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.
171 related articles for article (PubMed ID: 39341947)
1. PepNet: an interpretable neural network for anti-inflammatory and antimicrobial peptides prediction using a pre-trained protein language model. Han J; Kong T; Liu J Commun Biol; 2024 Sep; 7(1):1198. PubMed ID: 39341947 [TBL] [Abstract][Full Text] [Related]
2. TriNet: A tri-fusion neural network for the prediction of anticancer and antimicrobial peptides. Zhou W; Liu Y; Li Y; Kong S; Wang W; Ding B; Han J; Mou C; Gao X; Liu J Patterns (N Y); 2023 Mar; 4(3):100702. PubMed ID: 36960450 [TBL] [Abstract][Full Text] [Related]
3. deepAMPNet: a novel antimicrobial peptide predictor employing AlphaFold2 predicted structures and a bi-directional long short-term memory protein language model. Zhao F; Qiu J; Xiang D; Jiao P; Cao Y; Xu Q; Qiao D; Xu H; Cao Y PeerJ; 2024; 12():e17729. PubMed ID: 39040937 [TBL] [Abstract][Full Text] [Related]
4. Accurate de novo peptide sequencing using fully convolutional neural networks. Liu K; Ye Y; Li S; Tang H Nat Commun; 2023 Dec; 14(1):7974. PubMed ID: 38042873 [TBL] [Abstract][Full Text] [Related]
5. An efficient hybrid deep learning architecture for predicting short antimicrobial peptides. Nguyen QH; Nguyen-Vo TH; Do TTT; Nguyen BP Proteomics; 2024 Jul; 24(14):e2300382. PubMed ID: 38837544 [TBL] [Abstract][Full Text] [Related]
6. A novel antibacterial peptide recognition algorithm based on BERT. Zhang Y; Lin J; Zhao L; Zeng X; Liu X Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34037687 [TBL] [Abstract][Full Text] [Related]
7. Comparison of deep learning models with simple method to assess the problem of antimicrobial peptides prediction. Lobanov MY; Slizen MV; Dovidchenko NV; Panfilov AV; Surin AA; Likhachev IV; Galzitskaya OV Mol Inform; 2024 May; 43(5):e202200181. PubMed ID: 36961202 [TBL] [Abstract][Full Text] [Related]
8. AMPActiPred: A three-stage framework for predicting antibacterial peptides and activity levels with deep forest. Yao L; Guan J; Xie P; Chung CR; Deng J; Huang Y; Chiang YC; Lee TY Protein Sci; 2024 Jun; 33(6):e5006. PubMed ID: 38723168 [TBL] [Abstract][Full Text] [Related]
9. Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides. Medina-Ortiz D; Contreras S; Fernández D; Soto-García N; Moya I; Cabas-Mora G; Olivera-Nappa Á Int J Mol Sci; 2024 Aug; 25(16):. PubMed ID: 39201537 [TBL] [Abstract][Full Text] [Related]
10. AMP-RNNpro: a two-stage approach for identification of antimicrobials using probabilistic features. Shaon MSH; Karim T; Sultan MF; Ali MM; Ahmed K; Hasan MZ; Moustafa A; Bui FM; Al-Zahrani FA Sci Rep; 2024 Jun; 14(1):12892. PubMed ID: 38839785 [TBL] [Abstract][Full Text] [Related]
11. TP-LMMSG: a peptide prediction graph neural network incorporating flexible amino acid property representation. Chen N; Yu J; Zhe L; Wang F; Li X; Wong KC Brief Bioinform; 2024 May; 25(4):. PubMed ID: 38920345 [TBL] [Abstract][Full Text] [Related]
12. iAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types. Xiao X; Shao YT; Cheng X; Stamatovic B Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34086856 [TBL] [Abstract][Full Text] [Related]
13. ELASPIC2 (EL2): Combining Contextualized Language Models and Graph Neural Networks to Predict Effects of Mutations. Strokach A; Lu TY; Kim PM J Mol Biol; 2021 May; 433(11):166810. PubMed ID: 33450251 [TBL] [Abstract][Full Text] [Related]
14. Sense the moment: A highly sensitive antimicrobial activity predictor based on hydrophobic moment. Porto WF; Ferreira KCV; Ribeiro SM; Franco OL Biochim Biophys Acta Gen Subj; 2022 Mar; 1866(3):130070. PubMed ID: 34953809 [TBL] [Abstract][Full Text] [Related]