{"id":"W4402449873","doi":"10.1109/jbhi.2024.3458794","title":"GIAE-DTI: Predicting Drug-Target Interactions Based on Heterogeneous Network and GIN-Based Graph Autoencoder","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Autoencoder; Computer science; Graph; Graph theory; Artificial intelligence; Machine learning; Theoretical computer science; Artificial neural network; Mathematics; Combinatorics","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":[0.0007823119,0.001269244,0.001147935,0.001302806,0.0002797956,0.0006155648,0.00121974,0.001087973,0.001232684],"category_scores_gemma":[0.001908865,0.0004568038,0.0009301614,0.000929606,0.000421213,0.001168177,0.0008699864,0.001580343,0.0003929853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001070933,"about_ca_system_score_gemma":0.001135443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01217687,"about_ca_topic_score_gemma":0.0153165,"domain_scores_codex":[0.9996686,0.00006746507,0.00001998364,0.0001132367,0.00008277432,0.00004782015],"domain_scores_gemma":[0.9994662,0.0002414943,0.00009015011,0.00006090044,0.00009452779,0.00004671768],"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":[0.0001435373,0.0001819679,0.00641849,0.00009392809,0.0001791722,0.0001582008,0.00003772917,0.8966393,0.002950702,0.003423483,0.004119662,0.0856538],"study_design_scores_gemma":[0.000003452562,0.00001337432,0.0002483889,0.000002164132,0.000007117475,0.00001486203,0.000001948352,0.9982623,0.000341537,0.0008951582,0.0002067426,0.000003004061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1367857,0.002214775,0.849646,0.000881866,0.0001401218,0.0001829393,0.001800426,0.003674135,0.004674066],"genre_scores_gemma":[0.8256406,0.0009125607,0.1621815,0.0005377468,0.0000891012,0.0001896312,0.005116281,0.0001541918,0.005178528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01217687,"threshold_uncertainty_score":0.024212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02942510507412626,"score_gpt":0.3365335546010988,"score_spread":0.3071084495269726,"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."}}