BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

501 related articles for article (PubMed ID: 36747300)

  • 1. How to approach machine learning-based prediction of drug/compound-target interactions.
    Atas Guvenilir H; Doğan T
    J Cheminform; 2023 Feb; 15(1):16. PubMed ID: 36747300
    [TBL] [Abstract][Full Text] [Related]  

  • 2. MDeePred: novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery.
    Rifaioglu AS; Cetin Atalay R; Cansen Kahraman D; Doğan T; Martin M; Atalay V
    Bioinformatics; 2021 May; 37(5):693-704. PubMed ID: 33067636
    [TBL] [Abstract][Full Text] [Related]  

  • 3. GSL-DTI: Graph structure learning network for Drug-Target interaction prediction.
    E Z; Qiao G; Wang G; Li Y
    Methods; 2024 Mar; 223():136-145. PubMed ID: 38360082
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Unsupervised Representation Learning for Proteochemometric Modeling.
    Kim PT; Winter R; Clevert DA
    Int J Mol Sci; 2021 Nov; 22(23):. PubMed ID: 34884688
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Deep Learning in Drug Target Interaction Prediction: Current and Future Perspectives.
    Abbasi K; Razzaghi P; Poso A; Ghanbari-Ara S; Masoudi-Nejad A
    Curr Med Chem; 2021; 28(11):2100-2113. PubMed ID: 32895036
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A Machine Learning Approach for Drug-target Interaction Prediction using Wrapper Feature Selection and Class Balancing.
    Redkar S; Mondal S; Joseph A; Hareesha KS
    Mol Inform; 2020 May; 39(5):e1900062. PubMed ID: 32003548
    [TBL] [Abstract][Full Text] [Related]  

  • 7. DTi2Vec: Drug-target interaction prediction using network embedding and ensemble learning.
    Thafar MA; Olayan RS; Albaradei S; Bajic VB; Gojobori T; Essack M; Gao X
    J Cheminform; 2021 Sep; 13(1):71. PubMed ID: 34551818
    [TBL] [Abstract][Full Text] [Related]  

  • 8. EmbedDTI: Enhancing the Molecular Representations via Sequence Embedding and Graph Convolutional Network for the Prediction of Drug-Target Interaction.
    Jin Y; Lu J; Shi R; Yang Y
    Biomolecules; 2021 Nov; 11(12):. PubMed ID: 34944427
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Supervised graph co-contrastive learning for drug-target interaction prediction.
    Li Y; Qiao G; Gao X; Wang G
    Bioinformatics; 2022 May; 38(10):2847-2854. PubMed ID: 35561181
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases.
    Rifaioglu AS; Atas H; Martin MJ; Cetin-Atalay R; Atalay V; Doğan T
    Brief Bioinform; 2019 Sep; 20(5):1878-1912. PubMed ID: 30084866
    [TBL] [Abstract][Full Text] [Related]  

  • 11. IMCHGAN: Inductive Matrix Completion With Heterogeneous Graph Attention Networks for Drug-Target Interactions Prediction.
    Li J; Wang J; Lv H; Zhang Z; Wang Z
    IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(2):655-665. PubMed ID: 34115592
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Comparative analysis of network-based approaches and machine learning algorithms for predicting drug-target interactions.
    Jung YS; Kim Y; Cho YR
    Methods; 2022 Feb; 198():19-31. PubMed ID: 34737033
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Efficient machine learning model for predicting drug-target interactions with case study for Covid-19.
    El-Behery H; Attia AF; El-Feshawy N; Torkey H
    Comput Biol Chem; 2021 Aug; 93():107536. PubMed ID: 34271420
    [TBL] [Abstract][Full Text] [Related]  

  • 14. GraphormerDTI: A graph transformer-based approach for drug-target interaction prediction.
    Gao M; Zhang D; Chen Y; Zhang Y; Wang Z; Wang X; Li S; Guo Y; Webb GI; Nguyen ATN; May L; Song J
    Comput Biol Med; 2024 May; 173():108339. PubMed ID: 38547658
    [TBL] [Abstract][Full Text] [Related]  

  • 15. DTI-HeNE: a novel method for drug-target interaction prediction based on heterogeneous network embedding.
    Yue Y; He S
    BMC Bioinformatics; 2021 Sep; 22(1):418. PubMed ID: 34479477
    [TBL] [Abstract][Full Text] [Related]  

  • 16. DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences.
    Lee I; Keum J; Nam H
    PLoS Comput Biol; 2019 Jun; 15(6):e1007129. PubMed ID: 31199797
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Drug-target interaction prediction with tree-ensemble learning and output space reconstruction.
    Pliakos K; Vens C
    BMC Bioinformatics; 2020 Feb; 21(1):49. PubMed ID: 32033537
    [TBL] [Abstract][Full Text] [Related]  

  • 18. DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations.
    Rifaioglu AS; Nalbat E; Atalay V; Martin MJ; Cetin-Atalay R; Doğan T
    Chem Sci; 2020 Mar; 11(9):2531-2557. PubMed ID: 33209251
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Predicting Drug-Target Interactions Based on Small Positive Samples.
    Hu P; Chan KCC; Hu Y
    Curr Protein Pept Sci; 2018; 19(5):479-487. PubMed ID: 27829343
    [TBL] [Abstract][Full Text] [Related]  

  • 20. DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features.
    Chu Y; Kaushik AC; Wang X; Wang W; Zhang Y; Shan X; Salahub DR; Xiong Y; Wei DQ
    Brief Bioinform; 2021 Jan; 22(1):451-462. PubMed ID: 31885041
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 26.