Using Network Distance Analysis to Predict lncRNA–miRNA Interactions
LncRNA–miRNA interactions contribute to the regulation of therapeutic targets and diagnostic biomarkers in multifarious human diseases. However, it remains difficult to experimentally identify lncRNA–miRNA associations at large scale, and computational prediction methods are limited. In this study,...
Saved in:
| Published in | Interdisciplinary sciences : computational life sciences Vol. 13; no. 3; pp. 535 - 545 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Singapore
Springer Singapore
01.09.2021
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | LncRNA–miRNA interactions contribute to the regulation of therapeutic targets and diagnostic biomarkers in multifarious human diseases. However, it remains difficult to experimentally identify lncRNA–miRNA associations at large scale, and computational prediction methods are limited. In this study, we developed a network distance analysis model for lncRNA–miRNA association prediction (NDALMA). Similarity networks for lncRNAs and miRNAs were calculated and integrated with Gaussian interaction profile (GIP) kernel similarity. Then, network distance analysis was applied to the integrated similarity networks, and final scores were obtained after confidence calculation and score conversion. Our model obtained satisfactory results in fivefold cross validation, achieving an AUC of 0.8810 and an AUPR of 0.8315. Moreover, NDALMA showed superior prediction performance over several other network algorithms, and we tested the suitability and flexibility of the model by comparing different types of similarity. In addition, case studies of the relationships between lncRNAs and miRNAs were conducted, which verified the reliability of our method in predicting lncRNA–miRNA associations. The datasets and source code used in this study are available at
https://github.com/Liu-Lab-Lnu/NDALMA
.
Graphic Abstract |
|---|---|
| AbstractList | LncRNA–miRNA interactions contribute to the regulation of therapeutic targets and diagnostic biomarkers in multifarious human diseases. However, it remains difficult to experimentally identify lncRNA–miRNA associations at large scale, and computational prediction methods are limited. In this study, we developed a network distance analysis model for lncRNA–miRNA association prediction (NDALMA). Similarity networks for lncRNAs and miRNAs were calculated and integrated with Gaussian interaction profile (GIP) kernel similarity. Then, network distance analysis was applied to the integrated similarity networks, and final scores were obtained after confidence calculation and score conversion. Our model obtained satisfactory results in fivefold cross validation, achieving an AUC of 0.8810 and an AUPR of 0.8315. Moreover, NDALMA showed superior prediction performance over several other network algorithms, and we tested the suitability and flexibility of the model by comparing different types of similarity. In addition, case studies of the relationships between lncRNAs and miRNAs were conducted, which verified the reliability of our method in predicting lncRNA–miRNA associations. The datasets and source code used in this study are available at https://github.com/Liu-Lab-Lnu/NDALMA.Graphic Abstract LncRNA-miRNA interactions contribute to the regulation of therapeutic targets and diagnostic biomarkers in multifarious human diseases. However, it remains difficult to experimentally identify lncRNA-miRNA associations at large scale, and computational prediction methods are limited. In this study, we developed a network distance analysis model for lncRNA-miRNA association prediction (NDALMA). Similarity networks for lncRNAs and miRNAs were calculated and integrated with Gaussian interaction profile (GIP) kernel similarity. Then, network distance analysis was applied to the integrated similarity networks, and final scores were obtained after confidence calculation and score conversion. Our model obtained satisfactory results in fivefold cross validation, achieving an AUC of 0.8810 and an AUPR of 0.8315. Moreover, NDALMA showed superior prediction performance over several other network algorithms, and we tested the suitability and flexibility of the model by comparing different types of similarity. In addition, case studies of the relationships between lncRNAs and miRNAs were conducted, which verified the reliability of our method in predicting lncRNA-miRNA associations. The datasets and source code used in this study are available at https://github.com/Liu-Lab-Lnu/NDALMA .LncRNA-miRNA interactions contribute to the regulation of therapeutic targets and diagnostic biomarkers in multifarious human diseases. However, it remains difficult to experimentally identify lncRNA-miRNA associations at large scale, and computational prediction methods are limited. In this study, we developed a network distance analysis model for lncRNA-miRNA association prediction (NDALMA). Similarity networks for lncRNAs and miRNAs were calculated and integrated with Gaussian interaction profile (GIP) kernel similarity. Then, network distance analysis was applied to the integrated similarity networks, and final scores were obtained after confidence calculation and score conversion. Our model obtained satisfactory results in fivefold cross validation, achieving an AUC of 0.8810 and an AUPR of 0.8315. Moreover, NDALMA showed superior prediction performance over several other network algorithms, and we tested the suitability and flexibility of the model by comparing different types of similarity. In addition, case studies of the relationships between lncRNAs and miRNAs were conducted, which verified the reliability of our method in predicting lncRNA-miRNA associations. The datasets and source code used in this study are available at https://github.com/Liu-Lab-Lnu/NDALMA . LncRNA–miRNA interactions contribute to the regulation of therapeutic targets and diagnostic biomarkers in multifarious human diseases. However, it remains difficult to experimentally identify lncRNA–miRNA associations at large scale, and computational prediction methods are limited. In this study, we developed a network distance analysis model for lncRNA–miRNA association prediction (NDALMA). Similarity networks for lncRNAs and miRNAs were calculated and integrated with Gaussian interaction profile (GIP) kernel similarity. Then, network distance analysis was applied to the integrated similarity networks, and final scores were obtained after confidence calculation and score conversion. Our model obtained satisfactory results in fivefold cross validation, achieving an AUC of 0.8810 and an AUPR of 0.8315. Moreover, NDALMA showed superior prediction performance over several other network algorithms, and we tested the suitability and flexibility of the model by comparing different types of similarity. In addition, case studies of the relationships between lncRNAs and miRNAs were conducted, which verified the reliability of our method in predicting lncRNA–miRNA associations. The datasets and source code used in this study are available at https://github.com/Liu-Lab-Lnu/NDALMA . Graphic Abstract |
| Author | Zhao, Qi Feng, Huawei Yang, Pengyu Zhang, Li Liu, Hongsheng |
| Author_xml | – sequence: 1 givenname: Li surname: Zhang fullname: Zhang, Li organization: School of Life Science, Liaoning University, Research Center for Computer Simulating and Information Processing of Bio-Macromolecules of Shenyang, Liaoning University, Technology Innovation Center for Computer Simulating and Information Processing of Bio-Macromolecules of Shenyang – sequence: 2 givenname: Pengyu surname: Yang fullname: Yang, Pengyu organization: School of Information, Liaoning University – sequence: 3 givenname: Huawei surname: Feng fullname: Feng, Huawei organization: School of Life Science, Liaoning University – sequence: 4 givenname: Qi orcidid: 0000-0001-9713-1864 surname: Zhao fullname: Zhao, Qi email: zhaoqi@lnu.edu.cn organization: School of Computer Science and Software Engineering, University of Science and Technology Liaoning – sequence: 5 givenname: Hongsheng surname: Liu fullname: Liu, Hongsheng email: liuhongsheng@lnu.edu.cn organization: Research Center for Computer Simulating and Information Processing of Bio-Macromolecules of Shenyang, Liaoning University, Technology Innovation Center for Computer Simulating and Information Processing of Bio-Macromolecules of Shenyang, School of Pharmacy, Liaoning University |
| BookMark | eNp9kEFOAyEUQImpibV6AVeTuHEzymeAGZaNWm3SVGPsmlCGNtQpU4HG6Mo7eENPIrUmJl109Vm8x4d3jDqudQahM8CXgHF5FYCwQuSYQI4xZVX-cYC6UPEyB8pJJ50FFDkpGRyh4xAWGHNaFbiLBpNg3Twbm_jW-pfsxoaonDZZ36nmPdiQxTZ79Ka2OmaN00_j_vfn19KmmQ1dNF7paFsXTtDhTDXBnP7NHpoMbp-v7_PRw93wuj_KdcFIzBUILchMTQ1TXPMaqmlJoKqBC2CEAgNRME14zRWrBKaaGmFmVV1SxU1dlEUPXWzvXfn2dW1ClEsbtGka5Uy7DpIwKgghJacJPd9BF-3ap29tKA5YkLQhUWRLad-G4M1MrrxdKv8uActNWrlNK1Na-ZtWfiSp2pG0jWoTInplm_1qsVVD2uPmxv-_ao_1A9Gpj7w |
| CitedBy_id | crossref_primary_10_1038_s41598_023_42904_6 crossref_primary_10_3389_fmicb_2023_1147778 crossref_primary_10_3389_fonc_2022_987609 crossref_primary_10_3389_fmicb_2023_1170559 crossref_primary_10_1038_s41598_024_53716_7 crossref_primary_10_1186_s12890_023_02717_9 crossref_primary_10_1371_journal_pone_0269742 crossref_primary_10_1093_bib_bbac463 crossref_primary_10_3389_fgene_2022_828575 crossref_primary_10_1038_s41598_024_56694_y crossref_primary_10_1038_s41598_023_31754_x crossref_primary_10_1007_s12539_021_00478_9 crossref_primary_10_3934_mbe_2023345 crossref_primary_10_1016_j_jare_2024_06_002 crossref_primary_10_1016_j_isci_2022_105299 crossref_primary_10_1038_s41598_024_67717_z crossref_primary_10_3934_mbe_2022017 crossref_primary_10_3389_fcell_2021_726656 crossref_primary_10_3389_fgene_2022_1023615 crossref_primary_10_1111_jcmm_18180 crossref_primary_10_1186_s12859_023_05314_z crossref_primary_10_1038_s41598_024_64308_w crossref_primary_10_3934_mbe_2023085 crossref_primary_10_1021_acs_jcim_3c01214 crossref_primary_10_1111_jcmm_18345 crossref_primary_10_1371_journal_pone_0259436 crossref_primary_10_1371_journal_pone_0317369 crossref_primary_10_3934_mbe_2023476 crossref_primary_10_1016_j_compbiomed_2022_106464 crossref_primary_10_1093_bib_bbad259 crossref_primary_10_1142_S273741652350062X crossref_primary_10_3389_fmicb_2022_995323 crossref_primary_10_1038_s41598_024_63446_5 crossref_primary_10_3389_fmicb_2022_1024104 crossref_primary_10_1016_j_knosys_2023_110492 crossref_primary_10_1038_s41598_023_51126_9 crossref_primary_10_1186_s12859_021_04485_x crossref_primary_10_1371_journal_pone_0299898 crossref_primary_10_3389_fmicb_2022_1040252 crossref_primary_10_1186_s12882_025_04047_w crossref_primary_10_1016_j_compbiomed_2022_105728 crossref_primary_10_1038_s41598_024_63582_y crossref_primary_10_1111_jcmm_18571 crossref_primary_10_1109_TCBB_2022_3143770 crossref_primary_10_1038_s41598_024_55160_z crossref_primary_10_1093_bfgp_elac011 crossref_primary_10_3389_fgene_2022_864564 crossref_primary_10_3934_mbe_2022630 crossref_primary_10_1016_j_ymeth_2023_11_014 crossref_primary_10_1186_s12859_023_05571_y crossref_primary_10_1038_s41598_022_11260_2 crossref_primary_10_1038_s41598_023_46480_7 crossref_primary_10_1371_journal_pone_0264362 crossref_primary_10_3390_jof9100999 crossref_primary_10_1109_TCBB_2023_3264254 crossref_primary_10_1371_journal_pone_0272403 crossref_primary_10_1038_s41598_024_62796_4 crossref_primary_10_1093_bib_bbae533 crossref_primary_10_1111_jcmm_17889 crossref_primary_10_1089_cmb_2023_0266 crossref_primary_10_3389_fgene_2021_736158 crossref_primary_10_3389_fmicb_2022_919380 crossref_primary_10_1093_bib_bbac104 crossref_primary_10_1038_s41598_022_25730_0 crossref_primary_10_1097_MD_0000000000036456 crossref_primary_10_3389_fmicb_2022_1090770 crossref_primary_10_1186_s12864_023_09879_0 crossref_primary_10_1155_2022_9867660 crossref_primary_10_1186_s12859_022_04694_y crossref_primary_10_1186_s12859_022_04719_6 crossref_primary_10_1093_bib_bbac266 crossref_primary_10_1371_journal_pone_0278817 crossref_primary_10_1038_s41598_022_21243_y crossref_primary_10_1111_jcmm_18398 crossref_primary_10_1007_s12539_023_00602_x crossref_primary_10_1007_s12539_024_00616_z crossref_primary_10_1109_ACCESS_2024_3401005 crossref_primary_10_1186_s12864_022_08687_2 crossref_primary_10_3389_fmicb_2022_846915 crossref_primary_10_1002_cem_3553 crossref_primary_10_3389_fgene_2021_767694 crossref_primary_10_1016_j_ymthe_2022_01_041 crossref_primary_10_1371_journal_pone_0264645 crossref_primary_10_1038_s41598_024_61849_y crossref_primary_10_1038_s41598_024_58646_y crossref_primary_10_1016_j_future_2022_04_012 crossref_primary_10_1371_journal_pone_0260720 crossref_primary_10_1007_s12539_022_00535_x crossref_primary_10_1038_s41598_022_25745_7 crossref_primary_10_1111_jcmm_16818 crossref_primary_10_3389_fmicb_2022_1093615 crossref_primary_10_1089_cmb_2023_0449 crossref_primary_10_3389_fgene_2022_1010089 crossref_primary_10_1016_j_amc_2025_129346 crossref_primary_10_3934_mbe_2024131 crossref_primary_10_1093_bib_bbab440 crossref_primary_10_1038_s41598_022_08930_6 crossref_primary_10_1038_s41598_024_55187_2 crossref_primary_10_1093_bib_bbac527 crossref_primary_10_1016_j_compbiomed_2023_107414 crossref_primary_10_3389_fgene_2021_832627 crossref_primary_10_1186_s12864_024_10038_2 crossref_primary_10_1371_journal_pone_0259475 crossref_primary_10_1002_ctd2_257 crossref_primary_10_3934_mbe_2024008 crossref_primary_10_1111_jcmm_70071 crossref_primary_10_2174_1574893618666221118092849 crossref_primary_10_3389_fmicb_2022_740382 crossref_primary_10_1186_s12864_023_09496_x crossref_primary_10_1111_jcmm_18372 crossref_primary_10_1186_s12859_024_05708_7 crossref_primary_10_3390_ijms232113590 crossref_primary_10_1038_s41598_024_55812_0 crossref_primary_10_7717_peerj_17396 crossref_primary_10_3389_fmicb_2022_834982 crossref_primary_10_1038_s41598_024_53442_0 crossref_primary_10_1186_s12864_023_09829_w crossref_primary_10_1016_j_heliyon_2024_e26990 crossref_primary_10_3934_mbe_2022432 crossref_primary_10_1016_j_compbiomed_2022_105881 crossref_primary_10_1109_TCBB_2024_3421924 crossref_primary_10_1142_S0219720024500185 crossref_primary_10_1186_s12859_023_05228_w crossref_primary_10_1186_s12864_023_09273_w crossref_primary_10_1109_TCBB_2024_3402248 crossref_primary_10_1186_s12864_023_09363_9 crossref_primary_10_1016_j_heliyon_2023_e17726 crossref_primary_10_3389_fmicb_2022_963704 crossref_primary_10_1038_s41598_024_56583_4 crossref_primary_10_1080_01480545_2024_2364905 crossref_primary_10_1186_s12859_023_05348_3 crossref_primary_10_3389_fgene_2021_749256 crossref_primary_10_1371_journal_pone_0259091 crossref_primary_10_1021_acsomega_3c07923 crossref_primary_10_1371_journal_pone_0267798 crossref_primary_10_1371_journal_pone_0307954 crossref_primary_10_1016_j_heliyon_2023_e20184 crossref_primary_10_1155_2023_8702914 crossref_primary_10_1038_s41598_023_45626_x crossref_primary_10_1038_s41598_024_54837_9 crossref_primary_10_1155_2023_2785436 crossref_primary_10_1109_JBHI_2024_3467101 crossref_primary_10_1097_MD_0000000000040072 crossref_primary_10_3934_mbe_2023534 crossref_primary_10_1371_journal_pone_0261728 crossref_primary_10_3934_mbe_2022322 crossref_primary_10_3934_mbe_2023894 crossref_primary_10_1038_s41598_023_46669_w crossref_primary_10_3389_fgene_2022_895629 crossref_primary_10_1038_s41598_024_52653_9 crossref_primary_10_3389_fmicb_2021_735329 crossref_primary_10_1038_s41598_024_61762_4 crossref_primary_10_1186_s12967_025_06222_x |
| Cites_doi | 10.1093/bioinformatics/btv480 10.1093/bioinformatics/btx672 10.1002/jcb.27057 10.1080/15476286.2018.1457935 10.1016/j.ipl.2011.02.003 10.1038/srep16840 10.1111/jcmm.12681 10.1177/1074248416667600 10.18632/oncotarget.21934 10.3389/fgene.2018.00239 10.1109/TNB.2019.2922214 10.1093/nar/gkx1004 10.1016/j.ygeno.2019.08.001 10.18632/oncotarget.19044 10.1093/bioinformatics/9.1.117 10.1002/0471250953.bi1209s29 10.1111/biom.12866 10.1038/srep13186 10.18632/oncotarget.16880 10.1101/gad.17446611 10.1016/j.biopha.2017.09.113 10.1007/s12013-014-0142-y 10.1093/nar/gku1060 10.3109/07357907.2015.1025407 10.1093/nar/gkv1258 10.3390/molecules25194372 10.1016/j.omtn.2018.09.020 10.1016/j.canlet.2014.12.051 10.18632/oncotarget.11251 10.1371/journal.pone.0053823 10.1093/bioinformatics/bty333 10.1039/c7mb00290d 10.1093/nar/gkm1011 10.1093/nar/gkw443 10.1038/s41580-018-0059-1 10.1089/dna.2015.3187 10.1007/978-1-4939-3378-5_21 10.1007/978-94-007-5590-1_1 |
| ContentType | Journal Article |
| Copyright | International Association of Scientists in the Interdisciplinary Areas 2021 International Association of Scientists in the Interdisciplinary Areas 2021. 2021. International Association of Scientists in the Interdisciplinary Areas. |
| Copyright_xml | – notice: International Association of Scientists in the Interdisciplinary Areas 2021 – notice: International Association of Scientists in the Interdisciplinary Areas 2021. – notice: 2021. International Association of Scientists in the Interdisciplinary Areas. |
| DBID | AAYXX CITATION 7QO 7SC 8FD FR3 JQ2 K9. L7M L~C L~D P64 7X8 |
| DOI | 10.1007/s12539-021-00458-z |
| DatabaseName | CrossRef Biotechnology Research Abstracts Computer and Information Systems Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitle | CrossRef Biotechnology Research Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Health & Medical Complete (Alumni) Engineering Research Database Advanced Technologies Database with Aerospace Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitleList | Biotechnology Research Abstracts MEDLINE - Academic |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Biology |
| EISSN | 1867-1462 |
| EndPage | 545 |
| ExternalDocumentID | 10_1007_s12539_021_00458_z |
| GrantInformation_xml | – fundername: Key R&D Program of Liaoning Province grantid: 2019JH2/10300041 – fundername: Scientific Research Project from Department of Education of Liaoning Province grantid: LQN201906 – fundername: Shenyang Science and Technology Plan Project grantid: 17-65-7-00; 20-203-6-00 – fundername: National Natural Science Foundation of China grantid: 82003655 funderid: http://dx.doi.org/10.13039/501100001809 |
| GroupedDBID | --- -56 -5G -BR -EM -~C 06C 06D 0R~ 0VY 1N0 29~ 2KG 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 53G 67N 6NX 7X2 7X7 7XC 88E 8CJ 8FE 8FG 8FH 8FI 8FJ 96X AACDK AAHNG AAIAL AAJBT AAJKR AANXM AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH AAZMS ABAKF ABBXA ABDZT ABECU ABFTV ABHQN ABJNI ABJOX ABKCH ABMQK ABQBU ABSXP ABTEG ABTHY ABTKH ABTMW ABUWG ABWNU ABXPI ACAOD ACDTI ACGFS ACHSB ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACPRK ACZOJ ADBBV ADHHG ADHIR ADINQ ADKNI ADRFC ADURQ ADYFF ADZKW AEBTG AEFQL AEGNC AEJHL AEJRE AEMSY AEOHA AEPYU AESKC AEUYN AEVLU AEXYK AFKRA AFLOW AFQWF AFRAH AFWTZ AFZKB AGAYW AGDGC AGJBK AGMZJ AGQEE AGQMX AGRTI AGWZB AGYKE AHAVH AHBYD AHKAY AHMBA AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALFXC ALIPV ALMA_UNASSIGNED_HOLDINGS AMKLP AMXSW AMYLF AMYQR ANMIH AOCGG APEBS ARAPS ASPBG ATCPS AUKKA AVWKF AXYYD AZFZN BA0 BBNVY BENPR BGLVJ BGNMA BHPHI BKSAR BPHCQ BVXVI CAG CCPQU COF D1J D1K DDRTE DNIVK DPUIP EBD EBLON EBS EIOEI EJD EMOBN ESBYG F5P FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FYJPI FYUFA G-Y G-Z GGCAI GGRSB GJIRD GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG6 HLICF HMCUK HMJXF HQYDN HRMNR HZ~ I0C IJ- IKXTQ IWAJR IZIGR I~X J-C JBSCW JCJTX JZLTJ K6- KOV LK5 LK8 LLZTM M0K M1P M4Y M7P M7R NPVJJ NQJWS NU0 O9- O9J P62 PATMY PCBAR PQQKQ PROAC PSQYO PT4 PYCSY Q2X QOR QOS R89 RLLFE ROL RSV S1Z S27 S3A S3B SBL SCL SDH SHX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SV3 T13 TSG U2A U9L UG4 UKHRP UOJIU UTJUX UZXMN VC2 VFIZW W48 WK8 ZMTXR ZOVNA ~A9 AAPKM AAYXX ABBRH ABDBE AFDZB AFOHR AHPBZ ATHPR AYFIA CITATION PHGZM PHGZT 7QO 7SC 8FD ABRTQ FR3 JQ2 K9. L7M L~C L~D P64 7X8 |
| ID | FETCH-LOGICAL-c352t-a19c92fabe5a6c6d18b7218d1691524151935c26d6a58904c4e9ef8d74a6ed373 |
| IEDL.DBID | U2A |
| ISSN | 1913-2751 1867-1462 |
| IngestDate | Wed Jul 30 10:31:11 EDT 2025 Fri Jul 25 18:51:05 EDT 2025 Thu Apr 24 22:52:29 EDT 2025 Tue Jul 01 02:19:19 EDT 2025 Fri Feb 21 02:47:53 EST 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 3 |
| Keywords | Interaction prediction miRNA lncRNA Network distance |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c352t-a19c92fabe5a6c6d18b7218d1691524151935c26d6a58904c4e9ef8d74a6ed373 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-9713-1864 |
| PQID | 2561092890 |
| PQPubID | 326319 |
| PageCount | 11 |
| ParticipantIDs | proquest_miscellaneous_2549222764 proquest_journals_2561092890 crossref_primary_10_1007_s12539_021_00458_z crossref_citationtrail_10_1007_s12539_021_00458_z springer_journals_10_1007_s12539_021_00458_z |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2021-09-01 |
| PublicationDateYYYYMMDD | 2021-09-01 |
| PublicationDate_xml | – month: 09 year: 2021 text: 2021-09-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Singapore |
| PublicationPlace_xml | – name: Singapore – name: Bromont |
| PublicationTitle | Interdisciplinary sciences : computational life sciences |
| PublicationTitleAbbrev | Interdiscip Sci Comput Life Sci |
| PublicationYear | 2021 |
| Publisher | Springer Singapore Springer Nature B.V |
| Publisher_xml | – name: Springer Singapore – name: Springer Nature B.V |
| References | Zhao, Yang, Ren, Ge, Fan (CR23) 2019; 18 Chen (CR36) 2015; 5 Yao, Ma, Xue (CR16) 2015; 359 Liu, Ren, Hu, Zhang, Ai, Zhang, Zhao (CR25) 2017; 8 Ye, Yang, Zhao, Song, Wang, Zheng (CR9) 2014; 70 Li, Wang, Mei, Cao, Yang, Wang, Wen (CR40) 2017; 96 CR13 Chou, Chang, Shrestha (CR30) 2016; 44 Miao, Liu, Zhang, Guo (CR26) 2018; 46 Wallin, Wettergren, Hedman, von Heijne (CR32) 1993; 9 Achawanantakun, Chen, Sun, Zhang (CR3) 2015; 31 Hosseini, Soltani, Tavallaei, Mowla, Tafsiri, Bagheri, Khorshid (CR37) 2018; 10 Yang, Tang, Lu, Shi, Ye, Xu, Zhao (CR5) 2018; 119 Treiber, Treiber, Meister (CR7) 2019; 20 Huang, Chan, You (CR24) 2018; 34 Gao, Wu, Jiang (CR34) 2011; 111 Yang, Wang, Lin, Shao, He, Huang (CR31) 2020; 25 Zhao, Yu, Ming, Hu, Ren, Liu (CR19) 2018; 13 Zhao, Zhang, Hu, Ren, Zhang, Liu (CR17) 2018; 9 Ozen, Karatas, Gulluoglu (CR39) 2015; 33 CR6 Furió-Tarí, Tarazona, Gabaldón, Enright, Conesa (CR15) 2016; 44 Hu, Zhu, Ai, Zhang, Zhao, Zhao, Liu (CR18) 2017; 13 Jalali, Bhartiya, Lalwani, Sivasubbu, Scaria (CR2) 2013; 8 Li, Ponnusamy, Li, Wang, Li (CR14) 2017; 22 Chen (CR8) 2015; 5 Cabili, Trapnell, Goff, Koziol, Tazon-Vega, Regev, Rinn (CR1) 2011; 25 Wu, Guo, Jiang, Li, Li, Chen (CR10) 2015; 19 Chen, Yan, Zhang, You, Huang, Yan (CR33) 2016; 7 He, Liu, Skogerbø (CR29) 2007; 36 Zheng, Jeong, Zhu, Chen, Xia (CR12) 2017; 8 Griffiths-Jones (CR28) 2010; 29 Yang, Zhu, Ibrahim (CR35) 2018; 74 Hu, Zhang, Ai, Zhang, Fan, Zhao, Liu (CR20) 2018; 15 Volders, Verheggen, Menschaert, Vandepoele, Martens, Vandesompele, Mestdagh (CR27) 2015; 43 Shi, Zhang, Wang (CR11) 2017; 8 Li, Tian, Yang, Gong (CR4) 2016; 35 Chen, Xie, Wang, Zhao, You, Liu (CR21) 2018; 34 Jiang, Gao, Liu, Mao (CR38) 2016; 6 Ge, Yang, Gang, Fan, Zhao (CR22) 2020; 112 S Ye (458_CR9) 2014; 70 X Chen (458_CR33) 2016; 7 S Jalali (458_CR2) 2013; 8 R Achawanantakun (458_CR3) 2015; 31 Y-R Miao (458_CR26) 2018; 46 S Griffiths-Jones (458_CR28) 2010; 29 MN Cabili (458_CR1) 2011; 25 H Hu (458_CR20) 2018; 15 458_CR6 H Liu (458_CR25) 2017; 8 Z Yang (458_CR5) 2018; 119 B Zheng (458_CR12) 2017; 8 H Hu (458_CR18) 2017; 13 P-J Volders (458_CR27) 2015; 43 T Treiber (458_CR7) 2019; 20 X Shi (458_CR11) 2017; 8 E Wallin (458_CR32) 1993; 9 SM Hosseini (458_CR37) 2018; 10 Q Zhao (458_CR17) 2018; 9 Q Wu (458_CR10) 2015; 19 H Yang (458_CR35) 2018; 74 M Gao (458_CR34) 2011; 111 M Ozen (458_CR39) 2015; 33 X Chen (458_CR36) 2015; 5 P Furió-Tarí (458_CR15) 2016; 44 Q Zhao (458_CR23) 2019; 18 458_CR13 Y Jiang (458_CR38) 2016; 6 X Chen (458_CR8) 2015; 5 L Li (458_CR40) 2017; 96 S Yang (458_CR31) 2020; 25 Y Yao (458_CR16) 2015; 359 X Chen (458_CR21) 2018; 34 E Ge (458_CR22) 2020; 112 C-H Chou (458_CR30) 2016; 44 N Li (458_CR14) 2017; 22 Y-A Huang (458_CR24) 2018; 34 Q Zhao (458_CR19) 2018; 13 J Li (458_CR4) 2016; 35 S He (458_CR29) 2007; 36 |
| References_xml | – volume: 31 start-page: 3897 issue: 24 year: 2015 end-page: 3905 ident: CR3 article-title: LncRNA-ID: long non-coding RNA IDentification using balanced random forests publication-title: Bioinformatics doi: 10.1093/bioinformatics/btv480 – volume: 34 start-page: 812 issue: 5 year: 2018 end-page: 819 ident: CR24 article-title: Constructing prediction models from expression profiles for large scale lncRNA–miRNA interaction profiling publication-title: Bioinformatics doi: 10.1093/bioinformatics/btx672 – volume: 119 start-page: 8432 issue: 10 year: 2018 end-page: 8440 ident: CR5 article-title: Long non-coding RNA reprogramming (lncRNA-ROR) regulates cell apoptosis and autophagy in chondrocytes publication-title: J Cell Biochem doi: 10.1002/jcb.27057 – volume: 15 start-page: 797 issue: 6 year: 2018 end-page: 806 ident: CR20 article-title: HLPI-ensemble: prediction of human lncRNA–protein interactions based on ensemble strategy publication-title: RNA Biol doi: 10.1080/15476286.2018.1457935 – volume: 111 start-page: 440 issue: 9 year: 2011 end-page: 446 ident: CR34 article-title: Userrank for item-based collaborative filtering recommendation publication-title: Inf Process Lett doi: 10.1016/j.ipl.2011.02.003 – volume: 5 start-page: 16840 year: 2015 ident: CR36 article-title: KATZLDA: KATZ measure for the lncRNA-disease association prediction publication-title: Sci Rep doi: 10.1038/srep16840 – volume: 19 start-page: 2874 issue: 12 year: 2015 end-page: 2887 ident: CR10 article-title: Analysis of the mi RNA–mRNA–lnc RNA networks in ER+ and ER− breast cancer cell lines publication-title: J Cell Mol Med doi: 10.1111/jcmm.12681 – volume: 22 start-page: 105 issue: 2 year: 2017 end-page: 111 ident: CR14 article-title: The role of microRNA and LncRNA–MicroRNA interactions in regulating ischemic heart disease publication-title: J Cardiovasc Pharmacol Ther doi: 10.1177/1074248416667600 – volume: 8 start-page: 103975 issue: 61 year: 2017 ident: CR25 article-title: LPI-NRLMF: lncRNA–protein interaction prediction by neighborhood regularized logistic matrix factorization publication-title: Oncotarget doi: 10.18632/oncotarget.21934 – volume: 9 start-page: 239 year: 2018 ident: CR17 article-title: IRWNRLPI: integrating random walk and neighborhood regularized logistic matrix factorization for lncRNA–protein interaction prediction publication-title: Front Genet doi: 10.3389/fgene.2018.00239 – volume: 18 start-page: 578 issue: 4 year: 2019 end-page: 584 ident: CR23 article-title: Integrating bipartite network projection and KATZ measure to identify novel CircRNA-disease associations publication-title: IEEE Trans Nanobiosci doi: 10.1109/TNB.2019.2922214 – volume: 46 start-page: D276 issue: D1 year: 2018 end-page: D280 ident: CR26 article-title: lncRNASNP2: an updated database of functional SNPs and mutations in human and mouse lncRNAs publication-title: Nucleic Acids Res doi: 10.1093/nar/gkx1004 – volume: 112 start-page: 1335 issue: 2 year: 2020 end-page: 1342 ident: CR22 article-title: Predicting human disease-associated circRNAs based on locality-constrained linear coding publication-title: Genomics doi: 10.1016/j.ygeno.2019.08.001 – volume: 8 start-page: 100819 issue: 59 year: 2017 ident: CR12 article-title: miRNA and lncRNA as biomarkers in cholangiocarcinoma (CCA) publication-title: Oncotarget doi: 10.18632/oncotarget.19044 – volume: 9 start-page: 117 issue: 1 year: 1993 end-page: 118 ident: CR32 article-title: Fast Needleman–Wunsch scanning of sequence databanks on a massively parallel computer publication-title: Bioinformatics doi: 10.1093/bioinformatics/9.1.117 – volume: 6 start-page: 1890 issue: 9 year: 2016 end-page: 1905 ident: CR38 article-title: Sorting and biological characteristics analysis for side population cells in human primary hepatocellular carcinoma publication-title: Am J Cancer Res – ident: CR6 – volume: 29 start-page: 12.19.11 issue: 1 year: 2010 end-page: 12.19.10 ident: CR28 article-title: miRBase: microRNA sequences and annotation publication-title: Curr Protoc Bioinform doi: 10.1002/0471250953.bi1209s29 – volume: 74 start-page: 834 issue: 3 year: 2018 end-page: 844 ident: CR35 article-title: MILFM: Multiple index latent factor model based on high-dimensional features publication-title: Biometrics doi: 10.1111/biom.12866 – volume: 5 start-page: 1 issue: 1 year: 2015 end-page: 11 ident: CR8 article-title: Predicting lncRNA-disease associations and constructing lncRNA functional similarity network based on the information of miRNA publication-title: Sci Rep doi: 10.1038/srep13186 – volume: 8 start-page: 58394 issue: 35 year: 2017 ident: CR11 article-title: LncRNA AFAP1-AS1 promotes growth and metastasis of cholangiocarcinoma cells publication-title: Oncotarget doi: 10.18632/oncotarget.16880 – volume: 25 start-page: 1915 issue: 18 year: 2011 end-page: 1927 ident: CR1 article-title: Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses publication-title: Genes Dev doi: 10.1101/gad.17446611 – volume: 96 start-page: 165 year: 2017 end-page: 172 ident: CR40 article-title: lncRNAs HIF1A-AS2 facilitates the up-regulation of HIF-1α by sponging to miR-153-3p, whereby promoting angiogenesis in HUVECs in hypoxia publication-title: Biomed Pharmacother doi: 10.1016/j.biopha.2017.09.113 – volume: 70 start-page: 1849 issue: 3 year: 2014 end-page: 1858 ident: CR9 article-title: Bioinformatics method to predict two regulation mechanism: TF–miRNA–mRNA and lncRNA–miRNA–mRNA in pancreatic cancer publication-title: Cell Biochem Biophys doi: 10.1007/s12013-014-0142-y – volume: 43 start-page: D174 issue: D1 year: 2015 end-page: D180 ident: CR27 article-title: An update on LNCipedia: a database for annotated human lncRNA sequences publication-title: Nucleic Acids Res doi: 10.1093/nar/gku1060 – volume: 33 start-page: 251 issue: 6 year: 2015 end-page: 258 ident: CR39 article-title: Overexpression of miR-145-5p inhibits proliferation of prostate cancer cells and reduces SOX2 expression publication-title: Cancer Investig doi: 10.3109/07357907.2015.1025407 – volume: 44 start-page: D239 issue: D1 year: 2016 end-page: D247 ident: CR30 article-title: miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database publication-title: Nucleic Acids Res doi: 10.1093/nar/gkv1258 – volume: 25 start-page: 4372 issue: 19 year: 2020 ident: CR31 article-title: LncMirNet: predicting LncRNA–miRNA interaction based on deep learning of ribonucleic acid sequences publication-title: Molecules doi: 10.3390/molecules25194372 – volume: 13 start-page: 464 year: 2018 end-page: 471 ident: CR19 article-title: The bipartite network projection-recommended algorithm for predicting long non-coding RNA–protein interactions publication-title: Mol Ther Nucleic Acids doi: 10.1016/j.omtn.2018.09.020 – volume: 359 start-page: 75 issue: 1 year: 2015 end-page: 86 ident: CR16 article-title: Knockdown of long non-coding RNA XIST exerts tumor-suppressive functions in human glioblastoma stem cells by up-regulating miR-152 publication-title: Cancer Lett doi: 10.1016/j.canlet.2014.12.051 – volume: 7 start-page: 65257 issue: 40 year: 2016 ident: CR33 article-title: HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction publication-title: Oncotarget doi: 10.18632/oncotarget.11251 – volume: 8 issue: 2 year: 2013 ident: CR2 article-title: Systematic transcriptome wide analysis of lncRNA–miRNA interactions publication-title: PLoS One doi: 10.1371/journal.pone.0053823 – ident: CR13 – volume: 34 start-page: 3178 issue: 18 year: 2018 end-page: 3186 ident: CR21 article-title: BNPMDA: bipartite network projection for MiRNA–disease association prediction publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty333 – volume: 13 start-page: 1781 issue: 9 year: 2017 end-page: 1787 ident: CR18 article-title: LPI-ETSLP: lncRNA–protein interaction prediction using eigenvalue transformation-based semi-supervised link prediction publication-title: Mol BioSyst doi: 10.1039/c7mb00290d – volume: 10 start-page: 98 issue: 2 year: 2018 end-page: 104 ident: CR37 article-title: Clinically significant dysregulation of hsa-miR-30d-5p and hsa-let-7b expression in patients with surgically resected non-small cell lung cancer publication-title: Avicenna J. Med. Biotechnol. – volume: 36 start-page: D170 issue: suppl_1 year: 2007 end-page: D172 ident: CR29 article-title: NONCODE v2. 0: decoding the non-coding publication-title: Nucleic Acids Res doi: 10.1093/nar/gkm1011 – volume: 44 start-page: W176 issue: W1 year: 2016 end-page: W180 ident: CR15 article-title: spongeScan: a web for detecting microRNA binding elements in lncRNA sequences publication-title: Nucleic Acids Res doi: 10.1093/nar/gkw443 – volume: 20 start-page: 5 issue: 1 year: 2019 end-page: 20 ident: CR7 article-title: Regulation of microRNA biogenesis and its crosstalk with other cellular pathways publication-title: Nat Rev Mol Cell Biol doi: 10.1038/s41580-018-0059-1 – volume: 35 start-page: 459 issue: 9 year: 2016 end-page: 470 ident: CR4 article-title: Long noncoding RNAs regulate cell growth, proliferation, and apoptosis publication-title: DNA Cell Biol doi: 10.1089/dna.2015.3187 – volume: 44 start-page: D239 issue: D1 year: 2016 ident: 458_CR30 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkv1258 – volume: 34 start-page: 3178 issue: 18 year: 2018 ident: 458_CR21 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty333 – volume: 25 start-page: 4372 issue: 19 year: 2020 ident: 458_CR31 publication-title: Molecules doi: 10.3390/molecules25194372 – volume: 31 start-page: 3897 issue: 24 year: 2015 ident: 458_CR3 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btv480 – volume: 20 start-page: 5 issue: 1 year: 2019 ident: 458_CR7 publication-title: Nat Rev Mol Cell Biol doi: 10.1038/s41580-018-0059-1 – volume: 19 start-page: 2874 issue: 12 year: 2015 ident: 458_CR10 publication-title: J Cell Mol Med doi: 10.1111/jcmm.12681 – volume: 22 start-page: 105 issue: 2 year: 2017 ident: 458_CR14 publication-title: J Cardiovasc Pharmacol Ther doi: 10.1177/1074248416667600 – volume: 112 start-page: 1335 issue: 2 year: 2020 ident: 458_CR22 publication-title: Genomics doi: 10.1016/j.ygeno.2019.08.001 – volume: 74 start-page: 834 issue: 3 year: 2018 ident: 458_CR35 publication-title: Biometrics doi: 10.1111/biom.12866 – volume: 8 issue: 2 year: 2013 ident: 458_CR2 publication-title: PLoS One doi: 10.1371/journal.pone.0053823 – ident: 458_CR13 doi: 10.1007/978-1-4939-3378-5_21 – volume: 5 start-page: 16840 year: 2015 ident: 458_CR36 publication-title: Sci Rep doi: 10.1038/srep16840 – volume: 8 start-page: 58394 issue: 35 year: 2017 ident: 458_CR11 publication-title: Oncotarget doi: 10.18632/oncotarget.16880 – volume: 34 start-page: 812 issue: 5 year: 2018 ident: 458_CR24 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btx672 – volume: 8 start-page: 103975 issue: 61 year: 2017 ident: 458_CR25 publication-title: Oncotarget doi: 10.18632/oncotarget.21934 – volume: 13 start-page: 1781 issue: 9 year: 2017 ident: 458_CR18 publication-title: Mol BioSyst doi: 10.1039/c7mb00290d – volume: 25 start-page: 1915 issue: 18 year: 2011 ident: 458_CR1 publication-title: Genes Dev doi: 10.1101/gad.17446611 – volume: 359 start-page: 75 issue: 1 year: 2015 ident: 458_CR16 publication-title: Cancer Lett doi: 10.1016/j.canlet.2014.12.051 – volume: 13 start-page: 464 year: 2018 ident: 458_CR19 publication-title: Mol Ther Nucleic Acids doi: 10.1016/j.omtn.2018.09.020 – volume: 7 start-page: 65257 issue: 40 year: 2016 ident: 458_CR33 publication-title: Oncotarget doi: 10.18632/oncotarget.11251 – volume: 111 start-page: 440 issue: 9 year: 2011 ident: 458_CR34 publication-title: Inf Process Lett doi: 10.1016/j.ipl.2011.02.003 – volume: 18 start-page: 578 issue: 4 year: 2019 ident: 458_CR23 publication-title: IEEE Trans Nanobiosci doi: 10.1109/TNB.2019.2922214 – volume: 29 start-page: 12.19.11 issue: 1 year: 2010 ident: 458_CR28 publication-title: Curr Protoc Bioinform doi: 10.1002/0471250953.bi1209s29 – volume: 119 start-page: 8432 issue: 10 year: 2018 ident: 458_CR5 publication-title: J Cell Biochem doi: 10.1002/jcb.27057 – volume: 9 start-page: 239 year: 2018 ident: 458_CR17 publication-title: Front Genet doi: 10.3389/fgene.2018.00239 – volume: 10 start-page: 98 issue: 2 year: 2018 ident: 458_CR37 publication-title: Avicenna J. Med. Biotechnol. – volume: 35 start-page: 459 issue: 9 year: 2016 ident: 458_CR4 publication-title: DNA Cell Biol doi: 10.1089/dna.2015.3187 – volume: 36 start-page: D170 issue: suppl_1 year: 2007 ident: 458_CR29 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkm1011 – volume: 70 start-page: 1849 issue: 3 year: 2014 ident: 458_CR9 publication-title: Cell Biochem Biophys doi: 10.1007/s12013-014-0142-y – volume: 43 start-page: D174 issue: D1 year: 2015 ident: 458_CR27 publication-title: Nucleic Acids Res doi: 10.1093/nar/gku1060 – volume: 9 start-page: 117 issue: 1 year: 1993 ident: 458_CR32 publication-title: Bioinformatics doi: 10.1093/bioinformatics/9.1.117 – volume: 44 start-page: W176 issue: W1 year: 2016 ident: 458_CR15 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkw443 – volume: 6 start-page: 1890 issue: 9 year: 2016 ident: 458_CR38 publication-title: Am J Cancer Res – volume: 33 start-page: 251 issue: 6 year: 2015 ident: 458_CR39 publication-title: Cancer Investig doi: 10.3109/07357907.2015.1025407 – volume: 8 start-page: 100819 issue: 59 year: 2017 ident: 458_CR12 publication-title: Oncotarget doi: 10.18632/oncotarget.19044 – volume: 5 start-page: 1 issue: 1 year: 2015 ident: 458_CR8 publication-title: Sci Rep doi: 10.1038/srep13186 – ident: 458_CR6 doi: 10.1007/978-94-007-5590-1_1 – volume: 96 start-page: 165 year: 2017 ident: 458_CR40 publication-title: Biomed Pharmacother doi: 10.1016/j.biopha.2017.09.113 – volume: 15 start-page: 797 issue: 6 year: 2018 ident: 458_CR20 publication-title: RNA Biol doi: 10.1080/15476286.2018.1457935 – volume: 46 start-page: D276 issue: D1 year: 2018 ident: 458_CR26 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkx1004 |
| SSID | ssj0064830 |
| Score | 2.5671856 |
| Snippet | LncRNA–miRNA interactions contribute to the regulation of therapeutic targets and diagnostic biomarkers in multifarious human diseases. However, it remains... LncRNA-miRNA interactions contribute to the regulation of therapeutic targets and diagnostic biomarkers in multifarious human diseases. However, it remains... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 535 |
| SubjectTerms | Algorithms Biomarkers Biomedical and Life Sciences Case studies Computational Biology/Bioinformatics Computational Science and Engineering Computer Appl. in Life Sciences Computer applications Health Sciences Life Sciences Mathematical and Computational Physics Medicine miRNA Original Research Article Predictions Similarity Source code Statistics for Life Sciences Theoretical Theoretical and Computational Chemistry Therapeutic targets |
| Title | Using Network Distance Analysis to Predict lncRNA–miRNA Interactions |
| URI | https://link.springer.com/article/10.1007/s12539-021-00458-z https://www.proquest.com/docview/2561092890 https://www.proquest.com/docview/2549222764 |
| Volume | 13 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEB58oHgRrYr1RQRvGrCbxybHolZRWkQs6GlJdrMg1K2024Oe_A_-Q3-JmX1YFBU87WEnWZiZZL7ZeQEcMG6csAbdEh1Sbi2jVjBHU-acSmzi0hALhbs9edHnl3firioKG9fZ7nVIsripp8VugWCaYkoB4hBFX2ZhXqDv7rW4H7Tr-1dyVUwY8Y4Io0EoWlWpzM97fDVHU4z5LSxaWJvOCixXMJG0S7muwozLGrBQDo58bsBitwqJr0GnCPqTXpnOTU4RD3pBkrrbCMmH5HqE5DkZZPFNr_3--vb44J-k-BtYFjaM16HfObs9uaDVcAQae8yUU9PSsQ5SY50wMpZJS1nvzKkEm98INMsemYk4kIk0QuljHnOnXaqSkBvpEhayDZjLhpnbBKKtkNqoYyZYzJPUuxiOW7_QOuMdFKOa0Kp5FMVV53AcYDGIpj2Pka-R52tU8DV6acLh55qnsm_Gn9Q7Neuj6gyNowChncZAaBP2P1977ceQhsnccII0XGM1r-RNOKpFNt3i9y9u_Y98G5aCQmswuWwH5vLRxO16NJLbPZhvn99fne0VSvgBK5rXPg |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1ZS8QwEB488HgRT1zPCL5pwG2OJo-LuqzHLiIu-FaSNgVh7YpbH_TJ_-A_9JeY6eGiqOBTHzpJYWaS-aZzAewzbpywBt0SHVJuLaNWMEdT5pxKbOLSEAuFuz3Z6fPzW3FbFYWN6mz3OiRZ3NTjYrdAME0xpQBxiKIvkzDtwYDCRK5-0KrvX8lVMWHEOyKMBqFoVqUyP-_x1RyNMea3sGhhbdqLsFDBRNIq5boEEy5bhplycOTzMsx2q5D4CrSLoD_plenc5ATxoBckqbuNkHxIrh6RPCeDLL7utd5f3-7v_JMUfwPLwobRKvTbpzfHHVoNR6Cxx0w5NU0d6yA11gkjY5k0lfXOnEqw-Y1As-yRmYgDmUgjlD7iMXfapSoJuZEuYSFbg6lsmLl1INoKqY06YoLFPEm9i-G49QutM95BMaoBzZpHUVx1DscBFoNo3PMY-Rp5vkYFX6OXBhx8rnko-2b8Sb1Vsz6qztAoChDaaQyENmDv87XXfgxpmMwNn5CGa6zmlbwBh7XIxlv8_sWN_5HvwlznpnsZXZ71LjZhPig0CBPNtmAqf3xy2x6Z5HanUMQPs9DYnQ |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT9wwEB4VKlAvVctDXUpbI3EDCxI_Yh9XwIoWWCHESnuL7NiRkLbZ1RIO5cR_4B_yS_DksQuIInHKIWNHmvHjm8x8MwDbjBsvrEG3RCeUW8uoFczTnHmvnHU-T5AofNaXxwP-ZyiGT1j8VbZ7G5KsOQ1Ypako9yYu35sT32LBNMX0AsQkit4uwMdwHEe4rgdxtz2LJVdVt5HglDAaJyJqaDOvz_H8aprjzRch0urm6X2Bzw1kJN3axl_hgy9WYKluIvlvBZbPmvD4KvSqBADSr1O7ySFiw2BU0lYeIeWYnE9RvCSjIrvodx_u7v9ehSep_gzWJIfrNRj0ji4PjmnTKIFmAT-V1EQ603FurBdGZtJFygbHTjkshCPwig4oTWSxdNIIpfd5xr32uXIJN9I7lrB1WCzGhf8GRFshtVH7TLCMuzy4G57bMNB6E5wVozoQtTpKs6aKODazGKXz-seo1zToNa30mt52YGc2ZlLX0HhTerNVfdrsp-s0RpinMSjaga3Z67ATMLxhCj--QRmukdkreQd2W5PNp_j_FzfeJ_4Lls8Pe-np7_7Jd_gUVwsIc842YbGc3vgfAaSU9me1Dh8BaIzc2Q |
| 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=Using+Network+Distance+Analysis+to+Predict+lncRNA-miRNA+Interactions&rft.jtitle=Interdisciplinary+sciences+%3A+computational+life+sciences&rft.au=Zhang%2C+Li&rft.au=Yang%2C+Pengyu&rft.au=Feng%2C+Huawei&rft.au=Zhao%2C+Qi&rft.date=2021-09-01&rft.issn=1867-1462&rft.eissn=1867-1462&rft.volume=13&rft.issue=3&rft.spage=535&rft_id=info:doi/10.1007%2Fs12539-021-00458-z&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1913-2751&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1913-2751&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1913-2751&client=summon |