These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

197 related articles for article (PubMed ID: 36778738)

  • 1. Bitter-RF: A random forest machine model for recognizing bitter peptides.
    Zhang YF; Wang YH; Gu ZF; Pan XR; Li J; Ding H; Zhang Y; Deng KJ
    Front Med (Lausanne); 2023; 10():1052923. PubMed ID: 36778738
    [TBL] [Abstract][Full Text] [Related]  

  • 2. iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features.
    Charoenkwan P; Nantasenamat C; Hasan MM; Moni MA; Lio' P; Shoombuatong W
    Int J Mol Sci; 2021 Aug; 22(16):. PubMed ID: 34445663
    [TBL] [Abstract][Full Text] [Related]  

  • 3. iBitter-SCM: Identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides.
    Charoenkwan P; Yana J; Schaduangrat N; Nantasenamat C; Hasan MM; Shoombuatong W
    Genomics; 2020 Jul; 112(4):2813-2822. PubMed ID: 32234434
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Identify Bitter Peptides by Using Deep Representation Learning Features.
    Jiang J; Lin X; Jiang Y; Jiang L; Lv Z
    Int J Mol Sci; 2022 Jul; 23(14):. PubMed ID: 35887225
    [TBL] [Abstract][Full Text] [Related]  

  • 5. BitterSweetForest: A Random Forest Based Binary Classifier to Predict Bitterness and Sweetness of Chemical Compounds.
    Banerjee P; Preissner R
    Front Chem; 2018; 6():93. PubMed ID: 29696137
    [TBL] [Abstract][Full Text] [Related]  

  • 6. iTTCA-RF: a random forest predictor for tumor T cell antigens.
    Jiao S; Zou Q; Guo H; Shi L
    J Transl Med; 2021 Oct; 19(1):449. PubMed ID: 34706730
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A Random Forest Model for Peptide Classification Based on Virtual Docking Data.
    Feng H; Wang F; Li N; Xu Q; Zheng G; Sun X; Hu M; Xing G; Zhang G
    Int J Mol Sci; 2023 Jul; 24(14):. PubMed ID: 37511165
    [TBL] [Abstract][Full Text] [Related]  

  • 8. BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides.
    Charoenkwan P; Nantasenamat C; Hasan MM; Manavalan B; Shoombuatong W
    Bioinformatics; 2021 Sep; 37(17):2556-2562. PubMed ID: 33638635
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Conserved Sites and Recognition Mechanisms of T1R1 and T2R14 Receptors Revealed by Ensemble Docking and Molecular Descriptors and Fingerprints Combined with Machine Learning.
    Cui Z; Zhang N; Zhou T; Zhou X; Meng H; Yu Y; Zhang Z; Zhang Y; Wang W; Liu Y
    J Agric Food Chem; 2023 Apr; 71(14):5630-5645. PubMed ID: 37005743
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Development and validation of a machine learning-based predictive model for assessing the 90-day prognostic outcome of patients with spontaneous intracerebral hemorrhage.
    Geng Z; Yang C; Zhao Z; Yan Y; Guo T; Liu C; Wu A; Wu X; Wei L; Tian Y; Hu P; Wang K
    J Transl Med; 2024 Mar; 22(1):236. PubMed ID: 38439097
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Identification and prediction of milk-derived bitter taste peptides based on peptidomics technology and machine learning method.
    Yu Y; Liu S; Zhang X; Yu W; Pei X; Liu L; Jin Y
    Food Chem; 2024 Feb; 433():137288. PubMed ID: 37683467
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A multicenter random forest model for effective prognosis prediction in collaborative clinical research network.
    Li J; Tian Y; Zhu Y; Zhou T; Li J; Ding K; Li J
    Artif Intell Med; 2020 Mar; 103():101814. PubMed ID: 32143809
    [TBL] [Abstract][Full Text] [Related]  

  • 13. [Predicting prolonged length of intensive care unit stay
    Wu JY; Lin Y; Lin K; Hu YH; Kong GL
    Beijing Da Xue Xue Bao Yi Xue Ban; 2021 Dec; 53(6):1163-1170. PubMed ID: 34916699
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Mapping Taste-Relevant Food Peptidomes by Means of Sequential Window Acquisition of All Theoretical Fragment Ion-Mass Spectrometry.
    Sebald K; Dunkel A; Hofmann T
    J Agric Food Chem; 2020 Sep; 68(38):10287-10298. PubMed ID: 31508943
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Swin-GA-RF: genetic algorithm-based Swin Transformer and random forest for enhancing cervical cancer classification.
    Alohali MA; El-Rashidy N; Alaklabi S; Elmannai H; Alharbi S; Saleh H
    Front Oncol; 2024; 14():1392301. PubMed ID: 39099689
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Cancer survival classification using integrated data sets and intermediate information.
    Kim S; Park T; Kon M
    Artif Intell Med; 2014 Sep; 62(1):23-31. PubMed ID: 24997860
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation.
    Schaduangrat N; Nantasenamat C; Prachayasittikul V; Shoombuatong W
    Int J Mol Sci; 2019 Nov; 20(22):. PubMed ID: 31731751
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Graph Random Forest: A Graph Embedded Algorithm for Identifying Highly Connected Important Features.
    Tian L; Wu W; Yu T
    Biomolecules; 2023 Jul; 13(7):. PubMed ID: 37509188
    [TBL] [Abstract][Full Text] [Related]  

  • 19. AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest.
    Manavalan B; Shin TH; Kim MO; Lee G
    Front Pharmacol; 2018; 9():276. PubMed ID: 29636690
    [TBL] [Abstract][Full Text] [Related]  

  • 20. CPPred-RF: A Sequence-based Predictor for Identifying Cell-Penetrating Peptides and Their Uptake Efficiency.
    Wei L; Xing P; Su R; Shi G; Ma ZS; Zou Q
    J Proteome Res; 2017 May; 16(5):2044-2053. PubMed ID: 28436664
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.