Application of network link prediction in drug discovery
Background Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there...
Gespeichert in:
| Veröffentlicht in: | BMC bioinformatics Jg. 22; H. 1; S. 187 - 21 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
London
BioMed Central
12.04.2021
Springer Nature B.V BMC |
| Schlagworte: | |
| ISSN: | 1471-2105, 1471-2105 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Background
Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug–drug, drug–disease, and protein–protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches.
Results
We applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that
Prone
,
ACT
and
L
R
W
5
are the top 3 best performers on all five datasets.
Conclusions
This work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug–drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks. |
|---|---|
| AbstractList | Background Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug–drug, drug–disease, and protein–protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches. Results We applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone, ACT and \(LRW_5\) are the top 3 best performers on all five datasets. Conclusions This work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug–drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks. Background Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug–drug, drug–disease, and protein–protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches. Results We applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone , ACT and L R W 5 are the top 3 best performers on all five datasets. Conclusions This work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug–drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks. Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug-drug, drug-disease, and protein-protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches. We applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone, ACT and [Formula: see text] are the top 3 best performers on all five datasets. This work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug-drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks. Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug-drug, drug-disease, and protein-protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches.BACKGROUNDTechnological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug-drug, drug-disease, and protein-protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches.We applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone, ACT and [Formula: see text] are the top 3 best performers on all five datasets.RESULTSWe applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone, ACT and [Formula: see text] are the top 3 best performers on all five datasets.This work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug-drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks.CONCLUSIONSThis work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug-drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks. Abstract Background Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug–drug, drug–disease, and protein–protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches. Results We applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone, ACT and $$LRW_5$$ L R W 5 are the top 3 best performers on all five datasets. Conclusions This work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug–drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks. |
| ArticleNumber | 187 |
| Author | Dong, Shi Cai, Shi-Min Chen, Bolun Abbas, Khushnood Yu, Laihang Abbasi, Alireza Niu, Ling Hasan, Qambar |
| Author_xml | – sequence: 1 givenname: Khushnood orcidid: 0000-0002-0096-3179 surname: Abbas fullname: Abbas, Khushnood email: abbas@cigit.ac.cn organization: School of Computer Science and Technology, Zhoukou Normal University, School of Engineering and Information Technology, University of New South Wales – sequence: 2 givenname: Alireza surname: Abbasi fullname: Abbasi, Alireza organization: School of Engineering and Information Technology, University of New South Wales – sequence: 3 givenname: Shi surname: Dong fullname: Dong, Shi organization: School of Computer Science and Technology, Zhoukou Normal University – sequence: 4 givenname: Ling surname: Niu fullname: Niu, Ling organization: School of Computer Science and Technology, Zhoukou Normal University – sequence: 5 givenname: Laihang surname: Yu fullname: Yu, Laihang organization: School of Computer Science and Technology, Zhoukou Normal University – sequence: 6 givenname: Bolun surname: Chen fullname: Chen, Bolun organization: College of Computer and Software Engineering, Huaiyin Institute of Technology – sequence: 7 givenname: Shi-Min surname: Cai fullname: Cai, Shi-Min organization: School of Computer Science and Engineering, University of Electronic Science and Technology of China – sequence: 8 givenname: Qambar surname: Hasan fullname: Hasan, Qambar organization: Centre for Cellular and Molecular Biology, School of Life and Environmental Science, Deakin University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33845763$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kktv3CAUhVGVqnm0f6CLylI33Tjh8rBhUymK-ogUKZtmjTDgKRMPuGAnmn9fMs57kRWIe86no8s5RHshBofQZ8DHAKI5yUAElzUmUGOGBam379ABsBZqApjvPbvvo8Oc1xhDKzD_gPYpFYy3DT1A4nQcB2_05GOoYl8FN93GdF0NPlxXY3LWm93Ih8qmeVVZn028cWn7Eb3v9ZDdp_vzCF39_PHn7Hd9cfnr_Oz0ojac4aluOCctkb3krjFMYAEN4bJvGe562-IOJHCH254Z3fVAQZoWeEetlVYIwQw9QucL10a9VmPyG522Kmqvdg8xrZROkzeDU2AxaEktlZowLjspml4QZ7QWElgjCuv7whrnbuOscWFKengBfTkJ_q9axRslMCNS8AL4dg9I8d_s8qQ2ZR9uGHRwcc6KcCCUMQy0SL--kq7jnEJZ1Z2KkYbghhTVl-eJHqM8fFARiEVgUsw5uV4ZP-1-qwT0gwKs7rqgli6o0gW164LaFit5ZX2gv2miiykXcVi59BT7Ddd_vBvFYA |
| CitedBy_id | crossref_primary_10_1007_s12530_025_09690_0 crossref_primary_10_1371_journal_pcbi_1009909 crossref_primary_10_1371_journal_pone_0315507 crossref_primary_10_1038_s41467_025_63494_z crossref_primary_10_1016_j_ipm_2023_103292 crossref_primary_10_1016_j_engappai_2024_108846 crossref_primary_10_3389_fphar_2023_1113007 crossref_primary_10_3390_electronics11233866 crossref_primary_10_1007_s41109_025_00705_y crossref_primary_10_1016_j_jbi_2023_104373 crossref_primary_10_1093_bib_bbac481 crossref_primary_10_1016_j_eswa_2023_121716 crossref_primary_10_1007_s11042_024_20123_z crossref_primary_10_1016_j_chroma_2023_464109 crossref_primary_10_3389_fcell_2021_732370 crossref_primary_10_3390_su17083607 crossref_primary_10_1016_j_eswa_2024_126284 crossref_primary_10_1109_TCSS_2022_3200526 crossref_primary_10_3390_sym15061182 crossref_primary_10_1093_bioadv_vbae097 crossref_primary_10_1016_j_procs_2025_03_057 crossref_primary_10_3390_systems12070257 crossref_primary_10_3390_ijms26178407 crossref_primary_10_1016_j_artmed_2023_102687 crossref_primary_10_1016_j_ins_2022_07_031 crossref_primary_10_3390_bdcc6020066 crossref_primary_10_1002_qub2_38 crossref_primary_10_1016_j_jocs_2024_102513 crossref_primary_10_1287_ijoc_2022_0130 crossref_primary_10_1002_adma_202209503 crossref_primary_10_1016_j_neucom_2024_127820 crossref_primary_10_1038_s41598_022_22843_4 crossref_primary_10_1007_s11633_024_1519_z crossref_primary_10_3390_e25030422 crossref_primary_10_1146_annurev_biodatasci_110123_025333 crossref_primary_10_3390_app13179685 crossref_primary_10_1016_j_jii_2025_100802 crossref_primary_10_1109_TNSE_2025_3566227 crossref_primary_10_3390_genes13030473 crossref_primary_10_1109_ACCESS_2023_3260652 crossref_primary_10_2196_46777 crossref_primary_10_1007_s11280_022_01076_5 |
| Cites_doi | 10.1111/imm.12195 10.1142/9789814366496_0038 10.9758/cpn.2015.13.2.212 10.1007/s10822-016-9938-8 10.1186/s12859-016-1005-x 10.1038/msb.2009.98 10.1186/1471-2105-14-S16-S3 10.1093/bib/bbr028 10.1103/PhysRevE.80.017101 10.1038/nrd3078 10.1038/nrd.2016.3 10.1016/j.physa.2010.11.027 10.1007/978-3-319-66179-7_21 10.1007/978-1-4419-8462-3_9 10.1016/j.pharmthera.2013.01.016 10.1093/nar/gkx1037 10.1007/s11432-014-5237-y 10.1016/j.media.2018.06.001 10.1103/PhysRevE.73.026120 10.1038/msb.2008.60 10.1126/science.286.5439.509 10.1093/nar/gkw838 10.1038/srep07160 10.3390/molecules24091714 10.1103/PhysRevE.80.046122 10.1007/BF01164627 10.1039/c3mb70554d 10.1142/9789814583220_0014 10.1186/1752-0509-7-S5-S6 10.1016/S0169-7552(98)00110-X 10.1038/nrd1470 10.1038/nbt.1549 10.24963/ijcai.2019/594 10.1145/2939672.2939754 10.1126/science.1073374 10.1093/bioinformatics/bty294 10.1109/BIBM.2012.6392722 10.1039/c2mb00002d 10.1093/nar/gkm862 10.1103/PhysRevLett.120.145301 10.1145/2783258.2783307 10.1145/2736277.2741093 10.1209/0295-5075/89/58007 10.1016/S0378-8733(03)00009-1 10.1145/2623330.2623732 10.1089/cmb.2014.0158 10.1158/0008-5472.CAN-11-2333 10.1007/978-3-319-66182-7_54 10.1140/epjb/e2009-00335-8 10.1145/775047.775126 10.1038/nrd3681 10.1126/science.1158140 10.1002/wsbm.144 10.1209/0295-5075/96/48007 10.1371/journal.pcbi.1002503 10.1103/PhysRevE.64.025102 10.1016/j.compbiolchem.2019.03.016 10.1080/01621459.1937.10503522 10.1145/2939672.2939751 10.7150/ijbs.24612 10.1186/1471-2105-15-267 10.1145/2806416.2806512 10.1021/acs.jcim.6b00601 10.1007/978-3-319-34129-3_47 10.1109/TKDE.2007.46 10.1007/BF02289026 10.1038/nrd1468 10.1145/3159652.3159706 10.1038/s41467-017-00680-8 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2021 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2021 – notice: 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7QO 7SC 7X7 7XB 88E 8AL 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ JQ2 K7- K9. L7M LK8 L~C L~D M0N M0S M1P M7P P5Z P62 P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA |
| DOI | 10.1186/s12859-021-04082-y |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Biotechnology Research Abstracts Computer and Information Systems Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC Biological Science Collection ProQuest Central Technology collection Natural Science Collection ProQuest One Community College ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace Biological Sciences Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database ProQuest Health & Medical Collection Medical Database Biological Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies Database with Aerospace ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Medical Library ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database MEDLINE MEDLINE - Academic |
| 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: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1471-2105 |
| EndPage | 21 |
| ExternalDocumentID | oai_doaj_org_article_1d01a93d39a2459b986f82ecaa891468 PMC8042985 33845763 10_1186_s12859_021_04082_y |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Zhoukou Normal University grantid: ZKNUC2018019 funderid: http://dx.doi.org/10.13039/100012901 – fundername: http://dx.doi.org/10.13039/501100011447 grantid: 202102210379 funderid: http://dx.doi.org/10.13039/501100011447 – fundername: http://dx.doi.org/10.13039/501100011447 grantid: 202102210379 – fundername: Zhoukou Normal University grantid: ZKNUC2018019 – fundername: ; grantid: ZKNUC2018019 – fundername: ; grantid: 202102210379 |
| GroupedDBID | --- 0R~ 23N 2WC 53G 5VS 6J9 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AAKPC AASML ABDBF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADMLS ADUKV AEAQA AENEX AEUYN AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ARAPS AZQEC BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CS3 DIK DU5 DWQXO E3Z EAD EAP EAS EBD EBLON EBS EMB EMK EMOBN ESX F5P FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HMCUK HYE IAO ICD IHR INH INR ISR ITC K6V K7- KQ8 LK8 M1P M48 M7P MK~ ML0 M~E O5R O5S OK1 OVT P2P P62 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RBZ RNS ROL RPM RSV SBL SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XH6 XSB AAYXX AFFHD CITATION ALIPV CGR CUY CVF ECM EIF NPM 3V. 7QO 7SC 7XB 8AL 8FD 8FK FR3 JQ2 K9. L7M L~C L~D M0N P64 PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c540t-6552729f95e6c480816259f740bfd70b1915e07f4cabf1319c715b3dd9d8884c3 |
| IEDL.DBID | M7P |
| ISICitedReferencesCount | 67 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000639869800004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1471-2105 |
| IngestDate | Fri Oct 03 12:50:38 EDT 2025 Tue Nov 04 01:50:16 EST 2025 Thu Oct 02 09:44:37 EDT 2025 Mon Oct 06 18:28:01 EDT 2025 Mon Jul 21 05:53:50 EDT 2025 Tue Nov 18 22:20:04 EST 2025 Sat Nov 29 05:40:09 EST 2025 Sat Sep 06 07:27:36 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Poly-pharmacy Drug-target prediction Network link prediction Poly-pharmacy side effects prediction Data-driven drug discovery |
| Language | English |
| License | Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c540t-6552729f95e6c480816259f740bfd70b1915e07f4cabf1319c715b3dd9d8884c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-0096-3179 |
| OpenAccessLink | https://www.proquest.com/docview/2514262062?pq-origsite=%requestingapplication% |
| PMID | 33845763 |
| PQID | 2514262062 |
| PQPubID | 44065 |
| PageCount | 21 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_1d01a93d39a2459b986f82ecaa891468 pubmedcentral_primary_oai_pubmedcentral_nih_gov_8042985 proquest_miscellaneous_2512344013 proquest_journals_2514262062 pubmed_primary_33845763 crossref_citationtrail_10_1186_s12859_021_04082_y crossref_primary_10_1186_s12859_021_04082_y springer_journals_10_1186_s12859_021_04082_y |
| PublicationCentury | 2000 |
| PublicationDate | 2021-04-12 |
| PublicationDateYYYYMMDD | 2021-04-12 |
| PublicationDate_xml | – month: 04 year: 2021 text: 2021-04-12 day: 12 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | BMC bioinformatics |
| PublicationTitleAbbrev | BMC Bioinformatics |
| PublicationTitleAlternate | BMC Bioinformatics |
| PublicationYear | 2021 |
| Publisher | BioMed Central Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: Springer Nature B.V – name: BMC |
| References | L Lü (4082_CR64) 2009; 80 M Žitnik (4082_CR12) 2015; 22 4082_CR8 F Tan (4082_CR43) 2014; 10 F Fouss (4082_CR68) 2007; 19 S Parisot (4082_CR35) 2018; 48 SM Paul (4082_CR5) 2010; 9 H Bisgin (4082_CR39) 2014; 15 DJ Klein (4082_CR67) 1993; 12 TT Ashburn (4082_CR15) 2004; 3 D Vidović (4082_CR37) 2014; 5 P Csermely (4082_CR1) 2013; 138 4082_CR33 4082_CR77 4082_CR34 T Zhou (4082_CR63) 2009; 71 4082_CR78 4082_CR79 4082_CR36 P Jaccard (4082_CR57) 1901; 37 F Cheng (4082_CR20) 2012; 8 LA Adamic (4082_CR62) 2003; 25 Y Luo (4082_CR24) 2017; 8 J Loscalzo (4082_CR2) 2011; 3 4082_CR71 S-M Wang (4082_CR16) 2015; 13 K Yang (4082_CR23) 2008; 4 4082_CR74 4082_CR75 4082_CR76 J Li (4082_CR50) 2013; 14 L Katz (4082_CR66) 1953; 18 M Campillos (4082_CR22) 2008; 321 JW Scannell (4082_CR3) 2012; 11 W Liu (4082_CR70) 2010; 89 4082_CR26 T Xie (4082_CR32) 2018; 120 M Zitnik (4082_CR48) 2018; 34 4082_CR27 4082_CR28 S Günther (4082_CR86) 2007; 36 4082_CR29 D Sun (4082_CR72) 2009; 80 AP Davis (4082_CR84) 2017; 45 DS Wishart (4082_CR85) 2017; 46 4082_CR6 H Huang (4082_CR21) 2014; 4 4082_CR7 P Wang (4082_CR13) 2015; 58 4082_CR65 Z Liu (4082_CR73) 2011; 96 L Lü (4082_CR60) 2011; 390 G Jin (4082_CR52) 2012; 72 H Xue (4082_CR9) 2018; 14 L Zhou (4082_CR10) 2019; 24 ME Newman (4082_CR55) 2001; 64 4082_CR56 EA Leicht (4082_CR11) 2006; 73 4082_CR17 4082_CR18 TA Sorensen (4082_CR58) 1948; 5 I Kola (4082_CR4) 2004; 3 X Chen (4082_CR19) 2012; 8 E Ravasz (4082_CR59) 2002; 297 M Friedman (4082_CR87) 1937; 32 4082_CR53 SJ Hebbring (4082_CR38) 2014; 141 4082_CR54 CW Coley (4082_CR30) 2017; 57 SJ Swamidass (4082_CR42) 2011; 12 S Kearnes (4082_CR31) 2016; 30 J You (4082_CR14) 2019; 80 4082_CR44 A-L Barabási (4082_CR61) 1999; 286 RM Webster (4082_CR45) 2016; 15 4082_CR47 S Brin (4082_CR69) 1998; 30 4082_CR49 C Wu (4082_CR51) 2013; 7 4082_CR80 4082_CR81 4082_CR82 4082_CR83 G Fu (4082_CR25) 2016; 17 4082_CR41 J Lehár (4082_CR46) 2009; 27 M Kuhn (4082_CR40) 2010; 6 |
| References_xml | – volume: 141 start-page: 157 issue: 2 year: 2014 ident: 4082_CR38 publication-title: Immunology doi: 10.1111/imm.12195 – ident: 4082_CR41 doi: 10.1142/9789814366496_0038 – volume: 13 start-page: 212 issue: 2 year: 2015 ident: 4082_CR16 publication-title: Clin Psychopharmacol Neurosci doi: 10.9758/cpn.2015.13.2.212 – volume: 30 start-page: 595 issue: 8 year: 2016 ident: 4082_CR31 publication-title: J Comput Aided Mol Des doi: 10.1007/s10822-016-9938-8 – volume: 17 start-page: 160 issue: 1 year: 2016 ident: 4082_CR25 publication-title: BMC Bioinform doi: 10.1186/s12859-016-1005-x – volume: 6 start-page: 343 issue: 1 year: 2010 ident: 4082_CR40 publication-title: Mol Syst Biol doi: 10.1038/msb.2009.98 – volume: 14 start-page: 3 issue: 16 year: 2013 ident: 4082_CR50 publication-title: BMC Bioinform doi: 10.1186/1471-2105-14-S16-S3 – volume: 12 start-page: 327 issue: 4 year: 2011 ident: 4082_CR42 publication-title: Brief Bioinform doi: 10.1093/bib/bbr028 – volume: 80 start-page: 017101 issue: 1 year: 2009 ident: 4082_CR72 publication-title: Phys Rev E doi: 10.1103/PhysRevE.80.017101 – volume: 9 start-page: 203 issue: 3 year: 2010 ident: 4082_CR5 publication-title: Nat Rev Drug Discov doi: 10.1038/nrd3078 – volume: 15 start-page: 81 issue: 2 year: 2016 ident: 4082_CR45 publication-title: Nat Rev Drug Discov doi: 10.1038/nrd.2016.3 – volume: 390 start-page: 1150 issue: 6 year: 2011 ident: 4082_CR60 publication-title: Phys A doi: 10.1016/j.physa.2010.11.027 – ident: 4082_CR8 – ident: 4082_CR28 – ident: 4082_CR34 doi: 10.1007/978-3-319-66179-7_21 – ident: 4082_CR53 – ident: 4082_CR65 doi: 10.1007/978-1-4419-8462-3_9 – volume: 138 start-page: 333 issue: 3 year: 2013 ident: 4082_CR1 publication-title: Pharmacol Ther doi: 10.1016/j.pharmthera.2013.01.016 – volume: 46 start-page: 1074 issue: D1 year: 2017 ident: 4082_CR85 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkx1037 – volume: 58 start-page: 1 issue: 1 year: 2015 ident: 4082_CR13 publication-title: Sci China Inf Sci doi: 10.1007/s11432-014-5237-y – volume: 48 start-page: 117 year: 2018 ident: 4082_CR35 publication-title: Med Image Anal doi: 10.1016/j.media.2018.06.001 – volume: 73 start-page: 026120 issue: 2 year: 2006 ident: 4082_CR11 publication-title: Phys Rev E doi: 10.1103/PhysRevE.73.026120 – volume: 4 start-page: 228 issue: 1 year: 2008 ident: 4082_CR23 publication-title: Mol Syst Biol. doi: 10.1038/msb.2008.60 – volume: 286 start-page: 509 issue: 5439 year: 1999 ident: 4082_CR61 publication-title: Science doi: 10.1126/science.286.5439.509 – volume: 45 start-page: 972 issue: D1 year: 2017 ident: 4082_CR84 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkw838 – volume: 4 start-page: 7160 year: 2014 ident: 4082_CR21 publication-title: Sci Rep doi: 10.1038/srep07160 – volume: 24 start-page: 1714 issue: 9 year: 2019 ident: 4082_CR10 publication-title: Molecules doi: 10.3390/molecules24091714 – volume: 80 start-page: 046122 issue: 4 year: 2009 ident: 4082_CR64 publication-title: Phys Rev E doi: 10.1103/PhysRevE.80.046122 – volume: 12 start-page: 81 issue: 1 year: 1993 ident: 4082_CR67 publication-title: J Math Chem doi: 10.1007/BF01164627 – volume: 10 start-page: 1126 issue: 5 year: 2014 ident: 4082_CR43 publication-title: Mol BioSyst doi: 10.1039/c3mb70554d – ident: 4082_CR47 – ident: 4082_CR44 doi: 10.1142/9789814583220_0014 – volume: 7 start-page: 6 issue: 5 year: 2013 ident: 4082_CR51 publication-title: BMC Syst Biol doi: 10.1186/1752-0509-7-S5-S6 – volume: 30 start-page: 107 issue: 1–7 year: 1998 ident: 4082_CR69 publication-title: Comput Netw ISDN Syst doi: 10.1016/S0169-7552(98)00110-X – volume: 3 start-page: 711 issue: 8 year: 2004 ident: 4082_CR4 publication-title: Nat Rev Drug Discov doi: 10.1038/nrd1470 – volume: 27 start-page: 659 issue: 7 year: 2009 ident: 4082_CR46 publication-title: Nat Biotechnol doi: 10.1038/nbt.1549 – ident: 4082_CR75 doi: 10.24963/ijcai.2019/594 – ident: 4082_CR77 doi: 10.1145/2939672.2939754 – ident: 4082_CR54 – volume: 297 start-page: 1551 issue: 5586 year: 2002 ident: 4082_CR59 publication-title: Science doi: 10.1126/science.1073374 – ident: 4082_CR7 – volume: 34 start-page: 457 issue: 13 year: 2018 ident: 4082_CR48 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty294 – ident: 4082_CR49 doi: 10.1109/BIBM.2012.6392722 – volume: 8 start-page: 1970 issue: 7 year: 2012 ident: 4082_CR19 publication-title: Mol BioSyst doi: 10.1039/c2mb00002d – ident: 4082_CR27 – volume: 36 start-page: 919 issue: suppl–1 year: 2007 ident: 4082_CR86 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkm862 – volume: 120 start-page: 145301 issue: 14 year: 2018 ident: 4082_CR32 publication-title: Phys Rev Lett doi: 10.1103/PhysRevLett.120.145301 – ident: 4082_CR81 doi: 10.1145/2783258.2783307 – ident: 4082_CR79 doi: 10.1145/2736277.2741093 – ident: 4082_CR17 – volume: 5 start-page: 1 year: 1948 ident: 4082_CR58 publication-title: Biol Skar – volume: 89 start-page: 58007 issue: 5 year: 2010 ident: 4082_CR70 publication-title: EPL (Europhys Lett) doi: 10.1209/0295-5075/89/58007 – ident: 4082_CR36 – volume: 25 start-page: 211 issue: 3 year: 2003 ident: 4082_CR62 publication-title: Soc Netw doi: 10.1016/S0378-8733(03)00009-1 – ident: 4082_CR76 doi: 10.1145/2623330.2623732 – volume: 22 start-page: 595 issue: 6 year: 2015 ident: 4082_CR12 publication-title: J Comput Biol doi: 10.1089/cmb.2014.0158 – volume: 72 start-page: 33 issue: 1 year: 2012 ident: 4082_CR52 publication-title: Cancer Res doi: 10.1158/0008-5472.CAN-11-2333 – ident: 4082_CR33 doi: 10.1007/978-3-319-66182-7_54 – volume: 71 start-page: 623 issue: 4 year: 2009 ident: 4082_CR63 publication-title: Eur Phys J B doi: 10.1140/epjb/e2009-00335-8 – ident: 4082_CR71 doi: 10.1145/775047.775126 – ident: 4082_CR78 – volume: 11 start-page: 191 issue: 3 year: 2012 ident: 4082_CR3 publication-title: Nat Rev Drug Discov doi: 10.1038/nrd3681 – ident: 4082_CR6 – volume: 321 start-page: 263 issue: 5886 year: 2008 ident: 4082_CR22 publication-title: Science doi: 10.1126/science.1158140 – volume: 3 start-page: 619 issue: 6 year: 2011 ident: 4082_CR2 publication-title: Wiley Interdiscip Rev Syst Biol Med doi: 10.1002/wsbm.144 – ident: 4082_CR26 – volume: 96 start-page: 48007 issue: 4 year: 2011 ident: 4082_CR73 publication-title: EPL (Europhys Lett) doi: 10.1209/0295-5075/96/48007 – volume: 8 start-page: e1002503 issue: 5 year: 2012 ident: 4082_CR20 publication-title: PLoS Comput Biol. doi: 10.1371/journal.pcbi.1002503 – volume: 64 start-page: 025102 issue: 2 year: 2001 ident: 4082_CR55 publication-title: Phys Rev E doi: 10.1103/PhysRevE.64.025102 – volume: 80 start-page: 90 year: 2019 ident: 4082_CR14 publication-title: Comput Biol Chem doi: 10.1016/j.compbiolchem.2019.03.016 – volume: 32 start-page: 675 issue: 200 year: 1937 ident: 4082_CR87 publication-title: J Am Stat Assoc doi: 10.1080/01621459.1937.10503522 – ident: 4082_CR82 doi: 10.1145/2939672.2939751 – ident: 4082_CR74 – volume: 14 start-page: 1232 issue: 10 year: 2018 ident: 4082_CR9 publication-title: Int J Biol Sci doi: 10.7150/ijbs.24612 – volume: 15 start-page: 267 issue: 1 year: 2014 ident: 4082_CR39 publication-title: BMC Bioinform doi: 10.1186/1471-2105-15-267 – ident: 4082_CR83 doi: 10.1145/2806416.2806512 – volume: 57 start-page: 1757 issue: 8 year: 2017 ident: 4082_CR30 publication-title: J Chem Inf Model doi: 10.1021/acs.jcim.6b00601 – ident: 4082_CR18 doi: 10.1007/978-3-319-34129-3_47 – volume: 37 start-page: 547 year: 1901 ident: 4082_CR57 publication-title: Bull Soc Vaudoise Sci Nat – volume: 19 start-page: 355 issue: 3 year: 2007 ident: 4082_CR68 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2007.46 – ident: 4082_CR56 – ident: 4082_CR29 – volume: 5 start-page: 342 year: 2014 ident: 4082_CR37 publication-title: Front Genet – volume: 18 start-page: 39 issue: 1 year: 1953 ident: 4082_CR66 publication-title: Psychometrika doi: 10.1007/BF02289026 – volume: 3 start-page: 673 issue: 8 year: 2004 ident: 4082_CR15 publication-title: Nat Rev Drug Discov doi: 10.1038/nrd1468 – ident: 4082_CR80 doi: 10.1145/3159652.3159706 – volume: 8 start-page: 1 issue: 1 year: 2017 ident: 4082_CR24 publication-title: Nat Commun doi: 10.1038/s41467-017-00680-8 |
| SSID | ssj0017805 |
| Score | 2.6125975 |
| Snippet | Background
Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful... Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for... Background Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful... Abstract Background Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data... |
| SourceID | doaj pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 187 |
| SubjectTerms | Algorithms Bioinformatics Biomedical and Life Sciences Biomedical data Clinical trials Complex systems Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer applications Data-driven drug discovery Datasets Deep learning Disease Drug Discovery Drug Interactions Drug-target prediction Drugs Gene expression Graph representations Graphical representations Learning algorithms Life Sciences Ligands Machine Learning Machine Learning and Artificial Intelligence in Bioinformatics Microarrays Model testing Network analysis Network link prediction Neural networks Performance evaluation Pharmaceuticals Poly-pharmacy Poly-pharmacy side effects prediction Predictions Protein interaction Proteins R&D Research & development Research Article Therapeutic targets |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwEB2hqki9IAqUhi4oSNwg2thxEvvYVqw4oBUHQL1ZiT_KSihb7Uel_fedsZMt2xZ64bp2pNF4Zv2eZvwG4ANXZWPrps0QrBdIUATLpPOedG8F96ryLMgx_PxaT6fy4kJ9-2PUF_WERXng6LgxszlrVGEL1XBRqlbJykvuTNNIRc-G6N8XUc9Apvr6ASn1D09kZDVeMtJpy6gdIacJy9lm5xoKav0PQcz7nZJ3yqXhFpo8h2c9fExPo9mH8MR1L-BpHCi5eQny9LYenc592sUm75TKtOnVgooyYWnWpXaxvkzpTS71cG5ewY_J5-_nX7J-NkJmEGOtsoqU07jyqnSVEWF8BhIZX4u89bbOW6RhpctrL0zTeoZ5ZmpWtoW1yiLnFaY4gr1u3rljSA233ksEDqWXoqhdiwxRidw6QgvK2QTY4CpteuFwml_xWwcCISsd3avRvTq4V28S-Lj95irKZvxz9xmdwHYnSV6HHzAQdB8I-rFASGA0nJ_u83CpEb0Fyf2KJ_B-u4wZRGWRpnPzddjDC0E8M4HX8bi3liCBF8jIcKXeCYQdU3dXutmvoNIt6aqXZQKfhpC5NevvrnjzP1xxAgc8xnrG-Aj2Vou1ewv75no1Wy7ehUy5AZ3gE2Y priority: 102 providerName: Directory of Open Access Journals – databaseName: Springer LINK dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT9wwEB7xlLgUCoWGR5VK3CAidpzEPkJVxAGtqkIRNyvxA1ZCWZTdrbT_vh7nsVoeldprPFFG45l4Ps34G4BjKtJC50UZuWQ9cQCFkYgba5H3llErMks8HcPddT4Y8Pt78aO9FDbuut27kqT_U_uw5tnZmCDXWoQtBTFOSY5my7DqjjuO4fjz5q6vHSBLf3c95s33Fo4gz9T_Vnr5ukvyRanUn0CXm_-n-xZ8aDPO8LxxkY-wZKptWG9mUM52gJ_PS9jhyIZV0xce4mfC5xrrOH5pWIW6nj6EeI0X2z5nn-DX5ffbb1dRO04hUi4tm0QZkq1RYUVqMsX8xA2HfWzO4tLqPC4dcktNnFumitISF5oqJ2mZaC20g8lMJbuwUo0q8xlCRbW13Bk_tZwluSkdqBQs1gYTDGF0AKSzsFQt1ziOvHiSHnPwTDYWkc4i0ltEzgI46d95bpg2_ip9gRvXSyJLtn8wqh9kG3SS6JgUItGJKChLRSl4Zjk1qii4wCtnARx22y7b0B1Ll_B5lv6MBvC1X3ZBh5WUojKjqZehCUNoGsBe4yW9Jg7zMwfi3Eq-4D8Lqi6uVMNHT-zNMTvgaQCnnRfN1XrfFPv_Jn4AG7RxxIjQQ1iZ1FNzBGvq92Q4rr_4UPoDQyEY9Q priority: 102 providerName: Springer Nature |
| Title | Application of network link prediction in drug discovery |
| URI | https://link.springer.com/article/10.1186/s12859-021-04082-y https://www.ncbi.nlm.nih.gov/pubmed/33845763 https://www.proquest.com/docview/2514262062 https://www.proquest.com/docview/2512344013 https://pubmed.ncbi.nlm.nih.gov/PMC8042985 https://doaj.org/article/1d01a93d39a2459b986f82ecaa891468 |
| Volume | 22 |
| WOSCitedRecordID | wos000639869800004&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: PRVADU databaseName: BioMedCentral customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RBZ dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: DOA dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M~E dateStart: 20000101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: P5Z dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M7P dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: K7- dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: PIMPY dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: Springer LINK customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RSV dateStart: 20001201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB7RFiQuvEsDZRUkbmA1iZ3YPqEWtQIBq6hAtXCxktguK6Fkuw-k_fd4nMdqefTCxVIyjmR7ZuIZz_gbgBeJTAvNi5I4Y506B4XFRBhrEfeWJVZmNvZwDBcf-HgsJhOZdwduiy6tsv8n-h-1bio8Iz9y-7AHT8-S17MrglWjMLraldDYgT1ESaA-dS8fogiI199flBHZ0SJGtDaCSQkR1lkm663NyGP2_83Q_DNf8regqd-Lzu7-7yzuwZ3OCg2PW7G5DzdM_QButXUp1w9BHG_C2mFjw7rNFQ8x2hvO5hjb8aRpHer56jLEq72YCrp-BF_OTj-_eUu6EgukcqbakmQIwJZIK1OTVcxX4XD-kOUsKq3mUem8udRE3LKqKG3s1LXicVpSraV2rjOr6D7s1k1tDiCsEm2tcPZHagWj3JTO0ZQs0gaNDml0AHG_1qrq8MexDMYP5f0QkamWP8rxR3n-qHUAL4dvZi36xrW9T5CFQ09EzvYvmvml6hRRxTqKC0k1lUXCUllKkVmRmKoohMRraAEc9pxTnTov1IZtATwfyE4RMbpS1KZZ-T4JZeiuBvC4lZdhJJQK5hw7R-FbkrQ11G1KPf3uwb4FWgwiDeBVL3ObYf17KZ5cP4uncDtp1YDEySHsLucr8wxuVj-X08V8BDt8wn0rRrB3cjrOz0f-rMK17zkZeSVzbZ5-c_T83cf8q3s6_3TxCzVAKuU |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VAoIL70egQJDgBFET24ntA0LlUbXaZcWhoN7cJLbLSihZ9gHKn-I34nEeq-XRWw9cYydynM_jmcz4-wCeEZnmmudF5Jx16gIUlkTCWIu8t4xYmdnE0zF8HvPJRBwfy49b8LM_C4Nllb1N9IZa1yX-I991-7AnT8_I69m3CFWjMLvaS2i0sBiZ5ocL2RavDt-57_uckP33R28Pok5VICqdd7KMMuQcI9LK1GQl88ITLgSwnMWF1TwuXACTmphbVuaFTRxCS56kBdVaahctspK6516Ai4wKjlz9Ix4NWQvUB-gP5ohsd5EgO1yERRAx6jpHzcbm5zUC_ubY_lmf-VuS1u99-9f_t1m7Adc6Lzvca5fFTdgy1S243OpuNrdB7K3T9mFtw6qthQ8xmx3O5pi78k3TKtTz1WmIR5ex1LW5A5_OZdh3YbuqK3MfwpJoa4Xzr1IrGOWmcIG0ZLE26FRJowNI-m-ryo5fHWU-viofZ4lMtXhQDg_K40E1AbwY7pm17CJn9n6DkBl6IjO4v1DPT1VnaFSi4ySXVFOZE5bKQorMCmLKPBcSj9kFsNMjRXXmaqHWMAng6dDsDA1mj_LK1Cvfh1CG4XgA91p8DiOhVDAXuLoWvoHcjaFutlTTL57MXKBHJNIAXvYYXw_r31Px4Oy3eAJXDo4-jNX4cDJ6CFdJuwSjhOzA9nK-Mo_gUvl9OV3MH_sFHMLJeWP_F_XnfE4 |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Zb9QwEB5BOcQLZymBAkHiDaLGRxL7sRwrENWqElD1zUp8lJVQdpXdrbT_Ho-dpCwUJMRrxlacyYw8n2bmG4CXVBa1qeom88E68wCFk0xY55D3llMnS0cCHcPJUTWditNTefxTF3-odh9SkrGnAVma2tXBwrjo4qI8WBLkXcuwvCDHicnZ5ipc4zg0CPH655Mxj4CM_UOrzKX7tq6jwNp_Waj5e8XkL2nTcBtN7vz_d9yF230kmh5G07kHV2x7H27E2ZSbByAOL1Lb6dylbawXT_GV6aLD_E4QzdrUdOuzFNt7sRx0swtfJ--_vP2Q9WMWMu3DtVVWIgkblU4WttQ8TOLwmMhVPG-cqfLGI7rC5pXjum4c8S6rK1I0zBhpPHzmmj2EnXbe2keQamqcEz4GKZzgrLKNB5uS58Zi4CGtSYAM2la65yDHURjfVcAiolRRI8prRAWNqE0Cr8Y9i8jA8dfVb_AnjiuRPTs8mHdnqndGRUxOaskMkzXlhWykKJ2gVte1kNiKlsD-YAKqd-ml8oFgYO8vaQIvRrF3Rsyw1K2dr8MayjhC1gT2osWMJ2FMcA_uvKTasqWto25L2tm3QPgtMGoQRQKvB4u6ONafVfH435Y_h5vH7ybq6OP00xO4RaNNZoTuw86qW9uncF2fr2bL7lnwsB_6diS9 |
| 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=Application+of+network+link+prediction+in+drug+discovery&rft.jtitle=BMC+bioinformatics&rft.au=Abbas%2C+Khushnood&rft.au=Abbasi%2C+Alireza&rft.au=Dong%2C+Shi&rft.au=Niu%2C+Ling&rft.date=2021-04-12&rft.pub=BioMed+Central&rft.eissn=1471-2105&rft.volume=22&rft_id=info:doi/10.1186%2Fs12859-021-04082-y&rft_id=info%3Apmid%2F33845763&rft.externalDocID=PMC8042985 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2105&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2105&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2105&client=summon |