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.
252 related articles for article (PubMed ID: 34330209)
1. Co-AMPpred for in silico-aided predictions of antimicrobial peptides by integrating composition-based features. Singh O; Hsu WL; Su EC BMC Bioinformatics; 2021 Jul; 22(1):389. PubMed ID: 34330209 [TBL] [Abstract][Full Text] [Related]
2. Ensemble-AMPPred: Robust AMP Prediction and Recognition Using the Ensemble Learning Method with a New Hybrid Feature for Differentiating AMPs. Lertampaiporn S; Vorapreeda T; Hongsthong A; Thammarongtham C Genes (Basel); 2021 Jan; 12(2):. PubMed ID: 33494403 [TBL] [Abstract][Full Text] [Related]
3. Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides. Xu J; Li F; Leier A; Xiang D; Shen HH; Marquez Lago TT; Li J; Yu DJ; Song J Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33774670 [TBL] [Abstract][Full Text] [Related]
4. iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities. Xu J; Li F; Li C; Guo X; Landersdorfer C; Shen HH; Peleg AY; Li J; Imoto S; Yao J; Akutsu T; Song J Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37369638 [TBL] [Abstract][Full Text] [Related]
5. Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC. Meher PK; Sahu TK; Saini V; Rao AR Sci Rep; 2017 Feb; 7():42362. PubMed ID: 28205576 [TBL] [Abstract][Full Text] [Related]
6. Machine learning-enabled predictive modeling to precisely identify the antimicrobial peptides. Wani MA; Garg P; Roy KK Med Biol Eng Comput; 2021 Nov; 59(11-12):2397-2408. PubMed ID: 34632545 [TBL] [Abstract][Full Text] [Related]
7. AMP-BERT: Prediction of antimicrobial peptide function based on a BERT model. Lee H; Lee S; Lee I; Nam H Protein Sci; 2023 Jan; 32(1):e4529. PubMed ID: 36461699 [TBL] [Abstract][Full Text] [Related]
8. AMPpred-EL: An effective antimicrobial peptide prediction model based on ensemble learning. Lv H; Yan K; Guo Y; Zou Q; Hesham AE; Liu B Comput Biol Med; 2022 Jul; 146():105577. PubMed ID: 35576825 [TBL] [Abstract][Full Text] [Related]
9. Detecting antimicrobial peptides by exploring the mutual information of their sequences. Tripathi V; Tripathi P J Biomol Struct Dyn; 2020 Oct; 38(17):5037-5043. PubMed ID: 31760879 [TBL] [Abstract][Full Text] [Related]
10. Deep-AmPEP30: Improve Short Antimicrobial Peptides Prediction with Deep Learning. Yan J; Bhadra P; Li A; Sethiya P; Qin L; Tai HK; Wong KH; Siu SWI Mol Ther Nucleic Acids; 2020 Jun; 20():882-894. PubMed ID: 32464552 [TBL] [Abstract][Full Text] [Related]
11. Prediction of antimicrobial peptides toxicity based on their physico-chemical properties using machine learning techniques. Khabbaz H; Karimi-Jafari MH; Saboury AA; BabaAli B BMC Bioinformatics; 2021 Nov; 22(1):549. PubMed ID: 34758751 [TBL] [Abstract][Full Text] [Related]
13. CS-AMPPred: an updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides. Porto WF; Pires ÁS; Franco OL PLoS One; 2012; 7(12):e51444. PubMed ID: 23240023 [TBL] [Abstract][Full Text] [Related]
14. Characterization and Identification of Natural Antimicrobial Peptides on Different Organisms. Chung CR; Jhong JH; Wang Z; Chen S; Wan Y; Horng JT; Lee TY Int J Mol Sci; 2020 Feb; 21(3):. PubMed ID: 32024233 [TBL] [Abstract][Full Text] [Related]
15. AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest. Bhadra P; Yan J; Li J; Fong S; Siu SWI Sci Rep; 2018 Jan; 8(1):1697. PubMed ID: 29374199 [TBL] [Abstract][Full Text] [Related]
16. iASMP: An interpretable in-silico predictive tool focusing on species-specific antimicrobial peptides. Wang Y; Xie Y; Luo Y; Jia P; Wei J; Zhang J; Yan W; Huang J J Pept Sci; 2023 Sep; 29(9):e3490. PubMed ID: 36994602 [TBL] [Abstract][Full Text] [Related]
18. 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]
19. AntiCP 2.0: an updated model for predicting anticancer peptides. Agrawal P; Bhagat D; Mahalwal M; Sharma N; Raghava GPS Brief Bioinform; 2021 May; 22(3):. PubMed ID: 32770192 [TBL] [Abstract][Full Text] [Related]
20. ACEP: improving antimicrobial peptides recognition through automatic feature fusion and amino acid embedding. Fu H; Cao Z; Li M; Wang S BMC Genomics; 2020 Aug; 21(1):597. PubMed ID: 32859150 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]