{"doi":"10.1016/j.neunet.2022.04.025","title":"Context-aware dynamic neural computational models for accurate Poly(A) signal prediction","abstract":null,"journal":"Neural Networks","year":2022,"id":622255,"datarank":0.3596842909197557,"base_score":2.3978952727983707,"endowment":2.3978952727983707,"self_citation_contribution":0.3596842909197557,"citation_network_contribution":0.0,"self_endowment_contribution":0.3596842909197557,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":10,"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":190457,"name":"Chaoyang Li","orcid":"0000-0002-8498-737X","position":1,"is_corresponding":false},{"id":937943,"name":"Dongming Zhou","orcid":"0000-0002-5989-7591","position":2,"is_corresponding":false},{"id":1523756,"name":"Jinde Cao","orcid":"0000-0003-3133-7119","position":3,"is_corresponding":false},{"id":182718,"name":"Hui Liang","orcid":null,"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 dynamic neural computational models for accurate Poly(A) signal prediction","abstract":"Accurately predicting Polyadenylation (Poly(A)) signals isthe key to understand the mechanism of translation regulation and mRNA metabolism. However, existing computational algorithms fail to work well for predicting Poly(A) signals due to the vanishing gradient problem when simply increasing the number of layers. In this work, we devise a spatiotemporal context-aware neural model called ACNet for Poly(A) signal prediction based on co-occurrence embedding. Specifically, genomic sequences of Poly(A) signals are first split into k-mer sequences, and k-mer embeddings are pre-trained based on the co-occurrence matrix information; Then, gated residual networks are devised to fully extract spatial information, which has an excellent ability to control the information flow and ease the problem of vanishing gradients. The gated mechanism generates channel weights by a dilated convolution and aggregates local features by identity connections which are obtained by multi-scale dilated convolutions. Experimental results indicate that our ACNet model outperforms the state-of-the-art prediction methods on various Poly(A) signal data, and an ablation study shows the effectiveness of the design strategy.","is_dataset_classified":null,"base_score":2.3978952727983707,"endowment":2.3978952727983707,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"35588673","pmcid":null,"openalex_id":"https://openalex.org/W4225125743","authors":[],"funders":[{"funder_name":"National Natural Science Foundation of China","grant_id":"62066047","title":null},{"funder_name":"Yunnan Province Department of Education","grant_id":"2021Y023","title":null},{"funder_name":"Yunnan Province Department of Education","grant_id":"","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"","title":null}],"total_grants":4,"fwci":0.627,"citation_percentile":0.6246383,"influential_citations":0,"citation_trend":[{"year":2022,"count":2},{"year":2023,"count":4},{"year":2024,"count":2},{"year":2026,"count":2}],"oa_status":"closed","license":"https://doi.org/10.15223/policy-004","oa_locations":[{"url":"https://api.elsevier.com/content/article/PII:S0893608022001575?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S0893608022001575?httpAccept=text/plain","host_type":"publisher"},{"url":"https://doi.org/10.1016/j.neunet.2022.04.025","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/35588673","host_type":"repository"}],"fields_of_study":["RNA and protein synthesis mechanisms","RNA Research and Splicing","Genomics and Phylogenetic Studies","Algorithms","Computational Biology","Computer Simulation","Genomics","Poly A"],"mesh_terms":["Algorithms","Computer Simulation","Poly A","Computational Biology","Genomics"],"keywords":["Computer science","Context (archaeology)","Convolution (computer science)","SIGNAL (programming language)","Algorithm","Polyadenylation","Artificial intelligence","Artificial neural network","Pattern recognition (psychology)","Convolutional neural network","Embedding","Deep Neural Networks","Poly(a) Signals","Attention Mechanism","Co-occurrence Embedding"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-03T18:52:41.466530Z","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":[]}