miRNA Targets: From Prediction Tools to Experimental Validation
MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on target mRNAs, miRNAs play gene-regulatory roles, producing remarkable changes in several physiological and pathological processes. Thus, the iden...
Saved in:
| Published in | Methods and protocols Vol. 4; no. 1; p. 1 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
MDPI AG
24.12.2020
MDPI |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on target mRNAs, miRNAs play gene-regulatory roles, producing remarkable changes in several physiological and pathological processes. Thus, the identification of miRNA-mRNA target interactions is fundamental for discovering the regulatory network governed by miRNAs. The best way to achieve this goal is usually by computational prediction followed by experimental validation of these miRNA-mRNA interactions. This review summarizes the key strategies for miRNA target identification. Several tools for computational analysis exist, each with different approaches to predict miRNA targets, and their number is constantly increasing. The major algorithms available for this aim, including Machine Learning methods, are discussed, to provide practical tips for familiarizing with their assumptions and understanding how to interpret the results. Then, all the experimental procedures for verifying the authenticity of the identified miRNA-mRNA target pairs are described, including High-Throughput technologies, in order to find the best approach for miRNA validation. For each strategy, strengths and weaknesses are discussed, to enable users to evaluate and select the right approach for their interests. |
|---|---|
| AbstractList | MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on target mRNAs, miRNAs play gene-regulatory roles, producing remarkable changes in several physiological and pathological processes. Thus, the identification of miRNA-mRNA target interactions is fundamental for discovering the regulatory network governed by miRNAs. The best way to achieve this goal is usually by computational prediction followed by experimental validation of these miRNA-mRNA interactions. This review summarizes the key strategies for miRNA target identification. Several tools for computational analysis exist, each with different approaches to predict miRNA targets, and their number is constantly increasing. The major algorithms available for this aim, including Machine Learning methods, are discussed, to provide practical tips for familiarizing with their assumptions and understanding how to interpret the results. Then, all the experimental procedures for verifying the authenticity of the identified miRNA-mRNA target pairs are described, including High-Throughput technologies, in order to find the best approach for miRNA validation. For each strategy, strengths and weaknesses are discussed, to enable users to evaluate and select the right approach for their interests. MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on target mRNAs, miRNAs play gene-regulatory roles, producing remarkable changes in several physiological and pathological processes. Thus, the identification of miRNA-mRNA target interactions is fundamental for discovering the regulatory network governed by miRNAs. The best way to achieve this goal is usually by computational prediction followed by experimental validation of these miRNA-mRNA interactions. This review summarizes the key strategies for miRNA target identification. Several tools for computational analysis exist, each with different approaches to predict miRNA targets, and their number is constantly increasing. The major algorithms available for this aim, including Machine Learning methods, are discussed, to provide practical tips for familiarizing with their assumptions and understanding how to interpret the results. Then, all the experimental procedures for verifying the authenticity of the identified miRNA-mRNA target pairs are described, including High-Throughput technologies, in order to find the best approach for miRNA validation. For each strategy, strengths and weaknesses are discussed, to enable users to evaluate and select the right approach for their interests.MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on target mRNAs, miRNAs play gene-regulatory roles, producing remarkable changes in several physiological and pathological processes. Thus, the identification of miRNA-mRNA target interactions is fundamental for discovering the regulatory network governed by miRNAs. The best way to achieve this goal is usually by computational prediction followed by experimental validation of these miRNA-mRNA interactions. This review summarizes the key strategies for miRNA target identification. Several tools for computational analysis exist, each with different approaches to predict miRNA targets, and their number is constantly increasing. The major algorithms available for this aim, including Machine Learning methods, are discussed, to provide practical tips for familiarizing with their assumptions and understanding how to interpret the results. Then, all the experimental procedures for verifying the authenticity of the identified miRNA-mRNA target pairs are described, including High-Throughput technologies, in order to find the best approach for miRNA validation. For each strategy, strengths and weaknesses are discussed, to enable users to evaluate and select the right approach for their interests. |
| Author | Marzocchi, Carlotta Cantara, Silvia Riolo, Giulia Ricci, Claudia |
| AuthorAffiliation | Department of Medical, Surgical and Neurological Sciences, University of Siena, 53100 Siena, Italy; riolo@student.unisi.it (G.R.); cantara@unisi.it (S.C.); carlottamarzocchi@libero.it (C.M.) |
| AuthorAffiliation_xml | – name: Department of Medical, Surgical and Neurological Sciences, University of Siena, 53100 Siena, Italy; riolo@student.unisi.it (G.R.); cantara@unisi.it (S.C.); carlottamarzocchi@libero.it (C.M.) |
| Author_xml | – sequence: 1 givenname: Giulia orcidid: 0000-0003-2888-5755 surname: Riolo fullname: Riolo, Giulia – sequence: 2 givenname: Silvia orcidid: 0000-0002-5741-295X surname: Cantara fullname: Cantara, Silvia – sequence: 3 givenname: Carlotta orcidid: 0000-0002-8573-4913 surname: Marzocchi fullname: Marzocchi, Carlotta – sequence: 4 givenname: Claudia orcidid: 0000-0002-2431-0308 surname: Ricci fullname: Ricci, Claudia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33374478$$D View this record in MEDLINE/PubMed |
| BookMark | eNptkl9PFDEUxRuDEVh58QOYSXwxJAv9O2190BACQkLQmNXXptPeWbvpTNd21ui3t7CIQHxq0_vrybn33H20M6YREHpF8BFjGh8P68IxwRiTZ2iPcqznmkq98-C-iw5KWVWCEo4lFy_QLmNMci7VHvowhC_XJ83C5iVM5V1zntPQfM7gg5tCGptFSrE0U2rOfq0hhwHGycbmm43B2xvgJXre21jg4O6coa_nZ4vTi_nVp4-XpydXcyeYmubaOkU8g1Z45YkXTmnmheihc7QnikrWE-654q2WnXKK91T7FvegBXRta9kMXW51fbIrs65ObP5tkg3m9iHlpbF5Ci6CwZZQQrkivWDcdlh1lnVacEkBMyxo1Xq_1VpvugG8qz1lGx-JPq6M4btZpp9GqjpxpqrA2zuBnH5soExmCMVBjHaEtCmGcllBQrGo6Jsn6Cpt8lhHZaiogaiW1zBm6PVDR_dW_uZUgcMt4HIqJUN_jxBsbvbA_NuDCuMnsAvTbVq1mxD_9-UP-gyy0A |
| CitedBy_id | crossref_primary_10_3390_ijms23116016 crossref_primary_10_3389_fendo_2022_1084656 crossref_primary_10_1016_j_theriogenology_2024_06_026 crossref_primary_10_1186_s40246_023_00478_4 crossref_primary_10_15407_oncology_2023_04_255 crossref_primary_10_1002_smtd_202100402 crossref_primary_10_1016_j_canlet_2022_215677 crossref_primary_10_1007_s11030_024_10855_3 crossref_primary_10_3390_ijms25010603 crossref_primary_10_1186_s13293_025_00686_8 crossref_primary_10_1002_mco2_745 crossref_primary_10_1016_j_plaphy_2023_107940 crossref_primary_10_3390_biom12020332 crossref_primary_10_47836_mjmhs_20_1_21 crossref_primary_10_1021_acs_est_4c04190 crossref_primary_10_3390_genes13122323 crossref_primary_10_3390_genes14030550 crossref_primary_10_3390_epigenomes6020012 crossref_primary_10_3390_biology11121798 crossref_primary_10_3390_ncrna9020018 crossref_primary_10_3390_ijms242316593 crossref_primary_10_1007_s10528_023_10458_x crossref_primary_10_3390_cells13151289 crossref_primary_10_1093_nar_gkab1079 crossref_primary_10_1111_jcmm_16694 crossref_primary_10_1038_s41598_022_21473_0 crossref_primary_10_7717_peerj_18054 crossref_primary_10_1093_database_baad014 crossref_primary_10_1093_nar_gkad1142 crossref_primary_10_1021_acsomega_4c06551 crossref_primary_10_1212_NXG_0000000000200144 crossref_primary_10_3390_ijms25158493 crossref_primary_10_3390_biomedicines12030691 crossref_primary_10_3390_ijms232314716 crossref_primary_10_1016_j_arr_2024_102483 crossref_primary_10_1016_j_omtn_2023_102102 crossref_primary_10_3390_ijms25137498 crossref_primary_10_1016_j_jbc_2024_108015 crossref_primary_10_1016_j_bbagrm_2024_195022 crossref_primary_10_1016_j_humgen_2024_201306 crossref_primary_10_1021_acsomega_3c09633 crossref_primary_10_1016_j_jplph_2021_153451 crossref_primary_10_1111_and_14497 crossref_primary_10_1109_TCBB_2024_3362308 crossref_primary_10_3390_cancers13215312 crossref_primary_10_3390_ncrna10020021 crossref_primary_10_1007_s11845_023_03469_5 crossref_primary_10_3390_cells12091339 crossref_primary_10_1002_mnfr_202100428 crossref_primary_10_20517_evcna_2024_43 crossref_primary_10_3390_v14010041 crossref_primary_10_1016_j_repbio_2024_100924 crossref_primary_10_1016_j_aquatox_2023_106624 crossref_primary_10_1016_j_compbiolchem_2022_107729 crossref_primary_10_3390_ijms242316842 crossref_primary_10_3389_fcimb_2022_809052 crossref_primary_10_1089_regen_2023_0027 crossref_primary_10_3389_fpls_2024_1460540 crossref_primary_10_3389_fbinf_2024_1493712 crossref_primary_10_3390_ani14020214 crossref_primary_10_3390_ncrna10020022 crossref_primary_10_1093_jambio_lxad104 crossref_primary_10_1016_j_ncrna_2024_08_007 crossref_primary_10_1016_j_compbiolchem_2024_108208 crossref_primary_10_1007_s00705_023_05917_0 crossref_primary_10_1038_s41587_023_01951_0 crossref_primary_10_3390_genes15050574 crossref_primary_10_1186_s11658_022_00317_9 crossref_primary_10_1002_wrna_1768 crossref_primary_10_1038_s41598_024_72608_4 crossref_primary_10_31590_ejosat_1220962 crossref_primary_10_1152_ajpcell_00382_2023 crossref_primary_10_1016_j_prp_2024_155707 crossref_primary_10_1007_s00210_024_03719_y crossref_primary_10_3389_fgene_2022_812183 crossref_primary_10_3390_genes13091528 crossref_primary_10_3389_fgene_2022_1070528 crossref_primary_10_1111_jipb_13848 crossref_primary_10_31857_S0016675823100156 crossref_primary_10_1007_s11103_024_01433_0 crossref_primary_10_3390_cancers13174492 crossref_primary_10_1002_ijch_202200088 crossref_primary_10_2174_0122115366278651240105071533 crossref_primary_10_3390_ijms242417224 crossref_primary_10_1016_j_csbj_2024_07_014 crossref_primary_10_1111_ejn_16523 crossref_primary_10_1016_j_compbiomed_2021_104544 crossref_primary_10_1038_s42003_024_07309_9 crossref_primary_10_1080_03602532_2022_2048846 crossref_primary_10_3390_ijms232314755 crossref_primary_10_3390_ijms25084548 crossref_primary_10_1016_j_virusres_2021_198631 crossref_primary_10_55230_mabjournal_v51i5_2343 crossref_primary_10_1007_s00438_024_02199_2 crossref_primary_10_1007_s10722_024_02271_1 crossref_primary_10_3390_ijms241411861 crossref_primary_10_1016_j_tig_2022_02_006 crossref_primary_10_1038_s41598_023_42868_7 crossref_primary_10_2478_rrlm_2022_0022 crossref_primary_10_3389_fpls_2021_780877 crossref_primary_10_7717_peerj_17396 crossref_primary_10_1186_s12866_024_03187_7 crossref_primary_10_1093_bfgp_elad020 crossref_primary_10_1002_advs_202201501 crossref_primary_10_1007_s11103_022_01279_4 crossref_primary_10_3390_ijms252312883 crossref_primary_10_20517_mrr_2024_46 crossref_primary_10_1134_S1022795423100150 crossref_primary_10_1016_j_ncrna_2022_06_003 crossref_primary_10_3390_app122412908 crossref_primary_10_1016_j_imu_2023_101162 crossref_primary_10_1007_s10142_023_01178_x crossref_primary_10_1016_j_ttbdis_2023_102249 crossref_primary_10_1523_JNEUROSCI_0365_24_2024 crossref_primary_10_5230_jgc_2022_22_e31 crossref_primary_10_1007_s10142_025_01535_y crossref_primary_10_3390_app112311366 crossref_primary_10_1016_j_bpc_2025_107421 crossref_primary_10_1177_11772719241296619 crossref_primary_10_3390_ijms241411356 crossref_primary_10_1016_j_compbiomed_2025_109886 crossref_primary_10_3390_v16121844 crossref_primary_10_1017_neu_2023_27 crossref_primary_10_3390_cells11142151 crossref_primary_10_3390_insects15040238 crossref_primary_10_1038_s41598_024_63462_5 crossref_primary_10_1080_15476286_2023_2244790 crossref_primary_10_37349_emed_2023_00159 |
| Cites_doi | 10.1147/rd.33.0210 10.1007/978-1-61779-083-6_11 10.1093/nar/gkp292 10.1261/rna.768207 10.1093/bioinformatics/btp503 10.1016/j.ymeth.2010.08.009 10.4161/rna.28649 10.1038/sj.embor.7401088 10.3389/fendo.2018.00402 10.1093/nar/gkt393 10.1016/S0092-8674(03)01018-3 10.1038/nmeth.1608 10.1371/journal.pbio.0030085 10.3390/biology2010189 10.1007/s13238-010-0129-4 10.1155/2019/9029351 10.1016/j.gene.2005.09.035 10.1186/gb-2010-11-8-r90 10.1016/j.cell.2008.12.023 10.1016/j.cell.2006.07.031 10.1002/wrna.1217 10.1016/j.cell.2013.03.043 10.1111/j.1365-313X.2011.04882.x 10.1016/j.ymeth.2007.09.005 10.2174/1389202921666200106111813 10.1016/j.bbrc.2014.01.181 10.1093/bioinformatics/btm484 10.2144/btn-2019-0065 10.1093/nar/gkv050 10.1093/nar/gnh040 10.1126/science.1121158 10.2174/138920209789208219 10.1126/science.1258040 10.1186/1471-2105-7-411 10.1101/gr.089367.108 10.1186/gb-2003-5-1-r1 10.1152/ajpgi.00120.2010 10.1093/nar/gky1141 10.1093/bib/bbz111 10.1038/nrg3765 10.3389/fonc.2019.01114 10.1093/nar/gkz757 10.1093/bioinformatics/btw189 10.1093/nar/gkx1067 10.1007/978-1-61779-083-6_12 10.1186/1471-2105-7-S5-S20 10.1093/nar/gkn851 10.1016/j.molcel.2007.06.017 10.1038/srep08004 10.1093/hmg/ddp443 10.1038/nsmb1226 10.1261/rna.2239606 10.1158/0008-5472.CAN-08-1942 10.1038/nsmb.1921 10.1093/nar/gkr330 10.1261/rna.035634.112 10.1093/bioinformatics/bty424 10.1145/2975167.2975212 10.1007/s00018-013-1551-6 10.1007/978-1-4939-9207-2_1 10.1007/978-1-4419-9863-7 10.1016/j.cell.2009.01.002 10.1016/j.csbj.2020.02.019 10.1016/j.cell.2004.12.035 10.1371/journal.pbio.0030264 10.1038/nmeth1079 10.1038/ng2135 10.1093/nar/gkm995 10.1261/rna.034934.112 10.1261/rna.7290705 10.3390/ijms151120266 10.4161/rna.4.2.4640 10.1038/msb.2010.24 10.1038/s41467-018-07981-6 10.1101/gr.082701.108 10.1093/nar/gkv457 10.1093/nar/gkx1141 10.7554/eLife.05005 10.1038/nbt.1543 10.1074/jbc.M611393200 10.1007/978-1-4939-7046-9_8 10.1038/nbt.1873 10.1038/ng1536 10.1016/j.bbagrm.2015.08.007 10.1016/j.cell.2010.03.009 10.3389/fgene.2017.00059 10.1186/1472-6750-3-18 10.1093/nar/gkl243 10.1371/journal.pone.0206239 10.1159/000485491 10.1038/nrg3965 10.1073/pnas.0500775102 10.1093/bioinformatics/bts615 |
| ContentType | Journal Article |
| Copyright | 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2020 by the authors. 2020 |
| Copyright_xml | – notice: 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2020 by the authors. 2020 |
| DBID | AAYXX CITATION NPM 8FE 8FH ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO GNUQQ HCIFZ LK8 M7P PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.3390/mps4010001 |
| DatabaseName | CrossRef PubMed ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One ProQuest Central ProQuest Central Student SciTech Premium Collection ProQuest Biological Science Collection Biological Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) Open Access资源_DOAJ |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Biological Science Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection Biological Science Database ProQuest SciTech Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition Natural Science Collection ProQuest Central Korea Biological Science Collection ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) MEDLINE - Academic |
| DatabaseTitleList | CrossRef MEDLINE - Academic Publicly Available Content Database PubMed |
| 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: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2409-9279 |
| ExternalDocumentID | oai_doaj_org_article_0a1212481f534ab08ba3b95472e03052 PMC7839038 33374478 10_3390_mps4010001 |
| Genre | Journal Article Review |
| GroupedDBID | AADQD AAFWJ AAYXX ADBBV AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS AOIJS BCNDV CITATION GROUPED_DOAJ HYE IAO IGS ITC MODMG M~E OK1 PGMZT RPM NPM 8FE 8FH ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO GNUQQ HCIFZ LK8 M7P PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS 7X8 5PM |
| ID | FETCH-LOGICAL-c538t-9ac81d3e65d8d1d5c893d55febc2f18273f14d484697b8c84f29d60fe95eb66a3 |
| IEDL.DBID | BENPR |
| ISSN | 2409-9279 |
| IngestDate | Wed Aug 27 01:18:25 EDT 2025 Thu Aug 21 13:48:15 EDT 2025 Fri Jul 11 11:23:50 EDT 2025 Fri Jul 25 11:52:29 EDT 2025 Wed Feb 19 02:09:55 EST 2025 Tue Jul 01 01:20:19 EDT 2025 Thu Apr 24 22:57:45 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | validation criteria predictive strategies experimental validation prediction tools high-throughput technologies miRNA target machine learning |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0 Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c538t-9ac81d3e65d8d1d5c893d55febc2f18273f14d484697b8c84f29d60fe95eb66a3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 |
| ORCID | 0000-0002-2431-0308 0000-0003-2888-5755 0000-0002-5741-295X 0000-0002-8573-4913 |
| OpenAccessLink | https://www.proquest.com/docview/2521486433?pq-origsite=%requestingapplication% |
| PMID | 33374478 |
| PQID | 2521486433 |
| PQPubID | 5046877 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_0a1212481f534ab08ba3b95472e03052 pubmedcentral_primary_oai_pubmedcentral_nih_gov_7839038 proquest_miscellaneous_2473901205 proquest_journals_2521486433 pubmed_primary_33374478 crossref_primary_10_3390_mps4010001 crossref_citationtrail_10_3390_mps4010001 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20201224 |
| PublicationDateYYYYMMDD | 2020-12-24 |
| PublicationDate_xml | – month: 12 year: 2020 text: 20201224 day: 24 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland – name: Basel |
| PublicationTitle | Methods and protocols |
| PublicationTitleAlternate | Methods Protoc |
| PublicationYear | 2020 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Roberts (ref_58) 2017; 1617 Khan (ref_82) 2009; 27 ref_12 Ritchie (ref_63) 2014; 445 Schirle (ref_11) 2014; 346 Kim (ref_83) 2006; 7 Kuhn (ref_65) 2008; 44 ref_19 Lee (ref_54) 2009; 19 ref_16 Borchert (ref_52) 2009; 18 Riley (ref_91) 2012; 18 Rehmsmeier (ref_17) 2006; 34 Kertesz (ref_30) 2007; 39 Weinmann (ref_90) 2009; 136 Thomas (ref_76) 2010; 17 ref_72 Jonas (ref_5) 2015; 16 Fiszer (ref_4) 2014; 71 Miranda (ref_48) 2006; 126 Hayder (ref_3) 2018; 9 Helwak (ref_95) 2013; 153 Bandyopadhyay (ref_44) 2009; 25 Kobayashi (ref_21) 2016; 1859 Chen (ref_59) 2020; 48 Schmittgen (ref_71) 2004; 32 Yang (ref_53) 2012; 70 Lukasik (ref_62) 2016; 32 Grimson (ref_28) 2007; 27 Sansom (ref_68) 2010; 299 Carroll (ref_78) 2014; 5 Thomson (ref_84) 2014; 39 Kozomara (ref_2) 2019; 47 Zhang (ref_94) 2011; 29 Lewis (ref_9) 2003; 115 Xiao (ref_38) 2009; 37 Enright (ref_25) 2003; 5 Maragkakis (ref_50) 2009; 37 Gumienny (ref_32) 2015; 43 Elton (ref_64) 2015; 14 Liu (ref_61) 2019; 9 Long (ref_23) 2007; 14 Bandyopadhyay (ref_40) 2015; 5 Oliveira (ref_56) 2017; 8 Lewis (ref_10) 2005; 120 Parveen (ref_35) 2019; 20 Nicolas (ref_74) 2011; 732 Beitzinger (ref_88) 2011; 732 Betel (ref_47) 2010; 11 Hafner (ref_92) 2010; 141 Bartel (ref_8) 2009; 136 Hausser (ref_96) 2014; 15 Yousef (ref_41) 2007; 23 Beitzinger (ref_87) 2007; 4 Wang (ref_67) 2019; 10 Agarwal (ref_29) 2015; 4 Chou (ref_36) 2017; 46 Arvey (ref_80) 2010; 6 Wen (ref_45) 2018; 34 ref_60 Zhu (ref_97) 2007; 282 Loher (ref_33) 2012; 28 Nuovo (ref_69) 2010; 52 Nielsen (ref_14) 2007; 13 Saetrom (ref_42) 2005; 11 ref_66 Marin (ref_55) 2013; 19 Akhtar (ref_15) 2019; 1970 Forero (ref_73) 2019; 67 Bracken (ref_81) 2008; 68 Robins (ref_22) 2005; 102 Hock (ref_89) 2007; 8 Karagkouni (ref_37) 2017; 46 Ebert (ref_77) 2007; 4 Murphy (ref_70) 2013; 2 Karbiener (ref_79) 2014; 15 Krek (ref_27) 2005; 37 Yue (ref_18) 2009; 10 Fan (ref_57) 2017; 44 Ciaudo (ref_39) 2020; 18 Paraskevopoulou (ref_46) 2013; 41 Samuel (ref_34) 1959; 3 Farh (ref_51) 2005; 310 Friedman (ref_13) 2009; 19 Han (ref_86) 2015; 9 Betel (ref_49) 2008; 36 Robins (ref_31) 2015; 43 Kern (ref_6) 2019; 21 ref_43 Kishore (ref_93) 2011; 8 Huang (ref_75) 2010; 1 Sethupathy (ref_20) 2006; 12 ref_1 Tarang (ref_85) 2014; 11 Watanabe (ref_26) 2006; 365 (ref_24) 2010; 39 ref_7 |
| References_xml | – volume: 3 start-page: 210 year: 1959 ident: ref_34 article-title: Some studies in Machine Learning using the game of checkers publication-title: IBM J. Res. Dev. doi: 10.1147/rd.33.0210 – volume: 732 start-page: 139 year: 2011 ident: ref_74 article-title: Experimental validation of microRNA targets using a luciferase reporter system publication-title: Methods Mol. Biol. doi: 10.1007/978-1-61779-083-6_11 – volume: 37 start-page: W273 year: 2009 ident: ref_50 article-title: DIANA-microT web server: Elucidating microRNA functions through target prediction publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkp292 – volume: 13 start-page: 1894 year: 2007 ident: ref_14 article-title: Determinants of targeting by endogenous and exogenous microRNAs and siRNAs publication-title: RNA doi: 10.1261/rna.768207 – volume: 25 start-page: 2625 year: 2009 ident: ref_44 article-title: TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp503 – volume: 52 start-page: 307 year: 2010 ident: ref_69 article-title: In situ detection of microRNAs in paraffin embedded, formalin fixed tissues and the colocalization of their putative targets publication-title: Methods doi: 10.1016/j.ymeth.2010.08.009 – volume: 11 start-page: 324 year: 2014 ident: ref_85 article-title: Macros in microRNA target identification. A comparative analysis of in silico, in vitro and in vivo approaches to microRNA target identification publication-title: RNA Biol. doi: 10.4161/rna.28649 – volume: 8 start-page: 1052 year: 2007 ident: ref_89 article-title: Proteomic and functional analysis of Argonaute-containing mRNAprotein complexes in human cells publication-title: EMBO Rep. doi: 10.1038/sj.embor.7401088 – volume: 9 start-page: 402 year: 2018 ident: ref_3 article-title: Overview of microRNA biogenesis, mechanisms of actions, and circulation publication-title: Front. Endocrinol. doi: 10.3389/fendo.2018.00402 – volume: 41 start-page: W169 year: 2013 ident: ref_46 article-title: DIANA-microT web server v5.0: Service integration into miRNA functional analysis workflows publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkt393 – volume: 115 start-page: 787 year: 2003 ident: ref_9 article-title: Prediction of mammalian microRNA targets publication-title: Cell doi: 10.1016/S0092-8674(03)01018-3 – volume: 8 start-page: 559 year: 2011 ident: ref_93 article-title: A quantitative analysis of CLIP methods for identifying binding sites of RNA-binding proteins publication-title: Nat. Methods doi: 10.1038/nmeth.1608 – ident: ref_12 doi: 10.1371/journal.pbio.0030085 – volume: 2 start-page: 189 year: 2013 ident: ref_70 article-title: MicroRNA target identification-experimental approaches publication-title: Biology doi: 10.3390/biology2010189 – volume: 1 start-page: 979 year: 2010 ident: ref_75 article-title: A study of miRNAs targets prediction and experimental validation publication-title: Protein Cell doi: 10.1007/s13238-010-0129-4 – ident: ref_66 doi: 10.1155/2019/9029351 – volume: 365 start-page: 2 year: 2006 ident: ref_26 article-title: Computational analysis of microRNA targets in Caenorhabditis elegans publication-title: Gene doi: 10.1016/j.gene.2005.09.035 – volume: 11 start-page: R90 year: 2010 ident: ref_47 article-title: Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites publication-title: Genome Biol. doi: 10.1186/gb-2010-11-8-r90 – volume: 136 start-page: 496 year: 2009 ident: ref_90 article-title: Importin 8 is a gene silencing factor that targets argonaute proteins to distinct mRNAs publication-title: Cell doi: 10.1016/j.cell.2008.12.023 – volume: 126 start-page: 1203 year: 2006 ident: ref_48 article-title: A pattern-based method for the identification of microRNA binding sites and their corresponding heteroduplexes publication-title: Cell doi: 10.1016/j.cell.2006.07.031 – volume: 5 start-page: 361 year: 2014 ident: ref_78 article-title: Understanding principles of miRNA target recognition and function through integrated biological and bioinformatics approaches publication-title: Wiley Interdiscip. Rev. RNA doi: 10.1002/wrna.1217 – volume: 153 start-page: 654 year: 2013 ident: ref_95 article-title: Mapping the human miRNA interactome by CLASH reveals frequent non-canonical binding publication-title: Cell doi: 10.1016/j.cell.2013.03.043 – volume: 70 start-page: 421 year: 2012 ident: ref_53 article-title: Alternative mRNA processing increases the complexity of microRNA-based gene regulation in Arabidopsis publication-title: Plant. J. doi: 10.1111/j.1365-313X.2011.04882.x – volume: 44 start-page: 47 year: 2008 ident: ref_65 article-title: Experimental validation of miRNA targets publication-title: Methods doi: 10.1016/j.ymeth.2007.09.005 – volume: 20 start-page: 537 year: 2019 ident: ref_35 article-title: Applications of Machine Learning in miRNA discovery and target prediction publication-title: Curr. Genom. doi: 10.2174/1389202921666200106111813 – volume: 445 start-page: 780 year: 2014 ident: ref_63 article-title: Refining microRNA target predictions: Sorting the wheat from the chaff publication-title: Biochem. Biophys. Res. Commun. doi: 10.1016/j.bbrc.2014.01.181 – volume: 23 start-page: 2987 year: 2007 ident: ref_41 article-title: Naïve Bayes for microRNA target predictions-Machine Learning for microRNA targets publication-title: Bioinformatics doi: 10.1093/bioinformatics/btm484 – volume: 67 start-page: 192 year: 2019 ident: ref_73 article-title: qPCR-based methods for expression analysis of miRNAs publication-title: Biotechniques doi: 10.2144/btn-2019-0065 – volume: 43 start-page: 1380 year: 2015 ident: ref_32 article-title: Accurate transcriptome-wide prediction of microRNA targets and small interfering RNA off-targets with MIRZA-G publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkv050 – volume: 32 start-page: e43 year: 2004 ident: ref_71 article-title: A High-Throughput method to monitor the expression of microRNA precursors publication-title: Nucleic Acids Res. doi: 10.1093/nar/gnh040 – volume: 310 start-page: 1817 year: 2005 ident: ref_51 article-title: The widespread impact of mammalian MicroRNAs on mRNA repression and evolution publication-title: Science doi: 10.1126/science.1121158 – volume: 10 start-page: 478 year: 2009 ident: ref_18 article-title: Survey of computational algorithms for microRNA target prediction publication-title: Curr. Genom. doi: 10.2174/138920209789208219 – volume: 346 start-page: 608 year: 2014 ident: ref_11 article-title: Structural basis for microRNA targeting publication-title: Science doi: 10.1126/science.1258040 – volume: 14 start-page: 758 year: 2015 ident: ref_64 article-title: Experimental procedures to identify and validate specific mRNA targets of miRNAs publication-title: EXCLI J. – volume: 7 start-page: 411 year: 2006 ident: ref_83 article-title: MiTarget: microRNA target-gene prediction using a support vector machine publication-title: BMC Bioinform. doi: 10.1186/1471-2105-7-411 – volume: 19 start-page: 1175 year: 2009 ident: ref_54 article-title: New class of microRNA targets containing simultaneous 5-UTR and 3-UTR interaction sites publication-title: Genome Res. doi: 10.1101/gr.089367.108 – volume: 5 start-page: R1 year: 2003 ident: ref_25 article-title: MicroRNA targets in Drosophila publication-title: Genome Biol. doi: 10.1186/gb-2003-5-1-r1 – volume: 299 start-page: G632 year: 2010 ident: ref_68 article-title: miR-802 regulates human angiotensin II type 1 receptor expression in intestinal epithelial C2BBe1 cells publication-title: Am. J. Physiol. Gastrointest. Liver Physiol. doi: 10.1152/ajpgi.00120.2010 – volume: 47 start-page: D155 year: 2019 ident: ref_2 article-title: miRBase: from microRNA sequences to function publication-title: Nucleic Acids Res. doi: 10.1093/nar/gky1141 – volume: 21 start-page: 1999 year: 2019 ident: ref_6 article-title: What’s the target: Understanding two decades of in silico microRNA-target prediction publication-title: Brief. Bioinform. doi: 10.1093/bib/bbz111 – volume: 15 start-page: 599 year: 2014 ident: ref_96 article-title: Identification and consequences of miRNA-target interactions—Beyond repression of gene expression publication-title: Nat. Rev. Genet. doi: 10.1038/nrg3765 – volume: 9 start-page: 1114 year: 2019 ident: ref_61 article-title: Bioinformatic identification of miR-622 key target genes and experimental validation of the miR-622-RNF8 axis in breast cancer publication-title: Front. Oncol. doi: 10.3389/fonc.2019.01114 – volume: 48 start-page: D127 year: 2020 ident: ref_59 article-title: miRDB: An online database for prediction of functional microRNA targets publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkz757 – volume: 32 start-page: 2722 year: 2016 ident: ref_62 article-title: Tools4miRs—One place to gather all the tools for miRNA analysis publication-title: Bioinformatics doi: 10.1093/bioinformatics/btw189 – volume: 46 start-page: D296 year: 2017 ident: ref_36 article-title: miRTarBase update 2018: A resource for experimentally validated microRNA target interactions publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkx1067 – volume: 732 start-page: 153 year: 2011 ident: ref_88 article-title: Experimental identification of microRNA targets by immunoprecipitation of Argonaute protein complexes publication-title: Methods Mol. Biol. doi: 10.1007/978-1-61779-083-6_12 – ident: ref_16 doi: 10.1186/1471-2105-7-S5-S20 – volume: 37 start-page: D105 year: 2009 ident: ref_38 article-title: miRecords: An integrated resource for microRNA-target interactions publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkn851 – volume: 27 start-page: 91 year: 2007 ident: ref_28 article-title: MicroRNA targeting specificity in mammals: Determinants beyond seed pairing publication-title: Mol. Cell doi: 10.1016/j.molcel.2007.06.017 – volume: 5 start-page: 8004 year: 2015 ident: ref_40 article-title: MBSTAR: Multiple instance learning for predicting specific functional binding sites in microRNA targets publication-title: Sci. Rep. doi: 10.1038/srep08004 – volume: 18 start-page: 4801 year: 2009 ident: ref_52 article-title: Adenosine deamination in human transcripts generates novel microRNA binding sites publication-title: Hum. Mol. Genet. doi: 10.1093/hmg/ddp443 – volume: 14 start-page: 287 year: 2007 ident: ref_23 article-title: Potent effect of target structure on microRNA function publication-title: Nat. Struct. Mol. Biol. doi: 10.1038/nsmb1226 – volume: 12 start-page: 192 year: 2006 ident: ref_20 article-title: TarBase: A comprehensive database of experimentally supported animal microRNA targets publication-title: RNA doi: 10.1261/rna.2239606 – volume: 68 start-page: 7846 year: 2008 ident: ref_81 article-title: A double-negative feedback loop between ZEB1-SIP1 and the microRNA-200 family regulates epithelial-mesenchymal transition publication-title: Cancer Res. doi: 10.1158/0008-5472.CAN-08-1942 – volume: 17 start-page: 1169 year: 2010 ident: ref_76 article-title: Desperately seeking microRNA targets publication-title: Nat. Struct Mol. Biol. doi: 10.1038/nsmb.1921 – volume: 39 start-page: 6845 year: 2014 ident: ref_84 article-title: Experimental strategies for microRNA target identification publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkr330 – volume: 9 start-page: 29 year: 2015 ident: ref_86 article-title: Advanced applications of RNA sequencing and challenges publication-title: Bioinform. Biol. Insights – volume: 19 start-page: 467 year: 2013 ident: ref_55 article-title: Searching the coding region for microRNA targets publication-title: RNA doi: 10.1261/rna.035634.112 – volume: 34 start-page: 3781 year: 2018 ident: ref_45 article-title: DeepMirTar: A deep-learning approach for predicting human miRNA targets publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty424 – ident: ref_43 doi: 10.1145/2975167.2975212 – volume: 71 start-page: 2253 year: 2014 ident: ref_4 article-title: The panorama of miRNA-mediated mechanisms in mammalian cells publication-title: Cell. Mol. Life Sci. doi: 10.1007/s00018-013-1551-6 – volume: 1970 start-page: 1 year: 2019 ident: ref_15 article-title: A practical guide to miRNA target prediction publication-title: Methods Mol. Biol. doi: 10.1007/978-1-4939-9207-2_1 – ident: ref_1 doi: 10.1007/978-1-4419-9863-7 – volume: 136 start-page: 215 year: 2009 ident: ref_8 article-title: MicroRNAs: Target recognition and regulatory functions publication-title: Cell doi: 10.1016/j.cell.2009.01.002 – volume: 18 start-page: 548 year: 2020 ident: ref_39 article-title: Prediction of the miRNA interactome—Established methods and upcoming perspectives publication-title: Comput. Struct. Biotechnol. J. doi: 10.1016/j.csbj.2020.02.019 – volume: 120 start-page: 15 year: 2005 ident: ref_10 article-title: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets publication-title: Cell doi: 10.1016/j.cell.2004.12.035 – ident: ref_19 doi: 10.1371/journal.pbio.0030264 – volume: 4 start-page: 721 year: 2007 ident: ref_77 article-title: MicroRNA sponges: Competitive inhibitors of small RNAs in mammalian cells publication-title: Nat. Methods doi: 10.1038/nmeth1079 – volume: 39 start-page: 1278 year: 2007 ident: ref_30 article-title: The role of site accessibility in microRNA target recognition publication-title: Nat. Genet. doi: 10.1038/ng2135 – volume: 36 start-page: D149 year: 2008 ident: ref_49 article-title: The microRNA.org resource: Targets and expression publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkm995 – volume: 18 start-page: 1581 year: 2012 ident: ref_91 article-title: Association of Argonaute proteins and microRNAs can occur after cell lysis publication-title: RNA doi: 10.1261/rna.034934.112 – volume: 11 start-page: 995 year: 2005 ident: ref_42 article-title: Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms publication-title: RNA doi: 10.1261/rna.7290705 – volume: 15 start-page: 20266 year: 2014 ident: ref_79 article-title: Hunting needle in the haystack: A guide to obtain biologically meaningful microRNA targets publication-title: Int. J. Mol. Sci. doi: 10.3390/ijms151120266 – volume: 4 start-page: 76 year: 2007 ident: ref_87 article-title: Identification of human microRNA targets from isolated Argonaute protein complexes publication-title: RNA Biol. doi: 10.4161/rna.4.2.4640 – volume: 6 start-page: 363 year: 2010 ident: ref_80 article-title: Target mRNA abundance dilutes microRNA and siRNA activity publication-title: Mol. Syst. Biol. doi: 10.1038/msb.2010.24 – volume: 10 start-page: 95 year: 2019 ident: ref_67 article-title: Single-cell microRNA-mRNA co-sequencing reveals non-genetic heterogeneity and mechanisms of microRNA regulation publication-title: Nat. Commun. doi: 10.1038/s41467-018-07981-6 – ident: ref_7 – volume: 19 start-page: 92 year: 2009 ident: ref_13 article-title: Most mammalian mRNAs are conserved targets of microRNAs publication-title: Genome Res. doi: 10.1101/gr.082701.108 – volume: 43 start-page: W474 year: 2015 ident: ref_31 article-title: PACCMIT/PACCMIT-CDS: Identifying microRNA targets in 3′ UTRs and coding sequences publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkv457 – volume: 46 start-page: D239 year: 2017 ident: ref_37 article-title: DIANA-tarbase v8: A decade-long collection of experimentally supported miRNA-gene interactions publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkx1141 – volume: 4 start-page: e05005 year: 2015 ident: ref_29 article-title: Predicting effective microRNA target sites in mammalian mRNAs publication-title: eLife doi: 10.7554/eLife.05005 – volume: 27 start-page: 549 year: 2009 ident: ref_82 article-title: Transfection of small RNAs globally perturbs gene regulation by endogenous microRNAs publication-title: Nat. Biotechnol. doi: 10.1038/nbt.1543 – volume: 282 start-page: 14328 year: 2007 ident: ref_97 article-title: MicroRNA-21 targets the tumor suppressor gene tropomyosin 1 (TPM1) publication-title: J. Biol. Chem. doi: 10.1074/jbc.M611393200 – volume: 1617 start-page: 109 year: 2017 ident: ref_58 article-title: Computational prediction of microRNA target genes, target prediction databases, and web resources publication-title: Methods Mol. Biol. doi: 10.1007/978-1-4939-7046-9_8 – volume: 29 start-page: 607 year: 2011 ident: ref_94 article-title: Mapping in vivo protein-RNA interactions at single-nucleotide resolution from HITS-CLIP data publication-title: Nat. Biotechnol. doi: 10.1038/nbt.1873 – volume: 37 start-page: 495 year: 2005 ident: ref_27 article-title: Combinatorial microRNA target predictions publication-title: Nat. Genet. doi: 10.1038/ng1536 – volume: 1859 start-page: 71 year: 2016 ident: ref_21 article-title: RISC assembly: Coordination between small RNAs and Argonaute proteins publication-title: Biochim. Biophys. Acta doi: 10.1016/j.bbagrm.2015.08.007 – volume: 141 start-page: 129 year: 2010 ident: ref_92 article-title: Transcriptome wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP publication-title: Cell doi: 10.1016/j.cell.2010.03.009 – volume: 8 start-page: 59 year: 2017 ident: ref_56 article-title: Combining results from distinct microRNA target prediction tools enhances the performance of analyses publication-title: Front. Genet. doi: 10.3389/fgene.2017.00059 – ident: ref_72 doi: 10.1186/1472-6750-3-18 – volume: 34 start-page: W451 year: 2006 ident: ref_17 article-title: RNAhybrid: microRNA target prediction easy, fast and flexible publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkl243 – volume: 39 start-page: 19 year: 2010 ident: ref_24 article-title: Efficient use of accessibility in microRNA target prediction publication-title: Nucleic Acids Res. – ident: ref_60 doi: 10.1371/journal.pone.0206239 – volume: 44 start-page: 1311 year: 2017 ident: ref_57 article-title: MiR-193a-3p is an important tumour suppressor in lung cancer and directly targets KRAS publication-title: Cell Physiol. Biochem. doi: 10.1159/000485491 – volume: 16 start-page: 421 year: 2015 ident: ref_5 article-title: Towards a molecular understanding of microRNA-mediated gene silencing publication-title: Nat. Rev. Genet. doi: 10.1038/nrg3965 – volume: 102 start-page: 4006 year: 2005 ident: ref_22 article-title: Incorporating structure to predict microRNA targets publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.0500775102 – volume: 28 start-page: 3322 year: 2012 ident: ref_33 article-title: Interactive exploration of RNA22 microRNA target predictions publication-title: Bioinformatics doi: 10.1093/bioinformatics/bts615 |
| SSID | ssj0002140745 |
| Score | 2.5239248 |
| SecondaryResourceType | review_article |
| Snippet | MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 1 |
| SubjectTerms | Adenosine Algorithms Artificial intelligence Computer applications experimental validation Gene expression Gene regulation Learning algorithms Machine learning MicroRNAs miRNA miRNA target Post-transcription prediction tools predictive strategies Proteins Review RNA polymerase Seeds Transfer RNA validation criteria |
| SummonAdditionalLinks | – databaseName: Open Access资源_DOAJ dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NS8MwFA-ykxdR_KpfRPTiodh8NakXUdkYgiIyZbeSpAkOXCvb_P99abu5ieDFa_MO6XvJe79fk_4eQueAUaHMah5LnzAgKNLGmgkTa62EoMoRU_cieHhM-y_8fiiGS62-wp2wRh64cdxloglkV66IF4xrkyijmckEl9SFtVpnX6h5S2Qq5GAKvEFy0eiRMuD1l-OPKVCJAGlWKlAt1P8buvx5SXKp6vQ20UYLF_FNM80ttObKbXQ9Hj0_3uBBfYl7eoV7k2qMnybhyCW4GQ-q6n2KZxXuLsn341dA3E0DpR300usO7vpx2wghtpCPZnGmLcBK5lJRqIIUwgLIKITwzljqgSBI5gkvOECJTBplFfc0K9LEu0w4k6aa7aJOWZVuH2EniSsAYzBCbPjHTQfC4R33BkpVYWyELubOyW2rEh6aVbznwBaCI_NvR0bobGH70Whj_Gp1G3y8sAh61vUDiHLeRjn_K8oROppHKG832TSnAD24AkjFInS6GIbtEc48dOmqT7DhMnzVoYmI0F4T0MVMGGOSc6kiJFdCvTLV1ZFy9FZLcEvAlQlTB__xbodonQYST2hM-RHqzCaf7hiQzsyc1Iv6C_qv-W8 priority: 102 providerName: Directory of Open Access Journals |
| Title | miRNA Targets: From Prediction Tools to Experimental Validation |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/33374478 https://www.proquest.com/docview/2521486433 https://www.proquest.com/docview/2473901205 https://pubmed.ncbi.nlm.nih.gov/PMC7839038 https://doaj.org/article/0a1212481f534ab08ba3b95472e03052 |
| Volume | 4 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1La9wwEBbt5tJLSenLaRpU2ksPJtbLknsJSdklFLqEsCm5GT3bQNberjf_PyNb62RL6NUag5iRZr4ZSd8g9AUwKoRZzXMZCgYJirS5ZsLkWishqPLE9L0Ifs7L8yv-41pcp4Jbl65Vbn1i76hda2ON_JhCnOEK4ic7Wf3NY9eoeLqaWmg8R3vggpWaoL2z6fzicqyywD8QI8XAS8ogvz9erjpIKSK02YlEPWH_Uyjz38uSj6LPbB-9TLARnw52foWe-eY1OlneXM5P8aK_zN19w7N1u8QX63j0EtWNF2172-FNi6ePaPzxL0DeQyOlN-hqNl18P89TQ4Tcgl_a5JW2AC-ZL4VTjjhhAWw4IYI3lgZIFCQLhDsOkKKSRlnFA61cWQRfCW_KUrO3aNK0jX-PsJfEO8AajBAb37rpmHgEz4OBkOWMzdDXrXJqm9jCY9OK2xqyhqjI-kGRGfo8yq4Gjownpc6ijkeJyGvdf2jXv-u0TepCEzAkVyQIxrUplNHMVIJL6qNnohk63FqoTputqx-WRoY-jcOwTeLZh258ewcyXMbqDi1Eht4NBh1nwhiTnEuVIblj6p2p7o40N396Km4J-LJg6uD_0_qAXtCYphOaU36IJpv1nf8IWGZjjtKCPeprAfd-5vTo |
| linkProvider | ProQuest |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB6V7QEuCMQrUGgQcOAQNfEjdpBQ1ZZdbWm7qqot6i21HQcqdZNlsxXiT_EbGefVLqq49Rpb0Wge_r7xYwbgPXJUhFnFApGHFBMUYQJFuQ6UkpwTaSNd9yI4msTjU_b1jJ-twZ_uLYy7VtmtifVCnZXG7ZFvEcQZJhE_6fb8Z-C6RrnT1a6FRuMWB_b3L0zZqs_7X9C-HwgZDad746DtKhAYDO5lkCiDHI3amGcyizJuELEzznOrDcmRbQuaRyxjiMuJ0NJIlpMki8PcJtzqOFYU_3sP1hmNQzKA9d3h5Pik39VBGRGTeVMHldIk3JrNK0xhHJVaQb66QcBtrPbfy5k30G70CB62NNXfafzqMazZ4glszy5OJjv-tL48Xn3yR4ty5h8v3FGPM68_LcvLyl-W_vBG2wD_GzL9pnHTUzi9E1U9g0FRFvYF-FZENkNuQ6PIuLd1yiU6uWW5RojMtPHgY6ec1LTVyV2TjMsUsxSnyPRakR686-fOm5oct87adTruZ7g62vWHcvE9bcMyDVWE2M1klHPKlA6lVlQnnAli3UpIPNjoLJS2wV2l167owdt-GMPSnbWowpZXOIcJt5tEQu7B88agvSSUUsGYkB6IFVOviLo6Ulz8qEt_C-SzIZUv_y_WJtwfT48O08P9ycEreEDcFkFEAsI2YLBcXNnXyKOW-k3rvD6c33W8_AVs6zFh |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB6VrYS4IBCv0AJBwIFDtIkfcYKEqpbuqqWwWlVb1FuwHZtW6ibLZivEX-PXMc6rXVRx6zW2Imse_r6xxzMAb5GjIsxKFggbUgxQhA4k5SqQMuGcJCZSdS-Cr5P44IR9PuWnG_Cnewvj0iq7PbHeqPNSuzPyIUGcYQniJx3aNi1iuj_eWfwMXAcpd9PatdNoTOTI_P6F4Vv18XAfdf2OkPFo9ukgaDsMBBodfRWkUiNfoybmeZJHOdeI3jnn1ihNLDJvQW3EcoYYnQqV6IRZkuZxaE3KjYpjSfG_d2BTuKhoAJt7o8n0uD_hwfUiPvOmJiqlaTicLyoMZxytWkPBulnATQz330TNa8g3fgD3W8rq7zY29hA2TPEIdubnx5Ndf1Ynklcf_PGynPvTpbv2car2Z2V5Ufmr0h9dayHgf0PW3zRxegwntyKqJzAoysI8A9-IyOTIc2gUaffOTrqgxxpmFcJlrrQH7zvhZLqtVO4aZlxkGLE4QWZXgvTgTT930dTnuHHWnpNxP8PV1K4_lMsfWeuiWSgjxHGWRJZTJlWYKElVypkgxu2KxIPtTkNZ6-hVdmWWHrzuh9FF3b2LLEx5iXOYcCdLJOQePG0U2q-EUioYE4kHYk3Va0tdHynOz-oy4AK5bUiT5_9f1iu4i36SfTmcHG3BPeJOCyISELYNg9Xy0rxASrVSL1vb9eH7bbvLX3HkNZY |
| 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=miRNA+Targets%3A+From+Prediction+Tools+to+Experimental+Validation&rft.jtitle=Methods+and+protocols&rft.au=Riolo%2C+Giulia&rft.au=Cantara%2C+Silvia&rft.au=Marzocchi%2C+Carlotta&rft.au=Ricci%2C+Claudia&rft.date=2020-12-24&rft.eissn=2409-9279&rft.volume=4&rft.issue=1&rft_id=info:doi/10.3390%2Fmps4010001&rft_id=info%3Apmid%2F33374478&rft.externalDocID=33374478 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2409-9279&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2409-9279&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2409-9279&client=summon |