AIPPT: Predicts anti-inflammatory peptides using the most characteristic subset of bases and sequences by stacking ensemble learning strategies-Reference-Cited by-同舟云学术

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AIPPT: Predicts anti-inflammatory peptides using the most characteristic subset of bases and sequences by stacking ensemble learning strategies

Published:2023-12-05 Issue: Volume:1 Page:23-29
ISSN:
Container-title:2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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Author:

Liu Ruiqi¹,Fu Xiuhao¹,Yan Shankai¹,Zhang Zilong¹,Cui Feifei¹

Affiliation:

1. Hainan University,School of Computer Science and Technology,Haikou,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10385250/10385251/10385565.pdf?arnumber=10385565

Reference41 articles.

1. Molecular mechanisms underlying chronic inflammation-associated cancers

2. Peptides as Therapeutic Agents for Inflammatory-Related Diseases

3. Antimicrobial peptides: Role in human disease and potential as immunotherapies

4. Antiinflammatory peptides: current knowledge and promising prospects

5. Stress Hormones, Proinflammatory and Antiinflammatory Cytokines, and Autoimmunity

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2. PSAC-6mA: 6mA site identifier using self-attention capsule network based on sequence-positioning;Computers in Biology and Medicine;2024-03

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