,0.0009498302,0.0001728511,0.004966677],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01199966,"threshold_uncertainty_score":0.02385962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04659753268894447,"score_gpt":0.3253444633624056,"score_spread":0.2787469306734611,"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."}}