{"id":"W4284702964","doi":"10.36227/techrxiv.20202155","title":"Empirical Assessment of Graph Embedding Techniques for Predicting Missing Links in Biological Networks","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Heritage College","funders":"Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Embedding; Computer science; Graph; Homogeneous; Biological network; Representation (politics); Theoretical computer science; Similarity (geometry); Artificial intelligence; Encoder; Network analysis; Machine learning; Data mining; Mathematics; Engineering","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":[0.006611997,0.001552759,0.00074516,0.003784175,0.0004811495,0.0009471055,0.001259671,0.001554286,0.001065514],"category_scores_gemma":[0.02681101,0.0002933275,0.0006707653,0.00161367,0.0009514748,0.002498374,0.001136023,0.001685212,0.0004301979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007756401,"about_ca_system_score_gemma":0.0004841475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003800491,"about_ca_topic_score_gemma":0.00413158,"domain_scores_codex":[0.9976887,0.001216874,0.000158692,0.0004333736,0.0003855431,0.000116902],"domain_scores_gemma":[0.9589539,0.03403135,0.001977133,0.002739406,0.001771666,0.0005265957],"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":[0.0006316162,0.0005202154,0.04772936,0.0004302113,0.0004482376,0.0002072796,0.0001441536,0.7238371,0.002672064,0.003397352,0.005799129,0.2141833],"study_design_scores_gemma":[0.00001197039,0.000114613,0.002744971,0.00002045324,0.00003194192,0.00004968051,0.00002900313,0.9932499,0.001540825,0.001973006,0.0002218611,0.00001180901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7878463,0.007104933,0.1952073,0.001386763,0.0002096428,0.0001210784,0.001678818,0.002459595,0.003985652],"genre_scores_gemma":[0.9562395,0.000774498,0.03930014,0.00008962004,0.00008243203,0.00004574821,0.002564799,0.0001181305,0.0007851922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006611997,"threshold_uncertainty_score":0.03496802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03355121710250808,"score_gpt":0.3556241293900483,"score_spread":0.3220729122875402,"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."}}