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Enhanced prediction of hemolytic activity in antimicrobial peptides using deep learning-based sequence analysis.

• Authors : Abdelbaky, Ibrahim¹ (AUTHOR) ; Elhakeem, Mohamed¹ (AUTHOR) ; Tayara, Hilal² (AUTHOR)

• Source: BMC Bioinformatics. 11/27/2024, Vol. 25 Issue 1, p1-16. 16p.

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AMPDeep: hemolytic activity prediction of antimicrobial peptides using transfer learning.

• Authors : Salem, Milad¹ (AUTHOR) ; Keshavarzi Arshadi, Arash² (AUTHOR); Yuan, Jiann Shiun¹ (AUTHOR)

• Source: BMC Bioinformatics. 9/26/2022, Vol. 23 Issue 1, p1-17. 17p.

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Prediction of antimicrobial peptides toxicity based on their physico-chemical properties using machine learning techniques.

• Authors : Khabbaz, Hossein¹ (AUTHOR); Karimi-Jafari, Mohammad Hossein¹ (AUTHOR); Saboury, Ali Akbar¹ (AUTHOR)

• Source: BMC Bioinformatics. 11/10/2021, Vol. 22 Issue 1, p1-11. 11p.

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Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides.

• Authors : Boone, Kyle¹ (AUTHOR); Wisdom, Cate¹ (AUTHOR); Camarda, Kyle² (AUTHOR)

• Source: BMC Bioinformatics. 5/11/2021, Vol. 22 Issue 1, p1-17. 17p.

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Co-AMPpred for in silico-aided predictions of antimicrobial peptides by integrating composition-based features.

• Authors : Singh, Onkar^1,2,3 (AUTHOR); Hsu, Wen-Lian^1,2 (AUTHOR); Su, Emily Chia-Yu^3,4 (AUTHOR)

• Source: BMC Bioinformatics. 7/30/2021, Vol. 22 Issue 1, p1-21. 21p.

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UniAMP: enhancing AMP prediction using deep neural networks with inferred information of peptides.

• Authors : Chen Z; College of Artificial Intelligence, Nanjing Agricultural University, Weigang No.1, Nanjing, 210095, Jiangsu, China.; Ji C

Subjects: Deep Learning* ; Neural Networks, Computer* ; Antimicrobial Peptides*/Antimicrobial Peptides*/Antimicrobial Peptides*/chemistry

• Source: BMC bioinformatics [BMC Bioinformatics] 2025 Jan 11; Vol. 26 (1), pp. 10. Date of Electronic Publication: 2025 Jan 11.Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105

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An advanced approach to identify antimicrobial peptides and their function types for penaeus through machine learning strategies.

• Authors : Lin, Yuan^1,2 ; Cai, Yinyin¹ ; Liu, Juan³

• Source: BMC Bioinformatics. 6/10/2019 Supplement 8, Vol. 20 Issue 8, p1-10. 10p. 1 Diagram, 6 Charts, 2 Graphs.

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Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model.

• Authors : Akbar, Shahid^1,2 (AUTHOR); Raza, Ali³ (AUTHOR); Zou, Quan^1,4 (AUTHOR)

• Source: BMC Bioinformatics. 3/7/2024, Vol. 25 Issue 1, p1-16. 16p.

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BADASS: BActeriocin-Diversity ASsessment Software.

• Authors : Costa, Sávio S.¹ (AUTHOR) ; da Silva Moia, Gislenne² (AUTHOR); Silva, Artur¹ (AUTHOR)

• Source: BMC Bioinformatics. 1/20/2023, Vol. 24 Issue 1, p1-11. 11p.

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CL-ACP: a parallel combination of CNN and LSTM anticancer peptide recognition model.

• Authors : Wang, Huiqing¹ (AUTHOR); Zhao, Jian¹ (AUTHOR) ; Zhao, Hong¹ (AUTHOR)

• Source: BMC Bioinformatics. 10/20/2021, Vol. 22 Issue 1, p1-22. 22p.

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