{"id":"W4411274961","doi":"10.1016/j.tcs.2025.115414","title":"Parameterized complexity of weighted target set selection","year":2025,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Exploratory Research for Advanced Technology; Japan Society for the Promotion of Science","keywords":"Parameterized complexity; Set (abstract data type); Selection (genetic algorithm); Mathematics; Computer science; Computational complexity theory; Algorithm; Theoretical computer science; Combinatorics; Artificial intelligence; Programming language","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":[0.00178656,0.001466738,0.00194948,0.0007403758,0.0009944869,0.003119528,0.003070586,0.001497991,0.006209952],"category_scores_gemma":[0.01095887,0.0007184983,0.001547348,0.002140313,0.001227271,0.005637801,0.002259212,0.001680166,0.0007262366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003065725,"about_ca_system_score_gemma":0.002631015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005146086,"about_ca_topic_score_gemma":0.004876417,"domain_scores_codex":[0.9974947,0.0006261685,0.0001422414,0.000857204,0.0003755963,0.0005040896],"domain_scores_gemma":[0.9897552,0.00721931,0.0008114323,0.001239282,0.0005084491,0.0004663251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0009649991,0.0003194479,0.004920219,0.0007350605,0.0002210709,0.0004422875,0.0003857071,0.8272146,0.0127044,0.04304799,0.01295389,0.09609041],"study_design_scores_gemma":[0.00009887904,0.00007066971,0.0006538343,0.00001880211,0.00005974015,0.0001736448,0.0001168878,0.9266188,0.002201629,0.06836618,0.001601715,0.00001926779],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4658038,0.001178021,0.507026,0.004476232,0.0001330551,0.0004478826,0.004102496,0.003055631,0.01377687],"genre_scores_gemma":[0.8363097,0.000655247,0.1528491,0.0004230746,0.00009919202,0.0004308425,0.004287192,0.0005693579,0.004376263],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006209952,"threshold_uncertainty_score":0.02224344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01084371881575583,"score_gpt":0.260680608681841,"score_spread":0.2498368898660852,"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."}}