APDCA: An accurate and effective method for predicting associations between RBPs and AS-events during epithelial-mesenchymal transition
Motivation: Epithelial-mesenchymal transition (EMT) plays a key role in cancer metastasis by promoting changes in adhesion and motility. RNA-binding proteins (RBPs) regulate alternative splicing (AS) during EMT, enabling a single gene to produce multiple protein isoforms that affect tumor progressio...
Saved in:
| Published in: | PLoS computational biology Vol. 21; no. 11; p. e1013665 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
Public Library of Science (PLoS)
01.11.2025
|
| Subjects: | |
| ISSN: | 1553-7358, 1553-734X, 1553-7358 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Motivation: Epithelial-mesenchymal transition (EMT) plays a key role in cancer metastasis by promoting changes in adhesion and motility. RNA-binding proteins (RBPs) regulate alternative splicing (AS) during EMT, enabling a single gene to produce multiple protein isoforms that affect tumor progression. Disruption of RBP-AS interactions may disrupt the progress of diseases like cancer. Despite the importance of RBP-AS relationships in EMT, few computational methods predict these associations. Existing models struggle in sparse settings with limited known associations. To improve performance, we incorporate both sparsity constraints and heterogeneous biological data to infer RBP–AS associations.
Result: We propose a new method based on A ccelerated P roximal DC A lgorithm (APDCA) for predicting RBP–AS associations. In particular, APDCA combines sparse low-rank matrix factorization with a Difference-of-Convex (DC) optimization framework and uses extrapolation to improve convergence. A key feature of APDCA is the use of a sparsity constraint, which filters out noise and highlights key associations. In addition, integrating multiple related data sources with direct or indirect relationships can help in reaching a more comprehensive view of RBPs and AS events and to reduce the impact of false positives associated with individual data sources. we prove that our proposed algorithm is convergent under some conditions and the experimental results have illustrated that APDCA outperforms six baseline methods in both AUC and AUPR. A case study on the RBP QKI shows that the top predictions are verified by the OncoSplicing database. Thus, APDCA provides a fast, interpretable, and scalable tool for discovering post-transcriptional regulatory interactions. |
|---|---|
| AbstractList | MotivationEpithelial-mesenchymal transition (EMT) plays a key role in cancer metastasis by promoting changes in adhesion and motility. RNA-binding proteins (RBPs) regulate alternative splicing (AS) during EMT, enabling a single gene to produce multiple protein isoforms that affect tumor progression. Disruption of RBP-AS interactions may disrupt the progress of diseases like cancer. Despite the importance of RBP-AS relationships in EMT, few computational methods predict these associations. Existing models struggle in sparse settings with limited known associations. To improve performance, we incorporate both sparsity constraints and heterogeneous biological data to infer RBP-AS associations.ResultWe propose a new method based on Accelerated Proximal DC Algorithm (APDCA) for predicting RBP-AS associations. In particular, APDCA combines sparse low-rank matrix factorization with a Difference-of-Convex (DC) optimization framework and uses extrapolation to improve convergence. A key feature of APDCA is the use of a sparsity constraint, which filters out noise and highlights key associations. In addition, integrating multiple related data sources with direct or indirect relationships can help in reaching a more comprehensive view of RBPs and AS events and to reduce the impact of false positives associated with individual data sources. we prove that our proposed algorithm is convergent under some conditions and the experimental results have illustrated that APDCA outperforms six baseline methods in both AUC and AUPR. A case study on the RBP QKI shows that the top predictions are verified by the OncoSplicing database. Thus, APDCA provides a fast, interpretable, and scalable tool for discovering post-transcriptional regulatory interactions. Epithelial-mesenchymal transition (EMT) plays a key role in cancer metastasis by promoting changes in adhesion and motility. RNA-binding proteins (RBPs) regulate alternative splicing (AS) during EMT, enabling a single gene to produce multiple protein isoforms that affect tumor progression. Disruption of RBP-AS interactions may disrupt the progress of diseases like cancer. Despite the importance of RBP-AS relationships in EMT, few computational methods predict these associations. Existing models struggle in sparse settings with limited known associations. To improve performance, we incorporate both sparsity constraints and heterogeneous biological data to infer RBP-AS associations.MOTIVATIONEpithelial-mesenchymal transition (EMT) plays a key role in cancer metastasis by promoting changes in adhesion and motility. RNA-binding proteins (RBPs) regulate alternative splicing (AS) during EMT, enabling a single gene to produce multiple protein isoforms that affect tumor progression. Disruption of RBP-AS interactions may disrupt the progress of diseases like cancer. Despite the importance of RBP-AS relationships in EMT, few computational methods predict these associations. Existing models struggle in sparse settings with limited known associations. To improve performance, we incorporate both sparsity constraints and heterogeneous biological data to infer RBP-AS associations.We propose a new method based on Accelerated Proximal DC Algorithm (APDCA) for predicting RBP-AS associations. In particular, APDCA combines sparse low-rank matrix factorization with a Difference-of-Convex (DC) optimization framework and uses extrapolation to improve convergence. A key feature of APDCA is the use of a sparsity constraint, which filters out noise and highlights key associations. In addition, integrating multiple related data sources with direct or indirect relationships can help in reaching a more comprehensive view of RBPs and AS events and to reduce the impact of false positives associated with individual data sources. we prove that our proposed algorithm is convergent under some conditions and the experimental results have illustrated that APDCA outperforms six baseline methods in both AUC and AUPR. A case study on the RBP QKI shows that the top predictions are verified by the OncoSplicing database. Thus, APDCA provides a fast, interpretable, and scalable tool for discovering post-transcriptional regulatory interactions.RESULTWe propose a new method based on Accelerated Proximal DC Algorithm (APDCA) for predicting RBP-AS associations. In particular, APDCA combines sparse low-rank matrix factorization with a Difference-of-Convex (DC) optimization framework and uses extrapolation to improve convergence. A key feature of APDCA is the use of a sparsity constraint, which filters out noise and highlights key associations. In addition, integrating multiple related data sources with direct or indirect relationships can help in reaching a more comprehensive view of RBPs and AS events and to reduce the impact of false positives associated with individual data sources. we prove that our proposed algorithm is convergent under some conditions and the experimental results have illustrated that APDCA outperforms six baseline methods in both AUC and AUPR. A case study on the RBP QKI shows that the top predictions are verified by the OncoSplicing database. Thus, APDCA provides a fast, interpretable, and scalable tool for discovering post-transcriptional regulatory interactions. Epithelial-mesenchymal transition (EMT) plays a key role in cancer metastasis by promoting changes in adhesion and motility. RNA-binding proteins (RBPs) regulate alternative splicing (AS) during EMT, enabling a single gene to produce multiple protein isoforms that affect tumor progression. Disruption of RBP-AS interactions may disrupt the progress of diseases like cancer. Despite the importance of RBP-AS relationships in EMT, few computational methods predict these associations. Existing models struggle in sparse settings with limited known associations. To improve performance, we incorporate both sparsity constraints and heterogeneous biological data to infer RBP-AS associations. We propose a new method based on Accelerated Proximal DC Algorithm (APDCA) for predicting RBP-AS associations. In particular, APDCA combines sparse low-rank matrix factorization with a Difference-of-Convex (DC) optimization framework and uses extrapolation to improve convergence. A key feature of APDCA is the use of a sparsity constraint, which filters out noise and highlights key associations. In addition, integrating multiple related data sources with direct or indirect relationships can help in reaching a more comprehensive view of RBPs and AS events and to reduce the impact of false positives associated with individual data sources. we prove that our proposed algorithm is convergent under some conditions and the experimental results have illustrated that APDCA outperforms six baseline methods in both AUC and AUPR. A case study on the RBP QKI shows that the top predictions are verified by the OncoSplicing database. Thus, APDCA provides a fast, interpretable, and scalable tool for discovering post-transcriptional regulatory interactions. Motivation: Epithelial-mesenchymal transition (EMT) plays a key role in cancer metastasis by promoting changes in adhesion and motility. RNA-binding proteins (RBPs) regulate alternative splicing (AS) during EMT, enabling a single gene to produce multiple protein isoforms that affect tumor progression. Disruption of RBP-AS interactions may disrupt the progress of diseases like cancer. Despite the importance of RBP-AS relationships in EMT, few computational methods predict these associations. Existing models struggle in sparse settings with limited known associations. To improve performance, we incorporate both sparsity constraints and heterogeneous biological data to infer RBP–AS associations. Result: We propose a new method based on A ccelerated P roximal DC A lgorithm (APDCA) for predicting RBP–AS associations. In particular, APDCA combines sparse low-rank matrix factorization with a Difference-of-Convex (DC) optimization framework and uses extrapolation to improve convergence. A key feature of APDCA is the use of a sparsity constraint, which filters out noise and highlights key associations. In addition, integrating multiple related data sources with direct or indirect relationships can help in reaching a more comprehensive view of RBPs and AS events and to reduce the impact of false positives associated with individual data sources. we prove that our proposed algorithm is convergent under some conditions and the experimental results have illustrated that APDCA outperforms six baseline methods in both AUC and AUPR. A case study on the RBP QKI shows that the top predictions are verified by the OncoSplicing database. Thus, APDCA provides a fast, interpretable, and scalable tool for discovering post-transcriptional regulatory interactions. |
| Author | Zou, Quan Qiu, Yushan Bai, Zheng-Jian He, Yangsong Ching, Wai-Ki |
| Author_xml | – sequence: 1 givenname: Yangsong surname: He fullname: He, Yangsong – sequence: 2 givenname: Zheng-Jian surname: Bai fullname: Bai, Zheng-Jian – sequence: 3 givenname: Wai-Ki surname: Ching fullname: Ching, Wai-Ki – sequence: 4 givenname: Quan surname: Zou fullname: Zou, Quan – sequence: 5 givenname: Yushan orcidid: 0000-0002-9393-3648 surname: Qiu fullname: Qiu, Yushan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/41196940$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNkdtu1DAQhi1URA_wBgj5kpts49ixY-6WLdBKlag4XFs-TLpeJXawnaI-Aa9NtrtUSCPNaPTNPxffOToJMQBCb0m9IlSQy12cU9DDarLGr0hNKOftC3RG2pZWgrbdyX_zKTrPeVfXyyj5K3TKCJFcsvoM_VnfXW3WH_A6YG3tnHQBrIPD0Pdgi38APELZRof7mPCUwPllG-6xzjlar4uPIWMD5TdAwN8-3uWn6_X3Ch4glIzdnPY4TL5sYfB6qEbIEOz2cdQDLkmH7Pchr9HLXg8Z3hz7Bfr5-dOPzXV1-_XLzWZ9W1kq6lL13DKjrWmgkbrjlEpGGbfGARdOU9OCMaZxVhhpReN4bToA4gRbiola0wt0c8h1Ue_UlPyo06OK2qunRUz3Sqfi7QCKS9KbRjKQHWVtR2Tf2JZZB1I7IShdst4fsqYUf82Qixp9tjAMOkCcs6INl5wJTrsFfXdEZzOCe378T8QCsANgU8w5Qf-MkFrtfaujb7X3rY6-6V81XKMZ |
| Cites_doi | 10.1016/j.ymeth.2020.05.002 10.1007/978-1-4939-1221-6_8 10.1007/BF02139472 10.1038/nrg3813 10.1038/s41586-020-2077-3 10.1093/nar/gkw943 10.1016/j.compbiomed.2022.106527 10.1093/nar/gkab989 10.1016/j.drudis.2022.103432 10.1561/2400000003 10.1038/nrm2777 10.1093/bioinformatics/btx781 10.1007/BF01585745 10.1093/bib/bbac364 10.1093/bfgp/ely027 10.1261/rna.074187.119 10.1093/bioinformatics/bty366 10.1093/nar/gky1010 10.1038/s41392-024-01734-2 10.1137/110827144 10.1016/j.cell.2009.11.007 10.1038/nrg3778 10.1073/pnas.1419161111 10.1371/journal.pgen.1002218 10.3390/biom5020893 10.1002/pro.3978 10.1186/s12967-024-05793-5 10.1093/bioinformatics/btw639 10.1038/s41598-017-12763-z |
| ContentType | Journal Article |
| Copyright | Copyright: © 2025 He et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| Copyright_xml | – notice: Copyright: © 2025 He et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 DOA |
| DOI | 10.1371/journal.pcbi.1013665 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1553-7358 |
| ExternalDocumentID | oai_doaj_org_article_691fb294e98345819f2c54cde9ad7733 41196940 10_1371_journal_pcbi_1013665 |
| Genre | Journal Article |
| GroupedDBID | --- 123 29O 2WC 53G 5VS 7X7 88E 8FE 8FG 8FH 8FI 8FJ AAFWJ AAKPC AAUCC AAWOE AAYXX ABDBF ABUWG ACCTH ACGFO ACIHN ACIWK ACPRK ACUHS ADBBV AEAQA AENEX AEUYN AFFHD AFKRA AFPKN AFRAH AHMBA ALMA_UNASSIGNED_HOLDINGS AOIJS ARAPS AZQEC B0M BAIFH BAWUL BBNVY BBTPI BCNDV BENPR BGLVJ BHPHI BPHCQ BVXVI BWKFM CCPQU CITATION CS3 DIK DWQXO E3Z EAP EAS EBD EBS EJD EMK EMOBN ESX F5P FPL FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HMCUK HYE IAO IGS INH INR ISN ISR ITC J9A K6V K7- KQ8 LK8 M1P M7P O5R O5S OK1 OVT P2P P62 PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PV9 RNS RPM RZL SV3 TR2 TUS UKHRP WOW XSB ~8M ADRAZ C1A CGR CUY CVF ECM EIF H13 IPNFZ M48 NPM RIG WOQ 7X8 |
| ID | FETCH-LOGICAL-c370t-f6c4bacb2e29a863394346cbde67da3b5ebbb2dc7b9c72d60b8ee1d74d74470a3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001610832900002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1553-7358 1553-734X |
| IngestDate | Mon Nov 17 19:31:45 EST 2025 Fri Nov 07 23:27:04 EST 2025 Sat Nov 15 01:41:55 EST 2025 Thu Nov 13 04:36:44 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 11 |
| Language | English |
| License | Copyright: © 2025 He et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c370t-f6c4bacb2e29a863394346cbde67da3b5ebbb2dc7b9c72d60b8ee1d74d74470a3 |
| Notes | new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-9393-3648 |
| OpenAccessLink | https://doaj.org/article/691fb294e98345819f2c54cde9ad7733 |
| PMID | 41196940 |
| PQID | 3269647638 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_691fb294e98345819f2c54cde9ad7733 proquest_miscellaneous_3269647638 pubmed_primary_41196940 crossref_primary_10_1371_journal_pcbi_1013665 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-11-01 |
| PublicationDateYYYYMMDD | 2025-11-01 |
| PublicationDate_xml | – month: 11 year: 2025 text: 2025-11-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | PLoS computational biology |
| PublicationTitleAlternate | PLoS Comput Biol |
| PublicationYear | 2025 |
| Publisher | Public Library of Science (PLoS) |
| Publisher_xml | – name: Public Library of Science (PLoS) |
| References | A LTH (pcbi.1013665.ref023) 2005; 133 R Park (pcbi.1013665.ref015) 2019; 18 Z Huang (pcbi.1013665.ref037) 2019; 47 F-Y Zong (pcbi.1013665.ref009) 2014; 10 X-D Fu (pcbi.1013665.ref002) 2014; 15 AM Fredericks (pcbi.1013665.ref003) 2015; 5 B Wu (pcbi.1013665.ref028) 2014; 24 Y Tao (pcbi.1013665.ref007) 2024; 9 IM Shapiro (pcbi.1013665.ref004) 2011; 7 pcbi.1013665.ref006 Y Nesterov (pcbi.1013665.ref025) 1983; 27 J-Y Hou (pcbi.1013665.ref010) 2024; 22 JP Thiery (pcbi.1013665.ref005) 2009; 139 S Kelaini (pcbi.1013665.ref008) 2021; 10 M Zeng (pcbi.1013665.ref016) 2020; 179 J Yan (pcbi.1013665.ref013) 2023; 28 EL Van Nostrand (pcbi.1013665.ref032) 2020; 583 Y Qiu (pcbi.1013665.ref019) 2021; 22 S Shen (pcbi.1013665.ref035) 2014; 111 W Lan (pcbi.1013665.ref017) 2017; 33 Y Qiu (pcbi.1013665.ref034) 2020; 26 JMA Bullock (pcbi.1013665.ref021) 2018; 34 JK Lenstra (pcbi.1013665.ref029) 1990; 46 N Parikh (pcbi.1013665.ref024) 2014; 1 pcbi.1013665.ref031 pcbi.1013665.ref030 S Xiao (pcbi.1013665.ref026) 2022; 50 R Oughtred (pcbi.1013665.ref040) 2021; 30 S Gerstberger (pcbi.1013665.ref033) 2014; 15 N Sheng (pcbi.1013665.ref012) 2023; 153 Y Chen (pcbi.1013665.ref014) 2022; 23 M Chen (pcbi.1013665.ref001) 2009; 10 B Adhikari (pcbi.1013665.ref020) 2018; 34 H-Y Huang (pcbi.1013665.ref036) 2020; 48 Q-W Wu (pcbi.1013665.ref018) 2021; 22 C Gu (pcbi.1013665.ref022) 2017; 7 GA Watson (pcbi.1013665.ref027) 1992; 2 J Piñero (pcbi.1013665.ref038) 2017; 45 L Huo (pcbi.1013665.ref011) 2021; 22 DS Wishart (pcbi.1013665.ref039) 2017; 46 |
| References_xml | – volume: 179 start-page: 73 year: 2020 ident: pcbi.1013665.ref016 article-title: SDLDA: lncRNA-disease association prediction based on singular value decomposition and deep learning publication-title: Methods doi: 10.1016/j.ymeth.2020.05.002 – ident: pcbi.1013665.ref006 doi: 10.1007/978-1-4939-1221-6_8 – volume: 2 start-page: 321 issue: 3 year: 1992 ident: pcbi.1013665.ref027 article-title: Linear best approximation using a class of polyhedral norms publication-title: Numer Algor doi: 10.1007/BF02139472 – volume: 46 year: 2017 ident: pcbi.1013665.ref039 article-title: DrugBank 5.0: a major update to the DrugBank database for 2018 publication-title: Nucleic Acids Research – volume: 15 start-page: 829 issue: 12 year: 2014 ident: pcbi.1013665.ref033 article-title: A census of human RNA-binding proteins publication-title: Nat Rev Genet doi: 10.1038/nrg3813 – volume: 583 start-page: 711 issue: 7818 year: 2020 ident: pcbi.1013665.ref032 article-title: A large-scale binding and functional map of human RNA-binding proteins publication-title: Nature doi: 10.1038/s41586-020-2077-3 – volume: 45 year: 2017 ident: pcbi.1013665.ref038 article-title: DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants publication-title: Nucleic Acids Res doi: 10.1093/nar/gkw943 – volume: 153 start-page: 106527 year: 2023 ident: pcbi.1013665.ref012 article-title: Data resources and computational methods for lncRNA-disease association prediction publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2022.106527 – volume: 50 issue: 2 year: 2022 ident: pcbi.1013665.ref026 article-title: High-Throughput-Methyl-Reading (HTMR) assay: a solution based on nucleotide methyl-binding proteins enables large-scale screening for DNA/RNA methyltransferases and demethylases publication-title: Nucleic Acids Res doi: 10.1093/nar/gkab989 – volume: 28 start-page: 103432 issue: 2 year: 2023 ident: pcbi.1013665.ref013 article-title: Recent advances in predicting lncRNA-disease associations based on computational methods publication-title: Drug Discov Today doi: 10.1016/j.drudis.2022.103432 – volume: 1 start-page: 127 issue: 3 year: 2014 ident: pcbi.1013665.ref024 article-title: Proximal algorithms publication-title: FNT in Optimization doi: 10.1561/2400000003 – volume: 10 start-page: 741 issue: 11 year: 2009 ident: pcbi.1013665.ref001 article-title: Mechanisms of alternative splicing regulation: insights from molecular and genomics approaches publication-title: Nat Rev Mol Cell Biol doi: 10.1038/nrm2777 – volume: 10 start-page: 366 issue: 5 year: 2021 ident: pcbi.1013665.ref008 article-title: RNA-binding proteins hold key roles in function, dysfunction, and disease publication-title: Biology (Basel) – volume: 34 start-page: 1466 issue: 9 year: 2018 ident: pcbi.1013665.ref020 article-title: DNCON2: improved protein contact prediction using two-level deep convolutional neural networks publication-title: Bioinformatics doi: 10.1093/bioinformatics/btx781 – volume: 46 start-page: 259 year: 1990 ident: pcbi.1013665.ref029 article-title: Approximation algorithms for scheduling unrelated parallel machines publication-title: Mathematical Programming doi: 10.1007/BF01585745 – ident: pcbi.1013665.ref030 – volume: 23 issue: 6 year: 2022 ident: pcbi.1013665.ref014 article-title: Deep learning models for disease-associated circRNA prediction: a review publication-title: Brief Bioinform doi: 10.1093/bib/bbac364 – volume: 18 start-page: 133 issue: 2 year: 2019 ident: pcbi.1013665.ref015 article-title: Immune checkpoints and cancer in the immunogenomics era publication-title: Brief Funct Genomics doi: 10.1093/bfgp/ely027 – volume: 133 start-page: 23 issue: 1 year: 2005 ident: pcbi.1013665.ref023 article-title: The DC programming and DCA revisited with DC models of real-world non-convex optimization problems publication-title: Ann Oper Res – volume: 26 start-page: 1257 issue: 9 year: 2020 ident: pcbi.1013665.ref034 article-title: A combinatorially regulated RNA splicing signature predicts breast cancer EMT states and patient survival publication-title: RNA doi: 10.1261/rna.074187.119 – volume: 27 start-page: 372 issue: 2 year: 1983 ident: pcbi.1013665.ref025 article-title: A method for solving the convex programming problem with convergence rate O(1/k2) publication-title: Sov Math Dokl – volume: 34 start-page: 3584 issue: 20 year: 2018 ident: pcbi.1013665.ref021 article-title: Jwalk and MNXL web server: model validation using restraints from crosslinking mass spectrometry publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty366 – volume: 47 year: 2019 ident: pcbi.1013665.ref037 article-title: HMDD v3.0: a database for experimentally supported human microRNA-disease associations publication-title: Nucleic Acids Res doi: 10.1093/nar/gky1010 – volume: 9 start-page: 26 issue: 1 year: 2024 ident: pcbi.1013665.ref007 article-title: Alternative splicing and related RNA binding proteins in human health and disease publication-title: Signal Transduct Target Ther doi: 10.1038/s41392-024-01734-2 – volume: 24 start-page: 766 issue: 2 year: 2014 ident: pcbi.1013665.ref028 article-title: On the Moreau–Yosida regularization of the vector $k$-norm related functions publication-title: SIAM J Optim doi: 10.1137/110827144 – volume: 139 start-page: 871 issue: 5 year: 2009 ident: pcbi.1013665.ref005 article-title: Epithelial-mesenchymal transitions in development and disease publication-title: Cell doi: 10.1016/j.cell.2009.11.007 – volume: 22 issue: 6 year: 2021 ident: pcbi.1013665.ref011 article-title: Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects publication-title: Brief Bioinform – volume: 15 start-page: 689 issue: 10 year: 2014 ident: pcbi.1013665.ref002 article-title: Context-dependent control of alternative splicing by RNA-binding proteins publication-title: Nat Rev Genet doi: 10.1038/nrg3778 – volume: 22 issue: 5 year: 2021 ident: pcbi.1013665.ref019 article-title: Prediction of RNA-binding protein and alternative splicing event associations during epithelial-mesenchymal transition based on inductive matrix completion publication-title: Brief Bioinform – volume: 111 issue: 51 year: 2014 ident: pcbi.1013665.ref035 article-title: rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data publication-title: Proc Natl Acad Sci U S A doi: 10.1073/pnas.1419161111 – volume: 48 year: 2020 ident: pcbi.1013665.ref036 article-title: miRTarBase 2020 : updates to the experimentally validated microRNA-target interaction database publication-title: Nucleic Acids Res – volume: 7 issue: 8 year: 2011 ident: pcbi.1013665.ref004 article-title: An EMT-driven alternative splicing program occurs in human breast cancer and modulates cellular phenotype publication-title: PLoS Genet doi: 10.1371/journal.pgen.1002218 – volume: 5 start-page: 893 issue: 2 year: 2015 ident: pcbi.1013665.ref003 article-title: RNA-binding proteins: splicing factors and disease publication-title: Biomolecules doi: 10.3390/biom5020893 – volume: 10 issue: 4 year: 2014 ident: pcbi.1013665.ref009 article-title: The RNA-binding protein QKI suppresses cancer-associated aberrant splicing publication-title: PLoS Genet – volume: 30 start-page: 187 issue: 1 year: 2021 ident: pcbi.1013665.ref040 article-title: The BioGRID database: a comprehensive biomedical resource of curated protein, genetic, and chemical interactions publication-title: Protein Sci doi: 10.1002/pro.3978 – volume: 22 start-page: 995 issue: 1 year: 2024 ident: pcbi.1013665.ref010 article-title: PTBP1 crotonylation promotes colorectal cancer progression through alternative splicing-mediated upregulation of the PKM2 gene publication-title: J Transl Med doi: 10.1186/s12967-024-05793-5 – volume: 22 issue: 5 year: 2021 ident: pcbi.1013665.ref018 article-title: GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest publication-title: Brief Bioinform – volume: 33 start-page: 458 issue: 3 year: 2017 ident: pcbi.1013665.ref017 article-title: LDAP: a web server for lncRNA-disease association prediction publication-title: Bioinformatics doi: 10.1093/bioinformatics/btw639 – volume: 7 start-page: 12442 issue: 1 year: 2017 ident: pcbi.1013665.ref022 article-title: Global network random walk for predicting potential human lncRNA-disease associations publication-title: Sci Rep doi: 10.1038/s41598-017-12763-z – ident: pcbi.1013665.ref031 |
| SSID | ssj0035896 |
| Score | 2.4735649 |
| Snippet | Motivation: Epithelial-mesenchymal transition (EMT) plays a key role in cancer metastasis by promoting changes in adhesion and motility. RNA-binding proteins... Epithelial-mesenchymal transition (EMT) plays a key role in cancer metastasis by promoting changes in adhesion and motility. RNA-binding proteins (RBPs)... MotivationEpithelial-mesenchymal transition (EMT) plays a key role in cancer metastasis by promoting changes in adhesion and motility. RNA-binding proteins... |
| SourceID | doaj proquest pubmed crossref |
| SourceType | Open Website Aggregation Database Index Database |
| StartPage | e1013665 |
| SubjectTerms | Algorithms Alternative Splicing - genetics Computational Biology - methods Epithelial-Mesenchymal Transition - genetics Humans Neoplasms - genetics Neoplasms - metabolism RNA-Binding Proteins - genetics RNA-Binding Proteins - metabolism |
| Title | APDCA: An accurate and effective method for predicting associations between RBPs and AS-events during epithelial-mesenchymal transition |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/41196940 https://www.proquest.com/docview/3269647638 https://doaj.org/article/691fb294e98345819f2c54cde9ad7733 |
| Volume | 21 |
| WOSCitedRecordID | wos001610832900002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1553-7358 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0035896 issn: 1553-7358 databaseCode: DOA dateStart: 20050101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1553-7358 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0035896 issn: 1553-7358 databaseCode: P5Z dateStart: 20050601 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1553-7358 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0035896 issn: 1553-7358 databaseCode: M7P dateStart: 20050601 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1553-7358 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0035896 issn: 1553-7358 databaseCode: K7- dateStart: 20050601 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1553-7358 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0035896 issn: 1553-7358 databaseCode: 7X7 dateStart: 20050601 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1553-7358 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0035896 issn: 1553-7358 databaseCode: BENPR dateStart: 20050601 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1553-7358 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0035896 issn: 1553-7358 databaseCode: PIMPY dateStart: 20050601 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVATS databaseName: Public Library of Science (PLoS) Journals Open Access customDbUrl: eissn: 1553-7358 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0035896 issn: 1553-7358 databaseCode: FPL dateStart: 20050101 isFulltext: true titleUrlDefault: http://www.plos.org/publications/ providerName: Public Library of Science |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELaggMQF8SzLY2UkrqZJ7GQSbtnSFYiyispDC5fIdmxRiaarzS5SfwF_mxk7W3pBXJCiOUROYvmbeGbkmW8Ye1l0icmqNBegXIIBiqpEqb0XGfoO2qYedODp_nIMi0W5XFbNlVZflBMW6YHjwh0UVerxbcpVpVQ52i-f2VzZzlW6A5CB5xO9nl0wFfdgmZehMxc1xREg1XIsmpOQHowYvVpZc0qxqyzIsFwxSoG7_-8OZzA887vszugx8jrO9B675vr77FbsIXnxgP2qmzeH9Wte91xbuyXmB677jsdEDdzLeGwSzdE75as1nctQpjPXf3AZ-JitxU9mzRCerj-KQO008FjHyN2Kijd-oLaKMypYst8vznBWGzJ1IevrIfs8P_p0-FaM3RWElZBshC-sMtqazGWVLgspiSmusKZzBXRamtwZY7LOgqksZF2RmNK5tAOFl4JEy0dsrz_v3WPGQVmvTAo-91rZ0urUWYMoV1ZmufFywsRuedtVJNFow0kaYPARV7ElONoRjgmbEQaXY4kCO9xAxWhHxWj_pRgT9mKHYIu_DJ2D6N6db4cWPVaqv8WdZ8L2I7SXn1Ip8QWp5Mn_mMJTdjujdsGhdPEZ29ust-45u2l_bk6H9ZRdhyUEWU7ZjdnRojmZBi1GOW-OUb4HMaVk1AZlk3_DUc27D83X33x_-v0 |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=APDCA%3A+An+accurate+and+effective+method+for+predicting+associations+between+RBPs+and+AS-events+during+epithelial-mesenchymal+transition&rft.jtitle=PLoS+computational+biology&rft.au=Yangsong+He&rft.au=Zheng-Jian+Bai&rft.au=Wai-Ki+Ching&rft.au=Quan+Zou&rft.date=2025-11-01&rft.pub=Public+Library+of+Science+%28PLoS%29&rft.issn=1553-734X&rft.eissn=1553-7358&rft.volume=21&rft.issue=11&rft.spage=e1013665&rft_id=info:doi/10.1371%2Fjournal.pcbi.1013665&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_691fb294e98345819f2c54cde9ad7733 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1553-7358&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1553-7358&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1553-7358&client=summon |