{"doi":"10.1109/tnnls.2022.3226301","title":"Context-Aware Poly(A) Signal Prediction Model via Deep Spatial–Temporal Neural Networks","abstract":null,"journal":"IEEE Transactions on Neural Networks and Learning Systems","year":2024,"id":595119,"datarank":0.7077748306942643,"base_score":4.718498871295094,"endowment":4.718498871295094,"self_citation_contribution":0.7077748306942643,"citation_network_contribution":0.0,"self_endowment_contribution":0.7077748306942643,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":111,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":937943,"name":"Dongming Zhou","orcid":"0000-0002-5989-7591","position":1,"is_corresponding":false},{"id":1454884,"name":"Pu Li","orcid":"0000-0001-6481-9961","position":2,"is_corresponding":false},{"id":350291,"name":"Chaoyang Li","orcid":"0000-0001-7422-8110","position":3,"is_corresponding":false},{"id":1523756,"name":"Jinde Cao","orcid":"0000-0003-3133-7119","position":4,"is_corresponding":false},{"id":1523755,"name":"Yanbu Guo","orcid":"0000-0001-9532-2309","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Context-Aware Poly(A) Signal Prediction Model via Deep Spatial–Temporal Neural Networks","abstract":"Polyadenylation [Poly(A)] is an essential process during messenger RNA (mRNA) maturation in biological eukaryote systems. Identifying Poly(A) signals (PASs) from the genome level is the key to understanding the mechanism of translation regulation and mRNA metabolism. In this work, we propose a deep dual-dynamic context-aware Poly(A) signal prediction model, called multiscale convolution with self-attention networks (MCANet), to adaptively uncover the spatial-temporal contextual dependence information. Specifically, the model automatically learns and strengthens informative features from the temporalwise and the spatialwise dimension. The identity connectivity performs contextual feature maps of Poly(A) data by direct connections from previous layers to subsequent layers. Then, a fully parametric rectified linear unit (FP-RELU) with dual-dynamic coefficients is devised to make the training of the model easier and enhance the generalization ability. A cross-entropy loss (CL) function is designed to make the model focus on samples that are easy to misclassify. Experiments on different Poly(A) signals demonstrate the superior performance of the proposed MCANet, and an ablation study shows the effectiveness of the network design for the feature learning and prediction of Poly(A) signals.","is_dataset_classified":null,"base_score":4.718498871295094,"endowment":4.718498871295094,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"37015693","pmcid":null,"openalex_id":"https://openalex.org/W4312431055","authors":[],"funders":[{"funder_name":"National Natural Science Foundation of China","grant_id":"62066047","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"61802352","title":null},{"funder_name":"National Key Research and Development Project of China","grant_id":"2020YFA0714301","title":null},{"funder_name":"Project of Science and Technology in Henan Province","grant_id":"202102210178","title":null},{"funder_name":"Program for Young Key Teachers of Henan Province","grant_id":"2021GGJS095","title":null},{"funder_name":"Project of Collaborative Innovation in Zhengzhou","grant_id":"2021ZDPY0208","title":null},{"funder_name":"Doctor Scientific Research Fund of Zhengzhou University of Light Industry","grant_id":"2021BSJJ032","title":null},{"funder_name":"Doctor Scientific Research Fund of Zhengzhou University of Light Industry","grant_id":"2021BSJJ033","title":null}],"total_grants":8,"fwci":9.3174,"citation_percentile":0.98926158,"influential_citations":0,"citation_trend":[{"year":2023,"count":6},{"year":2024,"count":85},{"year":2025,"count":20}],"oa_status":"closed","license":"https://doi.org/10.15223/policy-029","oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx7/5962385/10547160/09982294.pdf?arnumber=9982294","host_type":"publisher"},{"url":"https://doi.org/10.1109/tnnls.2022.3226301","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/37015693","host_type":"repository"}],"fields_of_study":["RNA and protein synthesis mechanisms","RNA Research and Splicing","Molecular Biology Techniques and Applications","Neural Networks, Computer","Poly A","Humans","Deep Learning","RNA, Messenger","Algorithms","Polyadenylation"],"mesh_terms":["Deep Learning","Algorithms","Humans","Poly A","RNA, Messenger","Neural Networks, Computer","Polyadenylation"],"keywords":["Computer science","Polyadenylation","Artificial intelligence","Deep learning","Context (archaeology)","Pattern recognition (psychology)","Feature (linguistics)","Artificial neural network","Cross entropy","Parametric model","Parametric statistics","SIGNAL (programming language)","Machine learning","Messenger RNA","Mathematics","Biology"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-27T16:39:39.245108Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}