{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005145004,0.0001005809,0.000169064,0.00009440853,0.0001283476,0.00002670528,0.0004242448,0.00005856855,0.00004313497],"category_scores_gemma":[0.00003396473,0.00008756128,0.00008041857,0.0007410642,0.002710042,0.000003850208,0.0002839399,0.00005597061,0.000003118044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001447409,"about_ca_system_score_gemma":0.00009982829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002232116,"about_ca_topic_score_gemma":9.995413e-7,"domain_scores_codex":[0.9989172,0.000103208,0.0001944698,0.0003694176,0.0001854717,0.0002302134],"domain_scores_gemma":[0.9993508,0.00001843164,0.00005264785,0.0003385557,0.0001706552,0.0000688436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001091254,0.00008349433,0.002854805,0.0000190634,0.00006685196,5.718353e-7,0.00003007075,0.0009891485,0.4938728,0.498321,0.0003674988,0.003285569],"study_design_scores_gemma":[0.0003002487,0.0001741364,0.004293018,0.00001384191,0.0000279866,0.000004556275,0.000003986242,0.2653159,0.656283,0.07295164,0.0004819511,0.0001497687],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5565009,0.00002959265,0.4426895,0.00009796826,0.00007915123,0.00005731495,0.000002003355,0.00001045439,0.0005331176],"genre_scores_gemma":[0.9454166,0.000003914878,0.05434267,0.0001461944,0.00005092443,0.000002685076,0.00001050208,0.00000364272,0.00002290682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4253694,"threshold_uncertainty_score":0.9985263,"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."}}