{"id":"W3008506689","doi":"10.1101/2020.02.20.958512","title":"Bound2Learn: A Machine Learning Approach for Classification of DNA-Bound Proteins from Single-Molecule Tracking Experiments","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bacterial Genetics and Biotechnology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Cancer Institute; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Tracking (education); DNA; Biological system; Saccharomyces cerevisiae; In silico; Stability (learning theory); Genome; Computational biology; Visualization; Computer science; Kinetics; Data mining; Chemistry; Biology; Machine learning; Physics; Gene; Biochemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003412133,0.00173608,0.001454486,0.002863698,0.0006703563,0.001597121,0.002687652,0.002609069,0.002424169],"category_scores_gemma":[0.008349127,0.0006432088,0.001372131,0.00147936,0.0007647918,0.001352623,0.001658433,0.002548036,0.001885658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000957439,"about_ca_system_score_gemma":0.001030469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002644441,"about_ca_topic_score_gemma":0.003540691,"domain_scores_codex":[0.9987153,0.0003717286,0.00009491023,0.0004477965,0.0002790245,0.00009130484],"domain_scores_gemma":[0.9958242,0.0023474,0.0003996801,0.0006662863,0.0005726726,0.0001897125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007991507,0.0006194286,0.0136475,0.000654697,0.0005340244,0.000460261,0.0001834117,0.3057106,0.04802331,0.005664123,0.01568217,0.6080214],"study_design_scores_gemma":[0.00001316277,0.00002674832,0.0005178494,0.00001168077,0.00001042152,0.00003569634,0.000009863055,0.9891076,0.00465248,0.004606906,0.0009903879,0.00001712665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01190478,0.0002419998,0.9773912,0.0001012955,0.00004843552,0.00007438017,0.0009942669,0.008952872,0.0002907875],"genre_scores_gemma":[0.1645483,0.0001835505,0.8277256,0.0002221649,0.00008871259,0.0003716866,0.004504434,0.0007910804,0.001564488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003412133,"threshold_uncertainty_score":0.01804525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03107071809860804,"score_gpt":0.2403056106839019,"score_spread":0.2092348925852938,"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."}}