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2000
Volume 23, Issue 14
  • ISSN: 1570-159X
  • E-ISSN: 1875-6190

Abstract

Neurodegenerative diseases represent a prevalent category of age-associated diseases. As human lifespans extend and societies become increasingly aged, neurodegenerative diseases pose a growing threat to public health. The lack of effective therapeutic drugs for both common and rare neurodegenerative diseases amplifies the medical challenges they present. Current treatments for these diseases primarily offer symptomatic relief rather than a cure, underscoring the pressing need to develop efficacious therapeutic interventions. Drug repositioning, an innovative and data-driven approach to research and development, proposes the re-evaluation of existing drugs for potential application in new therapeutic areas. Fueled by rapid advancements in artificial intelligence and the burgeoning accumulation of medical data, drug repositioning has emerged as a promising pathway for drug discovery. This review comprehensively examines drug repositioning for neurodegenerative diseases through the lens of translational informatics, encompassing data sources, computational models, and clinical applications. Initially, we systematized drug repositioning-related databases and online platforms, focusing on data resource management and standardization. Subsequently, we classify computational models for drug repositioning from the perspectives of drug-drug, drug-target, and drug-disease interactions into categories such as machine learning, deep learning, and network-based approaches. Lastly, we highlight computational models presently utilized in neurodegenerative disease research and identify databases that hold potential for future drug repositioning efforts. In the artificial intelligence era, drug repositioning, as a data-driven strategy, offers a promising avenue for developing treatments suited to the complex and multifaceted nature of neurodegenerative diseases. These advancements could furnish patients with more rapid, cost-effective therapeutic options.

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2025-02-06
2025-12-07
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References

  1. DuggerB.N. DicksonD.W. Pathology of neurodegenerative diseases.Cold Spring Harb. Perspect. Biol.201797a02803510.1101/cshperspect.a028035 28062563
    [Google Scholar]
  2. HungC.W. ChenY.C. HsiehW.L. ChiouS.H. KaoC.L. Ageing and neurodegenerative diseases.Ageing Res. Rev.20109Suppl. 1S36S4610.1016/j.arr.2010.08.006 20732460
    [Google Scholar]
  3. PalmisanoJ. Developing drugs for rare diseases: Regulatory strategies and considerations. In: Drug Development for Rare Diseases.Chapman and Hall/CRC2023173310.1201/9781003080954‑3
    [Google Scholar]
  4. Blanco-GonzálezA. CabezónA. Seco-GonzálezA. Conde-TorresD. Antelo-RiveiroP. PiñeiroÁ. Garcia-FandinoR. The role of ai in drug discovery: Challenges, opportunities, and strategies.Pharmaceuticals (Basel)202316689110.3390/ph16060891 37375838
    [Google Scholar]
  5. LeiS. LeiX. ChenM. PanY. Drug repositioning based on deep sparse autoencoder and drug-disease similarity.Interdiscip. Sci.202416116017510.1007/s12539‑023‑00593‑9 38103130
    [Google Scholar]
  6. YuA.Z. RamseyS.A. A computational systems biology approach for identifying candidate drugs for repositioning for cardiovascular disease.Interdiscip. Sci.201810244945410.1007/s12539‑016‑0194‑3 27778232
    [Google Scholar]
  7. XueH. LiJ. XieH. WangY. Review of drug repositioning approaches and resources.Int. J. Biol. Sci.201814101232124410.7150/ijbs.24612 30123072
    [Google Scholar]
  8. JourdanJ.P. BureauR. RochaisC. DallemagneP. Drug repositioning: A brief overview.J. Pharm. Pharmacol.20207291145115110.1111/jphp.13273 32301512
    [Google Scholar]
  9. HeinrichM. Lee TeohH. Galanthamine from snowdrop-the development of a modern drug against Alzheimer’s disease from local caucasian knowledge.J. Ethnopharmacol.2004922-314716210.1016/j.jep.2004.02.012 15137996
    [Google Scholar]
  10. ClouserC.L. PattersonS.E. ManskyL.M. Exploiting drug repositioning for discovery of a novel HIV combination therapy.J. Virol.201084189301930910.1128/JVI.01006‑10 20610712
    [Google Scholar]
  11. SotiropoulouG. ZingkouE. PampalakisG. Redirecting drug repositioning to discover innovative cosmeceuticals.Exp. Dermatol.202130562864410.1111/exd.14299 33544970
    [Google Scholar]
  12. KobayashiY. BannoK. KunitomiH. TominagaE. AokiD. Current state and outlook for drug repositioning anticipated in the field of ovarian cancer.J. Gynecol. Oncol.2019301e1010.3802/jgo.2019.30.e10 30479094
    [Google Scholar]
  13. LythgoeM.P. PrasadV. Repositioning canakinumab for non-small cell lung cancer-important lessons for drug repurposing in oncology.Br. J. Cancer2022127578578710.1038/s41416‑022‑01893‑5 35739301
    [Google Scholar]
  14. RebeloR. PolóniaB. SantosL.L. VasconcelosM.H. XavierC.P.R. Drug repurposing opportunities in pancreatic ductal adenocarcinoma.Pharmaceuticals (Basel)202114328010.3390/ph14030280 33804613
    [Google Scholar]
  15. KhanA. AliS.S. KhanM.T. SaleemS. AliA. SulemanM. BabarZ. ShafiqA. KhanM. WeiD.Q. Combined drug repurposing and virtual screening strategies with molecular dynamics simulation identified potent inhibitors for SARS-CoV-2 main protease (3CLpro).J. Biomol. Struct. Dyn.202139134659467010.1080/07391102.2020.1779128 32552361
    [Google Scholar]
  16. DotoloS. MarabottiA. FacchianoA. TagliaferriR. A review on drug repurposing applicable to COVID-19.Brief. Bioinform.202122272674110.1093/bib/bbaa288 33147623
    [Google Scholar]
  17. MajiS. BadavathV.N. GangulyS. Drug repurposing and computational drug discovery for viral infections and coronavirus disease-2019 (COVID-19). In: Drug Repurposing and Computational Drug Discovery.Apple Academic Press2023597610.1201/9781003347705‑3
    [Google Scholar]
  18. WeiD. PeslherbeG.H. SelvarajG. WangY. Advances in drug design and development for human therapeutics using artificial intelligence-I.MDPI2022Vol. 121846
    [Google Scholar]
  19. SyedR. EdenR. MakasiT. ChukwudiI. MamuduA. KamalpourM. KapugamaG.D. SadeghianaslS. LeemansS.J.J. GoelK. AndrewsR. WynnM.T. ter HofstedeA. MyersT. Digital health data quality issues: Systematic review.J. Med. Internet Res.202325e4261510.2196/42615 37000497
    [Google Scholar]
  20. Von KroghG. SpaethS. LakhaniK. PatonC. KaropkaT. EricksonB. LangerS. NagyP. KobayashiS. YahataK. The privacy and security implications of open data in healthcare: A contribution from the IMIA open source working group.Yearb. Med. Inform.2018274147
    [Google Scholar]
  21. ShenB. LinY. BiC. ZhouS. BaiZ. ZhengG. ZhouJ. Translational informatics for Parkinson’s disease: From Big biomedical data to small actionable alterations.Genomics Proteomics Bioinformatics201917441542910.1016/j.gpb.2018.10.007 31786313
    [Google Scholar]
  22. ShenK. DinA.U. SinhaB. ZhouY. QianF. ShenB. Translational informatics for human microbiota: Data resources, models and applications.Brief. Bioinform.2023243bbad16810.1093/bib/bbad168 37141135
    [Google Scholar]
  23. SinglaR.K. JoonS. ShenL. ShenB. Translational informatics for natural products as antidepressant agents.Front. Cell Dev. Biol.2022973883810.3389/fcell.2021.738838 35127696
    [Google Scholar]
  24. WeiD-Q. MaY. ChoW.C. XuQ. ZhouF. Translational bioinformatics and its application.Springer201710.1007/978‑94‑024‑1045‑7
    [Google Scholar]
  25. PayneP.R.O. EmbiP.J. SenC.K. Translational informatics: enabling high-throughput research paradigms.Physiol. Genomics200939313114010.1152/physiolgenomics.00050.2009 19737991
    [Google Scholar]
  26. IvanovićM. BudimacZ. An overview of ontologies and data resources in medical domains.Expert Syst. Appl.201441115158516610.1016/j.eswa.2014.02.045
    [Google Scholar]
  27. LuoH. LiM. YangM. WuF.X. LiY. WangJ. Biomedical data and computational models for drug repositioning: A comprehensive review.Brief. Bioinform.20212221604161910.1093/bib/bbz176 32043521
    [Google Scholar]
  28. ChenJ. ChenZ. ChenR. FengD. LiT. HanH. BiX. WangZ. LiK. LiY. LiX. WangL. LiJ. HCDT: An integrated highly confident drug-target resource.Database (Oxford)20222022baac10110.1093/database/baac101
    [Google Scholar]
  29. Gonzalez-CavazosA.C. TanskaA. MayersM. Carvalho-SilvaD. SridharanB. RewersP.A. SankarlalU. JagannathanL. SuA.I. DrugMechDB: A curated database of drug mechanisms.Sci. Data202310163210.1038/s41597‑023‑02534‑z 37717042
    [Google Scholar]
  30. Masoudi-SobhanzadehY. OmidiY. AmanlouM. Masoudi-NejadA. DrugR. Drug R+: A comprehensive relational database for drug repurposing, combination therapy, and replacement therapy.Comput. Biol. Med.201910925426210.1016/j.compbiomed.2019.05.006 31096089
    [Google Scholar]
  31. ZhuQ. TaoC. ShenF. ChuteC.G. Exploring the pharmacogenomics knowledge base (PharmGKB) for repositioning breast cancer drugs by leveraging Web ontology language (OWL) and cheminformatics approaches.Pac. Symp. Biocomput.2014172182 24297544
    [Google Scholar]
  32. AbdelhakimM. McMurrayE. SyedA.R. KafkasS. KamauA.A. SchofieldP.N. HoehndorfR. DDIEM: drug database for inborn errors of metabolism.Orphanet J. Rare Dis.202015114610.1186/s13023‑020‑01428‑2 32527280
    [Google Scholar]
  33. KuoT.C. WangP.H. WangY.K. ChangC.I. ChangC.Y. TsengY.J. RSDB: A rare skin disease database to link drugs with potential drug targets for rare skin diseases.Sci. Data20229152110.1038/s41597‑022‑01654‑2 36028515
    [Google Scholar]
  34. TrouléK. López-FernándezH. García-MartínS. Reboiro-JatoM. Carretero-PucheC. Martorell-MarugánJ. Martín-SerranoG. Carmona-SáezP. Glez-PeñaD. Al-ShahrourF. Gómez-LópezG. DREIMT: A drug repositioning database and prioritization tool for immunomodulation.Bioinformatics202137457857910.1093/bioinformatics/btaa727 32818254
    [Google Scholar]
  35. TaoW. LiB. GaoS. BaiY. SharP.A. ZhangW. GuoZ. SunK. FuY. HuangC. ZhengC. MuJ. PeiT. WangY. LiY. WangY. CancerH.S.P. CancerHSP: Anticancer herbs database of systems pharmacology.Sci. Rep.2015511148110.1038/srep11481 26074488
    [Google Scholar]
  36. von EichbornJ. MurgueitioM.S. DunkelM. KoernerS. BourneP.E. PreissnerR. PROMISCUOUS: A database for network-based drug-repositioning.Nucleic Acids Res.201139Database) (Suppl. 1D1060D106610.1093/nar/gkq103721071407
    [Google Scholar]
  37. GalloK. GoedeA. EckertA. MoahamedB. PreissnerR. GohlkeB.O. PROMISCUOUS 2.0: A resource for drug-repositioning.Nucleic Acids Res.202149D1D1373D138010.1093/nar/gkaa1061 33196798
    [Google Scholar]
  38. HuangH. NguyenT. IbrahimS. ShantharamS. YueZ. ChenJ.Y. DMAP: A connectivity map database to enable identification of novel drug repositioning candidates.BMC Bioinformatics20151613S410.1186/1471‑2105‑16‑S13‑S4
    [Google Scholar]
  39. GnilopyatS. DePietroP.J. ParryT.K. McLaughlinW.A. The Pharmacorank search tool for the retrieval of prioritized protein drug targets and drug repositioning candidates according to selected diseases.Biomolecules20221211155910.3390/biom12111559 36358909
    [Google Scholar]
  40. YangJ. WuS.J. YangS.Y. PengJ.W. WangS.N. WangF.Y. SongY.X. QiT. LiY.X. LiY.Y. DNetDB: The human disease network database based on dysfunctional regulation mechanism.BMC Syst. Biol.20161013610.1186/s12918‑016‑0280‑5 27209279
    [Google Scholar]
  41. KangH. PanS. LinS. WangY.Y. YuanN. JiaP. PharmGWAS: A GWAS-based knowledgebase for drug repurposing.Nucleic Acids Res.202452D1D972D97910.1093/nar/gkad832 37831083
    [Google Scholar]
  42. ChenY.W. DiamanteG. DingJ. NghiemT.X. YangJ. HaS.M. CohnP. ArnesonD. BlencoweM. GarciaJ. ZaghariN. PatelP. YangX. PharmOmics: A species- and tissue-specific drug signature database and gene-network-based drug repositioning tool.iScience202225410405210.1016/j.isci.2022.104052 35345455
    [Google Scholar]
  43. XuR. LiL. WangQ. dRiskKB: A large-scale disease-disease risk relationship knowledge base constructed from biomedical text.BMC Bioinformatics201415110510.1186/1471‑2105‑15‑105 24725842
    [Google Scholar]
  44. XuR. WangQ. Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature.J. Biomed. Inform.20145119119910.1016/j.jbi.2014.05.013 24928448
    [Google Scholar]
  45. BrownA.S. PatelC.J. A standard database for drug repositioning.Sci. Data20174117002910.1038/sdata.2017.29 28291243
    [Google Scholar]
  46. ZhaoC. DaiX. LiY. GuoQ. ZhangJ. ZhangX. WangL. EK-DRD: A comprehensive database for drug repositioning inspired by experimental knowledge.J. Chem. Inf. Model.20195993619362410.1021/acs.jcim.9b00365 31433187
    [Google Scholar]
  47. TangJ. TanoliZ.R. RavikumarB. AlamZ. RebaneA. Vähä-KoskelaM. PeddintiG. van AdrichemA.J. WakkinenJ. JaiswalA. KarjalainenE. GautamP. HeL. ParriE. KhanS. GuptaA. AliM. YetukuriL. GustavssonA.L. Seashore-LudlowB. HerseyA. LeachA.R. OveringtonJ.P. RepaskyG. WennerbergK. AittokallioT. Drug target commons: A community effort to build a consensus knowledge base for drug-target interactions.Cell Chem. Biol.2018252224229.e210.1016/j.chembiol.2017.11.009 29276046
    [Google Scholar]
  48. FengY.H. ZhangS.W. ShiJ.Y. DPDDI: a deep predictor for drug-drug interactions.BMC Bioinformatics202021141910.1186/s12859‑020‑03724‑x 32972364
    [Google Scholar]
  49. YuY. HuangK. ZhangC. GlassL.M. SunJ. XiaoC. SumGNN: Multi-typed drug interaction prediction via efficient knowledge graph summarization.Bioinformatics202137182988299510.1093/bioinformatics/btab207 33769494
    [Google Scholar]
  50. ChenX. LiuX. WuJ. GCN-BMP: Investigating graph representation learning for DDI prediction task.Methods2020179475410.1016/j.ymeth.2020.05.014 32622985
    [Google Scholar]
  51. ZhangY. QiuY. CuiY. LiuS. ZhangW. Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning.Methods2020179374610.1016/j.ymeth.2020.05.007 32497603
    [Google Scholar]
  52. HuangK. XiaoC. GlassL.M. ZitnikM. SunJ. SkipGNN: predicting molecular interactions with skip-graph networks.Sci. Rep.20201012109210.1038/s41598‑020‑77766‑9 33273494
    [Google Scholar]
  53. ZhaoB.W. SuX.R. HuP.W. MaY.P. ZhouX. HuL. A geometric deep learning framework for drug repositioning over heterogeneous information networks.Brief. Bioinform.2022236bbac38410.1093/bib/bbac384 36125202
    [Google Scholar]
  54. WangF. LeiX. LiaoB. WuF.X. Predicting drug-drug interactions by graph convolutional network with multi-kernel.Brief. Bioinform.2022231bbab51110.1093/bib/bbab511 34864856
    [Google Scholar]
  55. HeT. HeidemeyerM. BanF. CherkasovA. EsterM. SimBoost: A read-across approach for predicting drug-target binding affinities using gradient boosting machines.J. Cheminform.2017912410.1186/s13321‑017‑0209‑z 29086119
    [Google Scholar]
  56. ChuY. ShanX. ChenT. JiangM. WangY. WangQ. SalahubD.R. XiongY. WeiD.Q. DTI-MLCD: Predicting drug-target interactions using multi-label learning with community detection method.Brief. Bioinform.2021223bbaa20510.1093/bib/bbaa205 32964234
    [Google Scholar]
  57. WanF. HongL. XiaoA. JiangT. ZengJ. NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions.Bioinformatics201935110411110.1093/bioinformatics/bty543 30561548
    [Google Scholar]
  58. PengJ. LiJ. ShangX. A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network.BMC Bioinformatics202021S13Suppl. 1339410.1186/s12859‑020‑03677‑1 32938374
    [Google Scholar]
  59. ÖztürkH. ÖzgürA. OzkirimliE. DeepDTA: Deep drug-target binding affinity prediction.Bioinformatics20183417i821i82910.1093/bioinformatics/bty593 30423097
    [Google Scholar]
  60. AbbasiK. RazzaghiP. PosoA. AmanlouM. GhasemiJ.B. Masoudi-NejadA. DeepCDA: deep cross-domain compound-protein affinity prediction through LSTM and convolutional neural networks.Bioinformatics202036174633464210.1093/bioinformatics/btaa544 32462178
    [Google Scholar]
  61. WeiL. LongW. WeiL. MDL-CPI: Multi-view deep learning model for compound-protein interaction prediction.Methods202220441842710.1016/j.ymeth.2022.01.008 35114401
    [Google Scholar]
  62. WeiB. ZhangY. GongX. DeepLPI: A novel deep learning-based model for protein-ligand interaction prediction for drug repurposing.Sci. Rep.20221211820010.1038/s41598‑022‑23014‑1 36307509
    [Google Scholar]
  63. ZhaiH. HouH. LuoJ. LiuX. WuZ. WangJ. DGDTA: Dynamic graph attention network for predicting drug-target binding affinity.BMC Bioinformatics202324136710.1186/s12859‑023‑05497‑5 37777712
    [Google Scholar]
  64. ZhangR. WangZ. WangX. MengZ. CuiW. MHTAN-DTI: Metapath-based hierarchical transformer and attention network for drug-target interaction prediction.Brief. Bioinform.2023242bbad07910.1093/bib/bbad079 36892155
    [Google Scholar]
  65. Yazdani-JahromiM. YousefiN. TayebiA. KolanthaiE. NealC.J. SealS. GaribayO.O. AttentionSiteDTI: An interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification.Brief. Bioinform.2022234bbac27210.1093/bib/bbac272 35817396
    [Google Scholar]
  66. YangX. YangG. ChuJ. GraphCL-DTA: A Graph contrastive learning with molecular semantics for drug-target binding affinity prediction.IEEE J. Biomed. Health Inform.2024284544455210.1109/JBHI.2024.3350666
    [Google Scholar]
  67. SonJ. KimD. Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities.PLoS One2021164e024940410.1371/journal.pone.0249404 33831016
    [Google Scholar]
  68. NguyenT. LeH. QuinnT.P. NguyenT. LeT.D. VenkateshS. GraphD.T.A. GraphDTA: predicting drug-target binding affinity with graph neural networks.Bioinformatics20213781140114710.1093/bioinformatics/btaa921 33119053
    [Google Scholar]
  69. LiJ. WangJ. LvH. ZhangZ. WangZ. IMCHGAN: Inductive matrix completion with heterogeneous graph attention networks for drug-target interactions prediction.IEEE/ACM Trans. Comput. Biol. Bioinformatics202219265566510.1109/TCBB.2021.3088614 34115592
    [Google Scholar]
  70. LiY. QiaoG. WangK. WangG. Drug-target interaction predication via multi-channel graph neural networks.Brief. Bioinform.2022231bbab34610.1093/bib/bbab346 34661237
    [Google Scholar]
  71. YangZ. ZhongW. ZhaoL. Yu-ChianC.C. MGraphDTA: deep multiscale graph neural network for explainable drug-target binding affinity prediction.Chem. Sci. (Camb.)202213381683310.1039/D1SC05180F
    [Google Scholar]
  72. ShaoK. ZhangY. WenY. ZhangZ. HeS. BoX. DTI-HETA: prediction of drug-target interactions based on GCN and GAT on heterogeneous graph.Brief. Bioinform.2022233bbac10910.1093/bib/bbac109 35380622
    [Google Scholar]
  73. LiM. CaiX. XuS. JiH. Metapath-aggregated heterogeneous graph neural network for drug-target interaction prediction.Brief. Bioinform.2023241bbac57810.1093/bib/bbac578 36592060
    [Google Scholar]
  74. WangH. GuoF. DuM. WangG. CaoC. A novel method for drug-target interaction prediction based on graph transformers model.BMC Bioinformatics202223145910.1186/s12859‑022‑04812‑w 36329406
    [Google Scholar]
  75. MaJ. LiC. ZhangY. WangZ. LiS. GuoY. ZhangL. LiuH. GaoX. SongJ. MULGA, a unified multi-view graph autoencoder-based approach for identifying drug-protein interaction and drug repositioning.Bioinformatics2023399btad52410.1093/bioinformatics/btad524 37610353
    [Google Scholar]
  76. ShangY. YeX. FutamuraY. YuL. SakuraiT. Multiview network embedding for drug-target Interactions prediction by consistent and complementary information preserving.Brief. Bioinform.2022233bbac05910.1093/bib/bbac059 35262678
    [Google Scholar]
  77. YangZ. ZhongW. ZhaoL. ChenC.Y.C. ML-DTI: Mutual learning mechanism for interpretable drug-target interaction prediction.J. Phys. Chem. Lett.202112174247426110.1021/acs.jpclett.1c00867 33904745
    [Google Scholar]
  78. ChuY. KaushikA.C. WangX. WangW. ZhangY. ShanX. SalahubD.R. XiongY. WeiD.Q. DTI-CDF: A cascade deep forest model towards the prediction of drug-target interactions based on hybrid features.Brief. Bioinform.202122145146210.1093/bib/bbz152 31885041
    [Google Scholar]
  79. TianZ. PengX. FangH. ZhangW. DaiQ. YeY. MHADTI: predicting drug-target interactions via multiview heterogeneous information network embedding with hierarchical attention mechanisms.Brief. Bioinform.2022236bbac43410.1093/bib/bbac434 36242566
    [Google Scholar]
  80. LuoY. ZhaoX. ZhouJ. YangJ. ZhangY. KuangW. PengJ. ChenL. ZengJ. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information.Nat. Commun.20178157310.1038/s41467‑017‑00680‑8 28924171
    [Google Scholar]
  81. ZhouD. XuZ. LiW. XieX. PengS. MultiD.T.I. MultiDTI: drug-target interaction prediction based on multi-modal representation learning to bridge the gap between new chemical entities and known heterogeneous network.Bioinformatics202137234485449210.1093/bioinformatics/btab473 34180970
    [Google Scholar]
  82. PengY. WangM. XuY. WuZ. WangJ. ZhangC. LiuG. LiW. LiJ. TangY. Drug repositioning by prediction of drug’s anatomical therapeutic chemical code via network-based inference approaches.Brief. Bioinform.20212222058207210.1093/bib/bbaa027 32221552
    [Google Scholar]
  83. LiY. QiaoG. GaoX. WangG. Supervised graph co-contrastive learning for drug-target interaction prediction.Bioinformatics202238102847285410.1093/bioinformatics/btac164 35561181
    [Google Scholar]
  84. MohamedS.K. NováčekV. NounuA. Discovering protein drug targets using knowledge graph embeddings.Bioinformatics202036260361010.1093/bioinformatics/btz600 31368482
    [Google Scholar]
  85. YeQ. HsiehC.Y. YangZ. KangY. ChenJ. CaoD. HeS. HouT. A unified drug-target interaction prediction framework based on knowledge graph and recommendation system.Nat. Commun.2021121677510.1038/s41467‑021‑27137‑3 34811351
    [Google Scholar]
  86. ZhaoB.W. HuL. YouZ.H. WangL. SuX.R. HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks.Brief. Bioinform.2022231bbab51510.1093/bib/bbab515 34891172
    [Google Scholar]
  87. XuX. GuH. WangY. WangJ. QinP. Autoencoder based feature selection method for classification of anticancer drug response.Front. Genet.20191023310.3389/fgene.2019.00233 30972101
    [Google Scholar]
  88. ZhangM.L. ZhaoB.W. SuX.R. HeY.Z. YangY. HuL. RLFDDA: a meta-path based graph representation learning model for drug-disease association prediction.BMC Bioinformatics202223151610.1186/s12859‑022‑05069‑z 36456957
    [Google Scholar]
  89. MengY. WangY. XuJ. LuC. TangX. PengT. ZhangB. TianG. YangJ. Drug repositioning based on weighted local information augmented graph neural network.Brief. Bioinform.2023251bbad43110.1093/bib/bbad431 38019732
    [Google Scholar]
  90. YuZ. HuangF. ZhaoX. XiaoW. ZhangW. Predicting drug-disease associations through layer attention graph convolutional network.Brief. Bioinform.2021224bbaa24310.1093/bib/bbaa243 33078832
    [Google Scholar]
  91. SunX. WangB. ZhangJ. LiM. Partner-specific drug repositioning approach based on graph convolutional network.IEEE J. Biomed. Health Inform.202226115757576510.1109/JBHI.2022.3194891 35921345
    [Google Scholar]
  92. SunX. JiaX. LuZ. TangJ. LiM. Drug repositioning with adaptive graph convolutional networks.Bioinformatics2024401btad74810.1093/bioinformatics/btad748 38070161
    [Google Scholar]
  93. CaiL. LuC. XuJ. MengY. WangP. FuX. ZengX. SuY. Drug repositioning based on the heterogeneous information fusion graph convolutional network.Brief. Bioinform.2021226bbab31910.1093/bib/bbab319 34378011
    [Google Scholar]
  94. ZengX. ZhuS. LiuX. ZhouY. NussinovR. ChengF. deepDR: A network-based deep learning approach to in silico drug repositioning.Bioinformatics201935245191519810.1093/bioinformatics/btz418 31116390
    [Google Scholar]
  95. LiuB.M. GaoY.L. ZhangD.J. ZhouF. WangJ. ZhengC.H. LiuJ.X. A new framework for drug-disease association prediction combing light-gated message passing neural network and gated fusion mechanism.Brief. Bioinform.2022236bbac45710.1093/bib/bbac457 36305457
    [Google Scholar]
  96. ChenP. BaoT. YuX. LiuZ. A drug repositioning algorithm based on a deep autoencoder and adaptive fusion.BMC Bioinformatics202122153210.1186/s12859‑021‑04406‑y 34717542
    [Google Scholar]
  97. YiH.C. YouZ.H. WangL. SuX.R. ZhouX. JiangT.H. In silico drug repositioning using deep learning and comprehensive similarity measures.BMC Bioinformatics202122S3Suppl. 329310.1186/s12859‑020‑03882‑y 34074242
    [Google Scholar]
  98. JaradaT.N. RokneJ.G. AlhajjR. SNF-NN: Computational method to predict drug-disease interactions using similarity network fusion and neural networks.BMC Bioinformatics20212212810.1186/s12859‑020‑03950‑3 33482713
    [Google Scholar]
  99. LiuH. ZhangW. SongY. DengL. ZhouS. HNet-DNN: inferring new drug-disease associations with deep neural network based on heterogeneous network features.J. Chem. Inf. Model.20206042367237610.1021/acs.jcim.9b01008 32118415
    [Google Scholar]
  100. EmdadiA. EslahchiC. Auto-HMM-LMF: Feature selection based method for prediction of drug response via autoencoder and hidden Markov model.BMC Bioinformatics20212213310.1186/s12859‑021‑03974‑3 33509079
    [Google Scholar]
  101. WangL. LiX. ZhangL. GaoQ. Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization.BMC Cancer201717151310.1186/s12885‑017‑3500‑5 28768489
    [Google Scholar]
  102. SuphavilaiC. BertrandD. NagarajanN. Predicting cancer drug response using a recommender system.Bioinformatics201834223907391410.1093/bioinformatics/bty452 29868820
    [Google Scholar]
  103. EmdadiA. EslahchiC. DSPLMF: A method for cancer drug sensitivity prediction using a novel regularization approach in logistic matrix factorization.Front. Genet.2020117510.3389/fgene.2020.00075 32174963
    [Google Scholar]
  104. IwataM. YuanL. ZhaoQ. TabeiY. BerengerF. SawadaR. AkiyoshiS. HamanoM. YamanishiY. Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm.Bioinformatics20193514i191i19910.1093/bioinformatics/btz313 31510663
    [Google Scholar]
  105. MengY. LuC. JinM. XuJ. ZengX. YangJ. A weighted bilinear neural collaborative filtering approach for drug repositioning.Brief. Bioinform.2022232bbab58110.1093/bib/bbab581 35039838
    [Google Scholar]
  106. XieG. LiJ. GuG. SunY. LinZ. ZhuY. WangW. BGMSDDA: a bipartite graph diffusion algorithm with multiple similarity integration for drug-disease association prediction.Mol. Omics2021176997101110.1039/D1MO00237F 34610633
    [Google Scholar]
  107. ZhangW. YueX. LinW. WuW. LiuR. HuangF. LiuF. Predicting drug-disease associations by using similarity constrained matrix factorization.BMC Bioinformatics201819123310.1186/s12859‑018‑2220‑4 29914348
    [Google Scholar]
  108. ZhangW. XuH. LiX. GaoQ. WangL. DRIMC: an improved drug repositioning approach using Bayesian inductive matrix completion.Bioinformatics20203692839284710.1093/bioinformatics/btaa062 31999326
    [Google Scholar]
  109. YanY. YangM. ZhaoH. DuanG. PengX. WangJ. Drug repositioning based on multi-view learning with matrix completion.Brief. Bioinform.2022233bbac05410.1093/bib/bbac054 35289352
    [Google Scholar]
  110. YangM. WuG. ZhaoQ. LiY. WangJ. Computational drug repositioning based on multi-similarities bilinear matrix factorization.Brief. Bioinform.2021224bbaa26710.1093/bib/bbaa267 33147616
    [Google Scholar]
  111. YangM. HuangL. XuY. LuC. WangJ. Heterogeneous graph inference with matrix completion for computational drug repositioning.Bioinformatics20213622-235456546410.1093/bioinformatics/btaa1024 33331887
    [Google Scholar]
  112. GhorbanaliZ. Zare-MirakabadF. SalehiN. AkbariM. Masoudi-NejadA. DrugRep-HeSiaGraph: when heterogenous siamese neural network meets knowledge graphs for drug repurposing.BMC Bioinformatics202324137410.1186/s12859‑023‑05479‑7 37789314
    [Google Scholar]
  113. GhorbanaliZ. Zare-MirakabadF. AkbariM. SalehiN. Masoudi-NejadA. DrugRep-KG: Toward learning a unified latent space for drug repurposing using knowledge graphs.J. Chem. Inf. Model.20236382532254510.1021/acs.jcim.2c01291 37023229
    [Google Scholar]
  114. YangK. YangY. FanS. XiaJ. ZhengQ. DongX. LiuJ. LiuQ. LeiL. ZhangY. LiB. GaoZ. ZhangR. LiuB. WangZ. ZhouX. DRONet: Effectiveness-driven drug repositioning framework using network embedding and ranking learning.Brief. Bioinform.2023241bbac51810.1093/bib/bbac518 36562715
    [Google Scholar]
  115. HeJ. YangX. GongZ. Zamit, Hybrid attentional memory network for computational drug repositioning.BMC Bioinformatics202021156610.1186/s12859‑020‑03898‑4 33297947
    [Google Scholar]
  116. FangJ. PieperA.A. NussinovR. LeeG. BekrisL. LeverenzJ.B. CummingsJ. ChengF. Harnessing endophenotypes and network medicine for Alzheimer’s drug repurposing.Med. Res. Rev.20204062386242610.1002/med.21709 32656864
    [Google Scholar]
  117. XieS.X. BaekY. GrossmanM. ArnoldS.E. KarlawishJ. SiderowfA. HurtigH. ElmanL. McCluskeyL. Van DeerlinV. LeeV.M.Y. TrojanowskiJ.Q. Building an integrated neurodegenerative disease database at an academic health center.Alzheimers Dement.201174e84e9310.1016/j.jalz.2010.08.233 21784346
    [Google Scholar]
  118. VasaikarS.V. PadhiA.K. JayaramB. GomesJ. NeuroDNet - an open source platform for constructing and analyzing neurodegenerative disease networks.BMC Neurosci.2013141310.1186/1471‑2202‑14‑3 23286825
    [Google Scholar]
  119. NaD. RoufM. O’KaneC.J. RubinszteinD.C. GsponerJ. NeuroGeM, a knowledgebase of genetic modifiers in neurodegenerative diseases.BMC Med. Genomics2013615210.1186/1755‑8794‑6‑52 24229347
    [Google Scholar]
  120. YangY. XuC. LiuX. XuC. ZhangY. ShenL. VihinenM. ShenB. NDDVD: An integrated and manually curated neurodegenerative diseases variation database.Database (Oxford)201820181810.1093/database/bay018
    [Google Scholar]
  121. ChaudhariS. NahaR. MukherjeeS. SharmaA. JayaramP. MallyaS. ChakrabartyS. SatyamoorthyK. DINAX- a comprehensive database of inherited ataxias.Comput. Biol. Med.202012610400010.1016/j.compbiomed.2020.104000 33007622
    [Google Scholar]
  122. BiC. ZhouS. LiuX. ZhuY. YuJ. ZhangX. ShiM. WuR. HeH. ZhanC. LinY. ShenB. NDDRF: A risk factor knowledgebase for personalized prevention of neurodegenerative diseases.J. Adv. Res.20224022323110.1016/j.jare.2021.06.015 36100329
    [Google Scholar]
  123. SzlachcicW.J. SwitonskiP.M. KurkowiakM. WiatrK. FigielM. Mouse polyQ database: A new online resource for research using mouse models of neurodegenerative diseases.Mol. Brain2015816910.1186/s13041‑015‑0160‑8 26515641
    [Google Scholar]
  124. EstevamB. MatosC.A. NóbregaC. PolyQ Database-an integrated database on polyglutamine diseases.Database (Oxford)20232023baad06010.1093/database/baad060
    [Google Scholar]
  125. BeeklyD.L. RamosE.M. van BelleG. DeitrichW. ClarkA.D. JackaM.E. KukullW.A. The National Alzheimer’s coordinating center (NACC) database: An Alzheimer disease database.Alzheimer Dis. Assoc. Disord.2004184270277 15592144
    [Google Scholar]
  126. BertramL. McQueenM.B. MullinK. BlackerD. TanziR.E. Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database.Nat. Genet.2007391172310.1038/ng1934 17192785
    [Google Scholar]
  127. LiuH. WangL. LvM. PeiR. LiP. PeiZ. WangY. SuW. XieX.Q. AlzPlatform: an Alzheimer’s disease domain-specific chemogenomics knowledgebase for polypharmacology and target identification research.J. Chem. Inf. Model.20145441050106010.1021/ci500004h 24597646
    [Google Scholar]
  128. BaiZ. HanG. XieB. WangJ. SongF. PengX. LeiH. AlzBase: An integrative database for gene dysregulation in Alzheimer’s disease.Mol. Neurobiol.201653131031910.1007/s12035‑014‑9011‑3 25432889
    [Google Scholar]
  129. FangJ. WangL. LiY. LianW. PangX. WangH. YuanD. WangQ. LiuA.L. DuG.H. AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer’s disease.PLoS One2017125e017834710.1371/journal.pone.0178347 28542505
    [Google Scholar]
  130. ZhouY. FangJ. BekrisL.M. KimY.H. PieperA.A. LeverenzJ.B. CummingsJ. ChengF. AlzGPS: A genome-wide positioning systems platform to catalyze multi-omics for Alzheimer’s drug discovery.Alzheimers Res. Ther.20211312410.1186/s13195‑020‑00760‑w 33441136
    [Google Scholar]
  131. WangZ. MengL. LiuH. ShenL. JiH.F. AlzRiskMR database: an online database for the impact of exposure factors on Alzheimer’s disease.Brief. Bioinform.2021223bbaa21310.1093/bib/bbaa213 32951050
    [Google Scholar]
  132. LinC.X. LiH.D. DengC. ErhardtS. WangJ. PengX. WangJ. AlzCode: A platform for multiview analysis of genes related to Alzheimer’s disease.Bioinformatics20223872030203210.1093/bioinformatics/btac033 35040932
    [Google Scholar]
  133. BajicV.P. SalhiA. LakotaK. RadovanovicA. RazaliR. ZivkovicL. Spremo-PotparevicB. UludagM. TifrateneF. MotwalliO. MarchandB. BajicV.B. GojoboriT. IsenovicE.R. EssackM. DES-Amyloidoses “Amyloidoses through the looking-glass”: A knowledgebase developed for exploring and linking information related to human amyloid-related diseases.PLoS One2022177e027173710.1371/journal.pone.0271737 35877764
    [Google Scholar]
  134. PengX. ZhangW. CuiW. DingB. LyuQ. WangJ. ADmeth: A manually curated database for the differential methylation in Alzheimer’s disease.IEEE/ACM Trans. Comput. Biol. Bioinformatics202320284385110.1109/TCBB.2022.3178087 35617175
    [Google Scholar]
  135. YangJ.O. KimW.Y. JeongS.Y. OhJ.H. JhoS. BhakJ. KimN.S. PDbase: A database of Parkinson’s disease-related genes and genetic variation using substantia nigra ESTs.BMC Genomics20091033210.1186/1471‑2164‑10‑S3‑S32
    [Google Scholar]
  136. TaccioliC. MaselliV. TegnerJ. Gomez-CabreroD. AltobelliG. EmmettW. LescaiF. GustincichS. StupkaE. ParkD.B. ParkDB: a Parkinson’s disease gene expression database.Database (Oxford)201120110bar00710.1093/database/bar007 21593080
    [Google Scholar]
  137. LillC.M. RoehrJ.T. McQueenM.B. KavvouraF.K. BagadeS. SchjeideB.M.M. SchjeideL.M. MeissnerE. ZauftU. AllenN.C. LiuT. SchillingM. AndersonK.J. BeechamG. BergD. BiernackaJ.M. BriceA. DeStefanoA.L. DoC.B. ErikssonN. FactorS.A. FarrerM.J. ForoudT. GasserT. HamzaT. HardyJ.A. HeutinkP. Hill-BurnsE.M. KleinC. LatourelleJ.C. MaraganoreD.M. MartinE.R. MartinezM. MyersR.H. NallsM.A. PankratzN. PayamiH. SatakeW. ScottW.K. SharmaM. SingletonA.B. StefanssonK. TodaT. TungJ.Y. VanceJ. WoodN.W. ZabetianC.P. YoungP. TanziR.E. KhouryM.J. ZippF. LehrachH. IoannidisJ.P.A. BertramL. Comprehensive research synopsis and systematic meta-analyses in Parkinson’s disease genetics: The PDGene database.PLoS Genet.201283e100254810.1371/journal.pgen.1002548 22438815
    [Google Scholar]
  138. Gan-OrZ. RaoT. LeveilleE. DegrootC. ChouinardS. CicchettiF. DagherA. DasS. DesautelsA. Drouin-OuelletJ. DurcanT. GagnonJ.F. GengeA. KaramchandaniJ. LafontaineA.L. SunS.L.W. LangloisM. LevesqueM. MelmedC. PanissetM. ParentM. PolineJ.B. PostumaR.B. PourcherE. RouleauG.A. SharpM. MonchiO. DupréN. FonE.A. The quebec parkinson network: A researcher-patient matching platform and multimodal biorepository.J. Parkinsons Dis.202010130131310.3233/JPD‑191775 31868683
    [Google Scholar]
  139. Pintado-GrimaC. BárcenasO. IglesiasV. SantosJ. Manglano-ArtuñedoZ. PallarèsI. BurdukiewiczM. VenturaS. aSynPEP-DB: A database of biogenic peptides for inhibiting α-synuclein aggregation.Database (Oxford)20232023baad084
    [Google Scholar]
  140. MillerR.G. AndersonF.A.Jr BradleyW.G. BrooksB.R. MitsumotoH. MunsatT.L. RingelS.P. The ALS patient care database: Goals, design, and early results. ALS C.Neurology2000541535710.1212/WNL.54.1.53 10636125
    [Google Scholar]
  141. WroeR. Wai-Ling ButlerA. AndersenP.M. PowellJ.F. Al-ChalabiA. ALSOD: The amyotrophic lateral sclerosis online database.Amyotroph. Lateral Scler.20089424925010.1080/17482960802146106 18608099
    [Google Scholar]
  142. SchultzJ.L. KamholzJ.A. MoserD.J. FeelyS.M.E. PaulsenJ.S. NopoulosP.C. Substance abuse may hasten motor onset of Huntington disease.Neurology201788990991510.1212/WNL.0000000000003661 28148631
    [Google Scholar]
  143. KalathurR.K.R. PedroP.J. SahooB. ChaurasiaG. FutschikM.E. HDNetDB: A Molecular interaction database for network-oriented investigations into Huntington’s disease.Sci. Rep.201771521610.1038/s41598‑017‑05224‑0 28701700
    [Google Scholar]
  144. MearsE.R. HandleyR.R. GrantM.J. ReidS.J. DayB.T. RudigerS.R. McLaughlanC.J. VermaP.J. BawdenS.C. PatassiniS. UnwinR.D. CooperG.J.S. GusellaJ.F. MacDonaldM.E. BrauningR. MacleanP. PearsonJ.F. WaldvogelH.J. FaullR.L.M. SnellR.G. A multi-omic Huntington’s disease transgenic sheep-model database for investigating disease pathogenesis.J. Huntingtons Dis.202110442343410.3233/JHD‑210482 34420978
    [Google Scholar]
  145. LiuY.F. YangU.C. SCA db: Spinocerebellar ataxia candidate gene database.Bioinformatics200420162656266110.1093/bioinformatics/bth305 15217823
    [Google Scholar]
  146. FaruqM. ScariaV. SinghI. TyagiS. SrivastavaA.K. MukerjiM. SCA-LSVD: A repeat-oriented locus-specific variation database for genotype to phenotype correlations in spinocerebellar ataxias.Hum. Mutat.20093071037104210.1002/humu.21006 19370769
    [Google Scholar]
  147. RodriguezS. HugC. TodorovP. MoretN. BoswellS.A. EvansK. ZhouG. JohnsonN.T. HymanB.T. SorgerP.K. AlbersM.W. SokolovA. Machine learning identifies candidates for drug repurposing in Alzheimer’s disease.Nat. Commun.2021121103310.1038/s41467‑021‑21330‑0 33589615
    [Google Scholar]
  148. YueZ. AroraI. ZhangE.Y. LauferV. BridgesS.L. ChenJ.Y. Repositioning drugs by targeting network modules: A Parkinson’s disease case study.BMC Bioinformatics201718S14Suppl. 1453210.1186/s12859‑017‑1889‑0 29297292
    [Google Scholar]
  149. FisconG. ConteF. AmadioS. VolontéC. PaciP. Drug repurposing: A network-based approach to amyotrophic lateral sclerosis.Neurotherapeutics20211831678169110.1007/s13311‑021‑01064‑z 33987813
    [Google Scholar]
  150. GhiamS. EslahchiC. ShahpasandK. Habibi-RezaeiM. GharaghaniS. Identification of repurposed drugs targeting significant long non-coding RNAs in the cross-talk between diabetes mellitus and Alzheimer’s disease.Sci. Rep.20221211833210.1038/s41598‑022‑22822‑9 36316461
    [Google Scholar]
  151. ChyrJ. GongH. ZhouX. DOTA: Deep learning optimal transport approach to advance drug repositioning for Alzheimer’s disease.Biomolecules202212219610.3390/biom12020196 35204697
    [Google Scholar]
  152. WuY. LiuQ. QiuY. XieL. Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized Alzheimer’s disease drug repurposing.PLOS Comput. Biol.2022188e101036710.1371/journal.pcbi.1010367 35951653
    [Google Scholar]
  153. NianY. HuX. ZhangR. FengJ. DuJ. LiF. BuL. ZhangY. ChenY. TaoC. Mining on Alzheimer’s diseases related knowledge graph to identity potential AD-related semantic triples for drug repurposing.BMC Bioinformatics202223S640710.1186/s12859‑022‑04934‑1 36180861
    [Google Scholar]
  154. HaneczokJ. DelijewskiM. MoldzioR. AI molecular property prediction for Parkinson’s Disease reveals potential repurposing drug candidates based on the increase of the expression of PINK1.Comput. Methods Programs Biomed.202324110773110.1016/j.cmpb.2023.107731 37544165
    [Google Scholar]
  155. PaikH. ChungA.Y. ParkH.C. ParkR.W. SukK. KimJ. KimH. LeeK. ButteA.J. Repurpose terbutaline sulfate for amyotrophic lateral sclerosis using electronic medical records.Sci. Rep.201551858010.1038/srep08580 25739475
    [Google Scholar]
  156. PapikinosT. KrokidisM.G. VrahatisA. VlamosP. ExarchosT.P. Signature-based computational drug repurposing for amyotrophic lateral sclerosis.Adv. Exp. Med. Biol.2023142420121110.1007/978‑3‑031‑31982‑2_22 37486495
    [Google Scholar]
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