통합자연과학논문집 (Journal of Integrative Natural Science)

조선대학교 기초과학연구원 (The Basic Science Institute Chosun University)
권호 표지
DOI QR코드

DOI QR Code

Machine learning Anti-inflammatory Peptides Role in Recent Drug Discovery

  • Subathra Selvam (Computational Biology Laboratory, Department of Genetic Engineering, SRM Institute of Science and Technology)
  • 투고 : 2024.01.19
  • 심사 : 2024.03.11
  • 발행 : 2024.03.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Several anti-inflammatory small molecules have been found in the process of the inflammatory response, and these small molecules have been used to treat some inflammatory and autoimmune diseases. Numerous tools for predicting anti-inflammatory peptides (AIPs) have emerged in recent years. However, conducting experimental validations in the lab is both resource-intensive and time-consuming. Current therapies for inflammatory and autoimmune disorders often involve nonspecific anti-inflammatory drugs and immunosuppressants, often with potential side effects. AIPs have been used in treating inflammatory illnesses like Alzheimer's disease and can limit the expression of inflammatory promoters. Recent advances in adverse incident predictions (AIPs) have been made, but it is crucial to acknowledge limitations and imperfections in existing methodologies.

키워드

참고문헌

  1. Basith S, Manavalan B, Hwan Shin T, Lee G., "Machine intelligence in peptide therapeutics: A next-generation tool for rapid disease screening.", Med Res Rev Vol. 40, pp. 1276-314, 2020. https://doi.org/10.1002/med.21658.
  2. Attique M, Farooq MS, Khelifi A, Abid A., "Prediction of Therapeutic Peptides Using Machine Learning: Computational Models, Datasets, and Feature Encodings.", IEEE Access Vol. 8, pp. 148570-94, 2020. https://doi.org/10.1109/ACCESS.2020.3015792
  3. Khatun MS, Hasan MM, Kurata H., "PreAIP: Computational Prediction of Anti-inflammatory Peptides by Integrating Multiple Complementary Features.", Front Genet Vol. 10, p. 129, 2019. https://doi.org/10.3389/fgene.2019.00129.
  4. Zhao D, Teng Z, Li Y, Chen D., "iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest.", Front Genet Vol. 12, pp. 1-9, 2021. https://doi.org/10.3389/fgene.2021.773202.
  5. Gupta S, Sharma AK, Shastri V, Madhu MK, Sharma VK., "Prediction of anti-inflammatory proteins/peptides: An insilico approach.", J Transl Med, Vol. 15, pp. 1-11, 2017. https://doi.org/10.1186/s12967-016-1103-6.
  6. Fahad Alotaibi Muhammad Attique YDK., "AntiFlamPred: An Anti-Inflammatory Peptide Predictor for Drug Selection Strategies.", Comput Mater \& Contin Vol. 69, pp. 1039-55, 2021. https://doi.org/10.32604/cmc.2021.017297.
  7. Shaker B, Yu M-S, Song JS, Ahn S, Ryu JY, Oh K-S, et al., "LightBBB: computational prediction model of blood-brain-barrier penetration based on LightGBM.", Bioinformatics, Vol. 37, pp. 1135-9, 2021. https://doi.org/10.1093/bioinformatics/btaa918.
  8. Breiman L, Friedman JH, Olshen RA, Stone CJ., "Classification and Regression Trees", 1984.
  9. Bansal M, Goyal A, Choudhary A., "A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning.", Decis Anal J, Vol. 3, p. 100071, 2021. https://doi.org/https://doi.org/10.1016/j.dajour.2022.100071.
  10. Chen T, Guestrin C., "XGBoost: A scalable tree boosting system.", Proc ACM SIGKDD Int Conf Knowl Discov Data Min 2016;13-17-Augu:785-94. https://doi.org/10.1145/2939672.2939785.
  11. Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, et al., "LightGBM: A highly efficient gradient boosting decision tree.", Adv Neural Inf Process Syst 2017;2017-Decem:3147-55.
  12. Kabiraj S, Raihan M, Alvi N, Afrin M, Akter L, Sohagi SA, et al. Breast Cancer Risk Prediction using XGBoost and Random Forest Algorithm. 2020 11th Int. Conf. Comput. Commun. Netw. Technol., 2020, p. 1-4. https://doi.org/10.1109/ICCCNT49239.2020.9225451.
  13. Prusty S, Patnaik S, Dash SK., "SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer.", Front Nanotechnol, Vol. 4, pp. 1-12, 2022. https://doi.org/10.3389/fnano.2022.972421.
  14. Refaeilzadeh P, Tang L, Liu H., "Cross-Validation. In: LIU L, OZSU MT, editors.", Encycl. Database Syst., Boston, MA: Springer US; pp. 532-8, 2009. https://doi.org/10.1007/978-0-387-39940-9_565.
  15. Wahi D, Jamal S, Goyal S, Singh A, Jain R, Rana P, et al., "Cheminformatics models based on machine learning approaches for design of USP1/UAF1 abrogators as anticancer agents.", Syst Synth Biol, Vol. 9, pp. 33-43, 2015. https://doi.org/10.1007/s11693-015-9162-1.
  16. Manavalan B, Shin TH, Kim MO, Lee G., "AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest.", Front Pharmacol, Vol. 9, p. 276, 2018. https://doi.org/10.3389/fphar.2018.00276.
  17. Khatun MS, Hasan MM, Kurata H., "PreAIP: Computational prediction of anti-inflammatory peptides by integrating multiple complementary features.", Front Genet, Vol. 10, 2019. https://doi.org/10.3389/fgene.2019.00129.
  18. Wei L, Zhou C, Chen H, Song J, Su R., "ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides.", Bioinformatics, Vol. 34, pp. 4007-16, 2018. https://doi.org/10.1093/bioinformatics/bty451.
  19. Deng H, Lou C, Wu Z, Li W, Liu G, Tang Y., "Prediction of anti-inflammatory peptides by a sequence-based stacking ensemble model named AIPStack.", IScience, Vol. 25, p. 104967, 2022. https://doi.org/https://doi.org/10.1016/j.isci.2022.104967.
  20. Zhang J, Zhang Z, Pu L, Tang J, Guo F., "AIEpred: An Ensemble Predictive Model of Classifier Chain to Identify Anti-Inflammatory Peptides.", IEEE/ACM Trans Comput Biol Bioinforma, Vol. 18, pp. 1831-40, 2021. https://doi.org/10.1109/TCBB.2020.2968419.
  21. Guo Y, Yan K, LV H, Liu B., "PreTP-EL: prediction of therapeutic peptides based on ensemble learning." Brief Bioinform, Vol. 22, bbab358, 2021. https://doi.org/10.1093/bib/bbab358.
  22. Shaker B, Yu MS, Song JS, Ahn S, Ryu JY, Oh KS, et al., "LightBBB: Computational prediction model of blood-brain-barrier penetration based on LightGBM.", Bioinformatics, Vol. 37, pp. 1135-9, 2021. https://doi.org/10.1093/bioinformatics/btaa918.