{"id":"W3122558558","doi":"10.1101/2021.01.14.425874","title":"Integrating protein networks and machine learning for disease stratification in the Hereditary Spastic Paraplegias","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Hereditary Neurological Disorders","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University College London Hospitals NHS Foundation Trust; University College London; Engineering and Physical Sciences Research Council; UK Dementia Research Institute; National Institute for Health and Care Research; Weston Brain Institute; Alzheimer's Society; Wellcome Trust; Medical Research Council; Alzheimer's Association; Biotechnology and Biological Sciences Research Council; Michael J. Fox Foundation for Parkinson's Research","keywords":"Hereditary spastic paraplegia; Phenotype; Spasticity; Disease; Identification (biology); Medicine; Computational biology; Biology; Bioinformatics; Gene; Neuroscience; Genetics; Physical medicine and rehabilitation; Pathology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001384359,0.0005015538,0.0003759893,0.002951952,0.000230998,0.0008546892,0.0002252801,0.0003603412,0.0006106537],"category_scores_gemma":[0.002369365,0.0001360428,0.0004354732,0.0009782414,0.0003487359,0.0005719989,0.0004728962,0.0004054901,0.0001649031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005188252,"about_ca_system_score_gemma":0.0003548126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00151041,"about_ca_topic_score_gemma":0.001587864,"domain_scores_codex":[0.9996213,0.0002228633,0.00002001493,0.00006111843,0.0000454744,0.00002914028],"domain_scores_gemma":[0.9990286,0.0006308468,0.0001367154,0.00005766481,0.00008781956,0.0000584789],"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":[0.001133681,0.0005428693,0.1824729,0.0003198912,0.0007061867,0.0006302068,0.0002301327,0.5278254,0.03340461,0.01341046,0.001964192,0.2373595],"study_design_scores_gemma":[0.00001357759,0.00007159497,0.02121634,0.00002454104,0.00005112998,0.0001070578,0.00004590191,0.9605745,0.002316036,0.01516308,0.000404441,0.0000117604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7666261,0.00178634,0.2260547,0.001043946,0.00004635835,0.0001134035,0.001058438,0.000681913,0.002588813],"genre_scores_gemma":[0.9648263,0.0002532807,0.03409186,0.00003099029,0.00002272942,0.00003084058,0.0004615817,0.00001296451,0.0002695133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002951952,"threshold_uncertainty_score":0.007321298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03121915330213449,"score_gpt":0.2343823238799001,"score_spread":0.2031631705777656,"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."}}