{"id":"W3126773939","doi":"10.1038/s41467-021-21194-4","title":"RNA secondary structure prediction using deep learning with thermodynamic integration","year":2021,"lang":"en","type":"article","venue":"Nature Communications","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":496,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; Japan Society for the Promotion of Science; Ministry of Education, Culture, Sports, Science and Technology; Research Organization of Information and Systems","keywords":"Overfitting; Nucleic acid secondary structure; Computer science; Parameterized complexity; RNA; Artificial intelligence; Protein secondary structure; Robustness (evolution); Regularization (linguistics); Non-coding RNA; Machine learning; Deep learning; Folding (DSP implementation); Nucleic acid structure; Artificial neural network; Computational biology; Algorithm; Biology; Gene; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008972905914192607,"score_gpt":0.2518728836326812,"score_spread":0.2428999777184886,"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."}}