臺灣博碩士論文加值系統

抗菌胜肽是一種寡肽分子，在醫學、食品安全和農業領域具有廣泛的應用價值。然而，抗菌胜肽的活性受到多種因素的影響，因此抗菌胜肽的設計需要大量計算和試驗，並利用開發新的方法以提高設計效率和準確性。

本論文蒐集DBAASP資料庫(Database of Antimicrobial Activity and Structure of Peptides)中抗菌胜肽序列與其針對大腸桿菌(Escherichia coli)與金黃色葡萄球菌(Staphylococcus aureus)的抗菌活性，並進行資料清理。資料清理完成後，計算數據中序列的淨電荷、等電點、疏水性、不穩定性、脂肪族指數和Boman指數，並將其各項理化性質納入數據中，再進行資料前處理。最後，將數據利用線性彈性網絡迴歸(Linear Elastic Net Regression)進行分析與建立預測胜肽活性的迴歸模型，並將其迴歸模型視為最佳化演算法中的目標函數，以作為評估序列活性的基準。線性彈性網絡迴歸分析善於處理複雜的高維度數據，因此在化學、生物和工程等領域得到廣泛應用。

在本研究中，選擇鯨魚最佳化演算法(Whale Optimization Algorithm，WOA) 作為搜索方法，鯨魚最佳化演算法模仿座頭鯨獵食行為用來搜尋最佳的解決方案之仿生最佳化演算法。WOA將座頭鯨的獵食行為分為三種：隨機搜索、包圍補食和氣泡網攻擊，而隨機搜索為探索階段，氣泡網攻擊為開發階段，並利用搜索向量過渡探索階段與開發階段，使WOA具有較強的全局搜索能力。

由於抗菌胜肽設計問題中相鄰的胺基酸之間擁有高度的相關性，也就表示在方法中個體的相鄰維度之間也擁有高度的相關性，並依此性質將WOA進行改良並產生出新的方法：片段式鯨魚最佳化演算法(Fragmented Whale Optimization Algorithm，FWOA)。FWOA以片段的方式更新個體，較不會打亂相鄰維度之間的相關性，所以能夠搜索更佳的解。

FWOA相比WOA搜尋性能更佳，能尋找到預測活性更佳的序列，由此可知FWOA相比WOA更適合應用於抗菌胜肽的設計和搜索，並也更適合應用於個體的相鄰維度之間高相關的問題。本研究有助於協助設計抗菌胜肽，提供一種搜尋方法能快速搜尋到預測活性較高的抗菌胜肽序列，從而減少實驗所需的時間與材料成本，同時也能提高研究的效率與成功率。

Antimicrobial peptides (AMPs) are peptide molecules with wide-ranging applications in fields of medicine, food safety and agriculture. However, the activity of AMPs is influenced by various factors, so the design of AMPs requires extensive calculations and experiments, and the development of new methods is used to improve design efficiency and accuracy.

This study collected the data of AMPs sequences and antibacterial activity against Escherichia coli and Staphylococcus aureus from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP), and performed data cleaning. After the data cleaning was completed, the net charge, isoelectric point, hydrophobicity, instability, aliphatic index and Boman index of the sequence in the data were calculated. These various physicochemical properties were incorporated into the dataset, which was then subjected to data preprocessing. Finally, Elastic Net Regression was used to analyze and establish a regression model for predicting AMPs activity, and the regression model was regarded as the fitness function in the optimization algorithm to serve as a benchmark for evaluating AMPs activity. Elastic Net Regression has been widely used in the fields of chemistry, biology, and engineering due to its ability to handle complex high-dimensional data.

In this study, the Whale Optimization Algorithm (WOA) was selected as the search method. WOA is a biomimetic optimization algorithm that mimics the hunting behavior of whales to search for optimal solutions. WOA categorizes the hunting behavior of whales into three phases: searching for prey, encircling prey, and bubble-net attacking. Among these, searching for prey corresponds to the exploration phase, while bubble-net attacking corresponds to the exploitation phase. WOA employs search vectors to transition between the exploration and exploitation stages, enhancing its global search capability.

In designing AMPs, adjacent amino acids have high correlations, which means that there are also high correlations between adjacent dimensions in individuals of the method. Based on these characteristics, the WOA has been enhanced to generate a new method known as the Fragmented Whale Optimization Algorithm (FWOA). FWOA updates individuals in a fragmented way will not disturb the correlation between adjacent dimensions, so a better solution can be searched.

Compared to WOA, FWOA has better search performance, and can find sequences with better predicted activity. It can be seen that FWOA is more suitable for the design and search of AMPs than WOA, and it is also more suitable for the problem of high correlation between adjacent dimensions of individuals. This study helps to assist in the design of AMPs, and provides a search method that can quickly search for AMPs sequences with high predicted activity, thereby reducing the time and material costs required for experiments, and at the same time improving the efficiency and success of research rate.

中文摘要...................................................................I
英文摘要.................................................................III
誌謝.......................................................................V
目錄......................................................................VI
表目錄....................................................................IX
圖目錄.....................................................................X
符號說明..................................................................XI
壹、 緒論...............................................................1
一、 背景...............................................................1
(一) 抗菌胜肽的應用..............................................2
(二) 抗菌胜肽的設計..............................................3
(三) 抗菌胜肽的實驗測試..........................................3
二、 動機...............................................................4
三、 目的...............................................................5
四、 論文結構...........................................................6
貳、 文獻探討...........................................................7
一、 抗菌胜肽...........................................................7
(一) 抗菌胜肽的作用機制..........................................7
(二) 設計抗菌胜肽的參數..........................................9
二、 迴歸分析..........................................................12
(一) 線性迴歸分析...............................................12
(二) 邏輯迴歸分析...............................................13
(三) 非線性迴歸分析.............................................13
(四) 縱向資料迴歸分析...........................................13
(五) 資料前處理.................................................14
三、 最佳化演算法.......................................................15
(一) 基因演算法（Genetic Algorithm，GA）........................15
(二) 粒子群最佳化演算法（Particle Swarm Optimization，PSO）......16
(三) 模擬退火演算法（Simulated Annealing，SA）...................16
(四) 算術最佳化演算法(Arithmetic Optimization Algorithm, AOA)....17
(五) 人工蜂群演算法（Artificial Bee Colony，ABC）................18
(六) 差分進化演算法（Differential Evolution，DE）................18
(七) 蟻群最佳化演算法（Ant Colony Optimization，ACO）............19
(八) 鯨魚最佳化演算法(Whale Optimization Algorithm，WOA).........20
四、 抗菌胜肽資料庫......................................................21
五、 利用機械學習方法設計抗菌胜肽.........................................22
參、 研究設計與方法......................................................23
一、 抗菌胜肽各項性質計算方法.............................................23
(一) 淨電荷(Net Charge).........................................23
(二) 等電點(Isoelectric Point)..................................23
(三) 疏水性(Hydrophilicity).....................................24
(四) 不穩定性(Instability Index)................................25
(五) 脂肪族指數(Aliphatic Index)................................28
(六) Boman指數(Boman index).....................................28
二、 線性彈性網路迴歸分析................................................30
(一) 數據.......................................................30
(二) Lasso迴歸分析..............................................33
(三) Ridge迴歸分析..............................................34
(四) 線性彈性網路迴歸分析........................................36
(五) 皮爾森相關係數..............................................38
三、 鯨魚最佳化演算法....................................................39
(一) 設計編碼與演算法參數.........................................41
(二) 初始化族群..................................................43
(三) 評估適應度函數..............................................44
(四) 更新最佳候選解..............................................45
(五) 更新鯨群的位置..............................................46
(六) 檢查是否合理或超出邊界.......................................50
(七) 檢查是否達成終止條件.........................................50
四、 片段式鯨魚最佳化演算法...............................................52
(一) 設計編碼與演算法參數.........................................55
(二) 初始化族群..................................................58
(三) 評估適應度函數..............................................58
(四) 更新最佳解集合..............................................59
(五) 更新鯨群的位置..............................................60
(六) 檢查是否達成終止條件.........................................66
肆、 研究結果............................................................68
一、 鯨魚最佳化演算法與片段式鯨魚最佳化演算法參數比較.......................68
(一) 篩選條件....................................................68
(二) 個體維度數、族群大小與最大迭代次數............................68
(三) 控制探索階段或開發階段之參數.................................68
(四) 探索階段與開發階段之學習對象.................................69
(五) 片段之參數..................................................69
二、 鯨魚最佳化演算法與片段式鯨魚最佳化演算法的比較.........................71
(一) 平均執行時間與平均胜肽抗菌活性預測值..........................71
(二) 鯨魚最佳化演算法與片段式鯨魚最佳化演算法迭代圖比較.............75
伍、 結論...............................................................78
一、 結論...............................................................78
二、 建議與討論..........................................................79
參考文獻....................................................................80
附錄一......................................................................86
附錄二......................................................................99

1.Frieri, M., K. Kumar, and A. Boutin, Antibiotic resistance. Journal of infection and public health, 2017. 10(4): p. 369-378.
2.Bahar, A.A. and D. Ren, Antimicrobial peptides. Pharmaceuticals, 2013. 6(12): p. 1543-1575.
3.Lazzaro, B.P., M. Zasloff, and J. Rolff, Antimicrobial peptides: Application informed by evolution. Science, 2020. 368(6490): p. eaau5480.
4.Stachurski, O., et al., Understanding the Role of Self-Assembly and Interaction with Biological Membranes of Short Cationic Lipopeptides in the Effective Design of New Antibiotics. Antibiotics, 2022. 11(11): p. 1491.
5.Lopes, B.S., et al., The role of antimicrobial peptides as antimicrobial and antibiofilm agents in tackling the silent pandemic of antimicrobial resistance. Molecules, 2022. 27(9): p. 2995.
6.Lei, J., et al., The antimicrobial peptides and their potential clinical applications. American journal of translational research, 2019. 11(7): p. 3919.
7.Todorov, S., B. de Melo Franco, and J. Tagg, Bacteriocins of Gram-positive bacteria having activity spectra extending beyond closely-related species. Beneficial Microbes, 2019. 10(3): p. 315-328.
8.Guith, T., et al., An overview of antimicrobial peptides. Viral, Parasitic, Bacterial, and Fungal Infections, 2023: p. 101-106.
9.Liu, Y., et al., Nonribosomal antibacterial peptides that target multidrug-resistant bacteria. Natural product reports, 2019. 36(4): p. 573-592.
10.Wang, J., et al., Antimicrobial peptides: Promising alternatives in the post feeding antibiotic era. Medicinal research reviews, 2019. 39(3): p. 831-859.
11.Memariani, H. and M. Memariani, Antibiofilm properties of cathelicidin LL-37: An in-depth review. World Journal of Microbiology and Biotechnology, 2023. 39(4): p. 1-28.
12.Ridyard, K.E. and J. Overhage, The potential of human peptide LL-37 as an antimicrobial and anti-biofilm agent. Antibiotics, 2021. 10(6): p. 650.
13.Wang, G., et al., Design of antimicrobial peptides: progress made with human cathelicidin LL-37. Antimicrobial Peptides: Basics for Clinical Application, 2019: p. 215-240.
14.Reddy, K., R. Yedery, and C. Aranha, Antimicrobial peptides: premises and promises. International journal of antimicrobial agents, 2004. 24(6): p. 536-547.
15.Leon Madrazo, A. and M.R. Segura Campos, Review of antimicrobial peptides as promoters of food safety: Limitations and possibilities within the food industry. Journal of Food Safety, 2020. 40(6): p. e12854.
16.Makroo, H., F. Naqash, and S. Fayaz, Antimicrobial compounds to improve foods’ shelf life. Food Waste Recovery, 2021: p. 461-482.
17.Liu, Y., et al., Antimicrobial peptides and their application in food packaging. Trends in food science & technology, 2021. 112: p. 471-483.
18.Akram, A. and G. McCann, Effect of antimicrobial peptides and chemicals produced by animals on Aspergillus fumigatus. Advances in Bioscience and Biotechnology, 2021. 12(6): p. 173-192.
19.Yun, H.-S., et al., A Study on the Use of Human Antibacterial Peptide LL-37-derived FK-13 as a Cosmetic Preservative. Journal of the Korean Applied Science and Technology, 2021. 38(6): p. 1568-1576.
20.Li, X., et al., Novel approaches to combat medical device-associated biofilms. Coatings, 2021. 11(3): p. 294.
21.Batoni, G., et al., Use of antimicrobial peptides against microbial biofilms: advantages and limits. Current medicinal chemistry, 2011. 18(2): p. 256-279.
22.Chou, S., et al., Short, symmetric-helical peptides have narrow-spectrum activity with low resistance potential and high selectivity. Biomaterials science, 2019. 7(6): p. 2394-2409.
23.Lu, C., et al., Molecular Architecture and Charging Effects Enhance the In Vitro and In Vivo Performance of Multi‐Arm Antimicrobial Agents Based on Star‐Shaped Poly (l‐lysine). Advanced Therapeutics, 2019. 2(12): p. 1900147.
24.Li, J., et al., Plant antimicrobial peptides: structures, functions, and applications. Botanical Studies, 2021. 62(1): p. 1-15.
25.Tian, T., et al., Industrial application of antimicrobial peptides based on their biological activity and structure-activity relationship. Critical Reviews in Food Science and Nutrition, 2021: p. 1-16.
26.Wang, X., et al., Stimuli‐Responsive antibacterial materials: molecular structures, design principles, and biomedical applications. Advanced Science, 2022. 9(13): p. 2104843.
27.Magana, M., et al., The value of antimicrobial peptides in the age of resistance. The Lancet Infectious Diseases, 2020. 20(9): p. e216-e230.
28.Kumar, P., et al., Antibacterial properties of graphene-based nanomaterials. Nanomaterials, 2019. 9(5): p. 737.
29.Lotfy, W.A., et al., Biosynthesis of silver nanoparticles by Aspergillus terreus: characterization, optimization, and biological activities. Frontiers in bioengineering and biotechnology, 2021. 9: p. 633468.
30.Chen, C.H., et al., Synthetic molecular evolution of antimicrobial peptides. Current Opinion in Biotechnology, 2022. 75: p. 102718.
31.Benfield, A.H. and S.T. Henriques, Mode-of-action of antimicrobial peptides: Membrane disruption vs. intracellular mechanisms. Frontiers in Medical Technology, 2020. 2: p. 610997.
32.Shi, S.M. and L. Di, Strategies to Optimize Peptide Stability and Prolong Half-Life, in Peptide Therapeutics: Fundamentals of Design, Development, and Delivery. 2022, Springer. p. 163-182.
33.Malmsten, M., Antimicrobial peptides. Upsala journal of medical sciences, 2014. 119(2): p. 199-204.
34.Geitani, R., et al., Expression and roles of antimicrobial peptides in innate defense of airway mucosa: potential implication in cystic fibrosis. Frontiers in immunology, 2020. 11: p. 1198.
35.Luo, Y. and Y. Song, Mechanism of antimicrobial peptides: Antimicrobial, anti-inflammatory and antibiofilm activities. International Journal of Molecular Sciences, 2021. 22(21): p. 11401.
36.Zhu, Y., et al., Antimicrobial peptides, conventional antibiotics, and their synergistic utility for the treatment of drug‐resistant infections. Medicinal Research Reviews, 2022. 42(4): p. 1377-1422.
37.Chen, Y.-C., et al., A novel antimicrobial peptide Sparamosin26–54 from the mud crab Scylla paramamosain showing potent antifungal activity against Cryptococcus neoformans. Frontiers in Microbiology, 2021. 12: p. 746006.
38.Wojciechowska, M., et al., Antibacterial peptide nucleic acids—Facts and perspectives. Molecules, 2020. 25(3): p. 559.
39.Pereira, P.R., C.S. Freitas, and V.M. Paschoalin, Saccharomyces cerevisiae biomass as a source of next‐generation food preservatives: Evaluating potential proteins as a source of antimicrobial peptides. Comprehensive reviews in food science and food safety, 2021. 20(5): p. 4450-4479.
40.Xuan, J., et al., Antimicrobial peptides for combating drug-resistant bacterial infections. Drug Resistance Updates, 2023: p. 100954.
41.Ciumac, D., et al., Membrane targeting cationic antimicrobial peptides. Journal of colloid and interface science, 2019. 537: p. 163-185.
42.Zou, F., et al., Plant antimicrobial peptides: A comprehensive review of their classification, production, mode of action, functions, applications, and challenges. Food & Function, 2023.
43.Zhang, S., et al., Structure and Formation Mechanism of Antimicrobial Peptides Temporin B-and L-Induced Tubular Membrane Protrusion. International Journal of Molecular Sciences, 2021. 22(20): p. 11015.
44.Ahmed, T.A. and R. Hammami, Recent insights into structure–function relationships of antimicrobial peptides. Journal of food biochemistry, 2019. 43(1): p. e12546.
45.Cardoso, M.H., et al., Non-lytic antibacterial peptides that translocate through bacterial membranes to act on intracellular targets. International Journal of Molecular Sciences, 2019. 20(19): p. 4877.
46.Qi, S., B. Gao, and S. Zhu, A fungal defensin inhibiting bacterial cell-wall biosynthesis with non-hemolysis and serum stability. Journal of Fungi, 2022. 8(2): p. 174.
47.Gao, X., et al., Defensins: The natural peptide antibiotic. Advanced drug delivery reviews, 2021. 179: p. 114008.
48.Piller, P., et al., Membrane activity of LL-37 derived antimicrobial peptides against Enterococcus hirae: superiority of SAAP-148 over OP-145. Biomolecules, 2022. 12(4): p. 523.
49.Guryanova, S.V. and T.V. Ovchinnikova, Immunomodulatory and allergenic properties of antimicrobial peptides. International Journal of Molecular Sciences, 2022. 23(5): p. 2499.
50.Duarte-Mata, D.I. and M.C. Salinas-Carmona, Antimicrobial peptides´ immune modulation role in intracellular bacterial infection. Frontiers in Immunology, 2023. 14.
51.Zhang, Q., et al., Role of antimicrobial peptide cathelicidin in thrombosis and thromboinflammation. Frontiers in Immunology, 2023. 14.
52.Pen, G., et al., A review on the use of antimicrobial peptides to combat porcine viruses. Antibiotics, 2020. 9(11): p. 801.
53.Ruiz Puentes, P., et al., Rational discovery of antimicrobial peptides by means of artificial intelligence. Membranes, 2022. 12(7): p. 708.
54.Renaud, S. and R.A. Mansbach, Latent spaces for antimicrobial peptide design. Digital Discovery, 2023. 2(2): p. 441-458.
55.Jiang, Z., et al., Effects of net charge and the number of positively charged residues on the biological activity of amphipathic α‐helical cationic antimicrobial peptides. Peptide Science, 2008. 90(3): p. 369-383.
56.Shimura, K., et al., Accuracy in the determination of isoelectric points of some proteins and a peptide by capillary isoelectric focusing: utility of synthetic peptides as isoelectric point markers. Analytical chemistry, 2000. 72(19): p. 4747-4757.
57.Chen, Y., et al., Role of peptide hydrophobicity in the mechanism of action of α-helical antimicrobial peptides. Antimicrobial agents and chemotherapy, 2007. 51(4): p. 1398-1406.
58.Juvvadi, P., et al., Hydrophobic Effects on Antibacterial and Channel‐forming Properties of Cecropin A–Melittin Hybrids. Journal of peptide science: an official publication of the European Peptide Society, 1996. 2(4): p. 223-232.
59.Strömstedt, A.A., et al., Evaluation of strategies for improving proteolytic resistance of antimicrobial peptides by using variants of EFK17, an internal segment of LL-37. Antimicrobial agents and chemotherapy, 2009. 53(2): p. 593-602.
60.Cherkasov, A., et al., Use of artificial intelligence in the design of small peptide antibiotics effective against a broad spectrum of highly antibiotic-resistant superbugs. ACS chemical biology, 2009. 4(1): p. 65-74.
61.Osorio, D., P. Rondón-Villarreal, and R. Torres, Peptides: a package for data mining of antimicrobial peptides. Small, 2015. 12: p. 44-444.
62.Boman, H.G., Antibacterial peptides: basic facts and emerging concepts. Journal of internal medicine, 2003. 254(3): p. 197-215.
63.Nunkoo, R., et al., Tourism and economic growth: A meta-regression analysis. Journal of Travel Research, 2020. 59(3): p. 404-423.
64.Moitra, M., et al., Estimating the risk of suicide associated with mental disorders: A systematic review and meta-regression analysis. Journal of psychiatric research, 2021. 137: p. 242-249.
65.Mas' ud, A., N.A. Abd Manaf, and N. Saad, Trust and power as predictors of tax compliance: global evidence. Economics & Sociology, 2019. 12(2): p. 192-204.
66.Jiang, R., et al., A medical big data access control model based on fuzzy trust prediction and regression analysis. Applied Soft Computing, 2022. 117: p. 108423.
67.Keunecke, J.G., et al., Workload and influencing factors in non-emergency medical transfers: a multiple linear regression analysis of a cross-sectional questionnaire study. BMC health services research, 2019. 19(1): p. 1-11.
68.Qiu, S., L.M. Dooley, and L. Xie, How servant leadership and self-efficacy interact to affect service quality in the hospitality industry: A polynomial regression with response surface analysis. Tourism Management, 2020. 78: p. 104051.
69.Vyas, U.B., et al., Gaussian exponential regression method for modeling open circuit voltage of lithium-ion battery as a function of state of charge. COMPEL-The international journal for computation and mathematics in electrical and electronic engineering, 2021. 41(1): p. 64-80.
70.Liu, S., et al., Prediction of gene expression patterns with generalized linear regression model. Frontiers in Genetics, 2019. 10: p. 120.
71.Wang, R., N. Xiu, and C. Zhang, Greedy projected gradient-Newton method for sparse logistic regression. IEEE transactions on neural networks and learning systems, 2019. 31(2): p. 527-538.
72.Maulud, D. and A.M. Abdulazeez, A review on linear regression comprehensive in machine learning. Journal of Applied Science and Technology Trends, 2020. 1(4): p. 140-147.
73.Jenul, A., et al., Rent—repeated elastic net technique for feature selection. IEEE Access, 2021. 9: p. 152333-152346.
74.Torang, A., P. Gupta, and D.J. Klinke, An elastic-net logistic regression approach to generate classifiers and gene signatures for types of immune cells and T helper cell subsets. BMC bioinformatics, 2019. 20(1): p. 1-15.
75.Madhuri, C.R., G. Anuradha, and M.V. Pujitha. House price prediction using regression techniques: A comparative study. in 2019 International conference on smart structures and systems (ICSSS). 2019. IEEE.
76.Wang, M. and F. Vermeulen, Life between buildings from a street view image: What do big data analytics reveal about neighbourhood organisational vitality? Urban Studies, 2021. 58(15): p. 3118-3139.
77.Putka, D.J., et al., Evaluating a natural language processing approach to estimating KSA and interest job analysis ratings. Journal of Business and Psychology, 2022: p. 1-26.
78.Abuhashesh, M.Y., et al., The effect of culture on customers’ attitudes toward Facebook advertising: the moderating role of gender. Review of International Business and Strategy, 2021. 31(3): p. 416-437.
79.Dumitrescu, E., et al., Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects. European Journal of Operational Research, 2022. 297(3): p. 1178-1192.
80.Zhu, C., C.U. Idemudia, and W. Feng, Improved logistic regression model for diabetes prediction by integrating PCA and K-means techniques. Informatics in Medicine Unlocked, 2019. 17: p. 100179.
81.Nusinovici, S., et al., Logistic regression was as good as machine learning for predicting major chronic diseases. Journal of clinical epidemiology, 2020. 122: p. 56-69.
82.Senaviratna, N. and T. A Cooray, Diagnosing multicollinearity of logistic regression model. Asian Journal of Probability and Statistics, 2019. 5(2): p. 1-9.
83.Karimipour, A., et al., A novel nonlinear regression model of SVR as a substitute for ANN to predict conductivity of MWCNT-CuO/water hybrid nanofluid based on empirical data. Physica A: Statistical Mechanics and its Applications, 2019. 521: p. 89-97.
84.Cheng, L., et al., An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data. Nature communications, 2019. 10(1): p. 1798.
85.Abualigah, L. and A. Diabat, A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications. Neural Computing and Applications, 2020. 32(19): p. 15533-15556.
86.Katoch, S., S.S. Chauhan, and V. Kumar, A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 2021. 80: p. 8091-8126.
87.Lalwani, S., et al., A survey on parallel particle swarm optimization algorithms. Arabian Journal for Science and Engineering, 2019. 44: p. 2899-2923.
88.Elgamal, Z.M., et al., An improved harris hawks optimization algorithm with simulated annealing for feature selection in the medical field. IEEE access, 2020. 8: p. 186638-186652.
89.Abualigah, L., et al., The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering, 2021. 376: p. 113609.
90.Sharma, A., et al., A review on artificial bee colony and it’s engineering applications. Journal of Critical Reviews, 2020. 7(11): p. 4097-4107.
91.Pant, M., et al., Differential Evolution: A review of more than two decades of research. Engineering Applications of Artificial Intelligence, 2020. 90: p. 103479.
92.Deng, W., J. Xu, and H. Zhao, An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem. IEEE access, 2019. 7: p. 20281-20292.
93.Gharehchopogh, F.S. and H. Gholizadeh, A comprehensive survey: Whale Optimization Algorithm and its applications. Swarm and Evolutionary Computation, 2019. 48: p. 1-24.
94.Rana, N., et al., Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments. Neural Computing and Applications, 2020. 32: p. 16245-16277.
95.Pirtskhalava, M., et al., DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics. Nucleic acids research, 2021. 49(D1): p. D288-D297.
96.Wang, G., X. Li, and Z. Wang, APD3: the antimicrobial peptide database as a tool for research and education. Nucleic acids research, 2016. 44(D1): p. D1087-D1093.
97.Gawde, U., et al., CAMPR4: a database of natural and synthetic antimicrobial peptides. Nucleic Acids Research, 2023. 51(D1): p. D377-D383.
98.Alimbarashvili, E., et al. DATABASE OF ANTIMICROBIAL ACTIVITY AND STRUCTURE OF PEPTIDES (DBAASP)-FINDING A WAY OUT OF MICROBIAL RESISTANCE. in XXXVIII Symposium of Bioinformatics and Computer-Aided Drug Discovery. 2022.
99.Wang, G., I.I. Vaisman, and M.L. van Hoek, Machine learning prediction of antimicrobial peptides, in Computational Peptide Science: Methods and Protocols. 2022, Springer. p. 1-37.
100.Xiao, X., et al., iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Analytical biochemistry, 2013. 436(2): p. 168-177.
101.Kavousi, K., et al., IAMPE: NMR-assisted computational prediction of antimicrobial peptides. Journal of Chemical Information and Modeling, 2020. 60(10): p. 4691-4701.
102.Guruprasad, K., B.B. Reddy, and M.W. Pandit, Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Engineering, Design and Selection, 1990. 4(2): p. 155-161.
103.Ranstam, J. and J. Cook, LASSO regression. Journal of British Surgery, 2018. 105(10): p. 1348-1348.
104.McDonald, G.C., Ridge regression. Wiley Interdisciplinary Reviews: Computational Statistics, 2009. 1(1): p. 93-100.
105.Mirjalili, S. and A. Lewis, The whale optimization algorithm. Advances in engineering software, 2016. 95: p. 51-67.
106.Jin, H., et al., Eagle strategy using uniform mutation and modified whale optimization algorithm for QoS-aware cloud service composition. Applied Soft Computing, 2022. 114: p. 108053.
107.Lin, T.-T., et al., AI4AMP: an antimicrobial Peptide predictor using physicochemical property-Based encoding method and deep learning. Msystems, 2021. 6(6): p. e00299-21.