Phaniendra A, Jestadi DB, Periyasamy L. Free radicals: properties, sources, targets, and their implication in various diseases. Indian J Clin Biochem. 2015;30:11–26.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Sundaram Sanjay S, Shukla AK. Free radicals versus antioxidants. In: Sanjay SS, Shukla AK, editors. Potential therapeutic applications of nano-antioxidants. Springer: Singapore; 2021. p. 1–17.ChapterÂ
Google ScholarÂ
Nimse SB, Pal D. Free radicals, natural antioxidants, and their reaction mechanisms. RSC Adv. 2015;5(35):27986–8006.ArticleÂ
CASÂ
Google ScholarÂ
Rajendran P, Nandakumar N, Rengarajan T, Palaniswami R, Gnanadhas EN, Lakshminarasaiah U, Gopas J, Nishigaki I. Antioxidants and human diseases. Clin Chim Acta. 2014;436:332–47.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Jomova K, Raptova R, Alomar SY, Alwasel SH, Nepovimova E, Kuca K, Valko M. Reactive oxygen species, toxicity, oxidative stress, and antioxidants: chronic diseases and aging. Arch Toxicol. 2023;97(10):2499–574.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Kıran TR, Otlu O, Karabulut AB. Oxidative stress and antioxidants in health and disease. J Lab Med. 2023;47(1):1–11.ArticleÂ
Google ScholarÂ
He P, Zhang Y, Zhang Y, Zhang L, Lin Z, Sun C, Wu H, Zhang M. Isolation, identification of antioxidant peptides from earthworm proteins and analysis of the structure–activity relationship of the peptides based on quantum chemical calculations. Food Chem. 2024;431:137137.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Pagan LU, Gomes MJ, Gatto M, Mota GA, Okoshi K, Okoshi MP. The role of oxidative stress in the aging heart. Antioxidants. 2022;11(2):336.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Chang K-H, Chen C-M. The role of oxidative stress in Parkinson’s disease. Antioxidants. 2020;9(7):597.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Sun Q, Kong W, Mou X, Wang S. Transcriptional regulation analysis of Alzheimer’s disease based on FastNCA algorithm. Curr Bioinform. 2019;14(8):771–82.ArticleÂ
CASÂ
Google ScholarÂ
Liguori I, Russo G, Curcio F, Bulli G, Aran L, Della-Morte D, Gargiulo G, Testa G, Cacciatore F, Bonaduce D. Oxidative stress, aging, and diseases. Clin Interv Aging. 2018;13:757–72.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Reddy VP. Oxidative stress in health and disease. Biomedicines. 2023;11(11):2925.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Li X, Tang Q, Tang H, Chen W. Identifying antioxidant proteins by combining multiple methods. Front Bioeng Biotechnol. 2020;8:858.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Chaudhary P, Janmeda P, Docea AO, Yeskaliyeva B, Abdull Razis AF, Modu B, Calina D, Sharifi-Rad J. Oxidative stress, free radicals and antioxidants: potential crosstalk in the pathophysiology of human diseases. Front Chem. 2023;11:1158198.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Dhalaria R, Verma R, Kumar D, Puri S, Tapwal A, Kumar V, Nepovimova E, Kuca K. Bioactive compounds of edible fruits with their anti-aging properties: a comprehensive review to prolong human life. Antioxidants. 2020;9(11):1123.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Moulahoum H, Ghorbanizamani F, Timur S, Zihnioglu F. Beyond natural antioxidants in cancer therapy: novel synthetic approaches in harnessing oxidative stress. In: Chakraborti S, editor. Handbook of oxidative stress in cancer: therapeutic aspects. Springer: Singapore; 2022. p. 1–17.
Google ScholarÂ
Rojas-Fernandez CH, Tyber K. Benefits, potential harms, and optimal use of nutritional supplementation for preventing progression of age-related macular degeneration. Ann Pharmacother. 2017;51(3):264–70.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Mishra N, Tripathi S, Nahar L, Sarker SD, Kumar A: Mitigation of arsenic poisoning induced oxidative stress and genotoxicity by Ocimum gratissimum L. Toxicon 2024:107603.Pisoschi AM, Negulescu GP. Methods for total antioxidant activity determination: a review. Biochem Anal Biochem. 2011;1(1):106.
Google ScholarÂ
Wachirattanapongmetee K, Katekaew S, Weerapreeyakul N, Thawornchinsombut S. Differentiation of protein types extracted from tilapia byproducts by FTIR spectroscopy combined with chemometric analysis and their antioxidant protein hydrolysates. Food Chem. 2024;437:137862.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Madhani Mohammed Sadhakathullah AH, Paulo Mirasol S, Molina GarcÃa BG, Torras Costa J, ArmelÃn Diggroc EA. PLA-PEG-cholesterol biomimetic membrane for electrochemical sensing of antioxidants. Electrochim Acta. 2024;476:143716.ArticleÂ
Google ScholarÂ
Chen L, Chen S, Rong Y, Zeng W, Hu Z, Ma X, Feng S. Identification and evaluation of antioxidant peptides from highland barley distiller’s grains protein hydrolysate assisted by molecular docking. Food Chem. 2024;434:137441.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Li W, Zhu L, Zhang F, Han C, Li P, Jiang J. A novel strategy by combining foam fractionation with high-speed countercurrent chromatography for the rapid and efficient isolation of antioxidants and cytostatics from Camellia oleifera cake. Food Res Int. 2024;176:113798.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Lv Z, Cui F, Zou Q, Zhang L, Xu L. Anticancer peptides prediction with deep representation learning features. Brief Bioinform. 2021;22(5):bbab008.ArticleÂ
PubMedÂ
Google ScholarÂ
Lv Z, Zhang J, Ding H, Zou Q. RF-PseU: a random forest predictor for RNA pseudouridine sites. Front Bioeng Biotechnol. 2020;8:134.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Lv H, Dao F-Y, Zulfiqar H, Lin H. DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach. Brief Bioinform. 2021;22(6):bbab244.ArticleÂ
PubMedÂ
Google ScholarÂ
Olawoye B, Fagbohun OF, Popoola-Akinola O, Akinsola JET, Akanbi CT. A supervised machine learning approach for the prediction of antioxidant activities of Amaranthus viridis seed. Heliyon. 2024;10:e24506.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Meng C, Pei Y, Bu Y, Zou Q, Ju Y. Machine learning-based antioxidant protein identification model: progress and evaluation. J Cell Biochem. 2023;124:1825–34.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Feng P, Ding H, Lin H, Chen W. AOD: the antioxidant protein database. Sci Rep. 2017;7(1):7449.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Fernández-Blanco E, Aguiar-Pulido V, Munteanu CR, Dorado J. Random Forest classification based on star graph topological indices for antioxidant proteins. J Theor Biol. 2013;317:331–7.ArticleÂ
PubMedÂ
Google ScholarÂ
Feng P-M, Lin H, Chen W. Identification of antioxidants from sequence information using naive Bayes. Comput Math Methods Med. 2013;2013:567529.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Feng P, Chen W, Lin H. Identifying antioxidant proteins by using optimal dipeptide compositions. Interdiscip Sci Comput Life Sci. 2016;8:186–91.ArticleÂ
CASÂ
Google ScholarÂ
Zhang L, Zhang C, Gao R, Yang R. Incorporating g-gap dipeptide composition and position specific scoring matrix for identifying antioxidant proteins. In: 2015 IEEE 28th Canadian conference on electrical and computer engineering (CCECE). IEEE; 2015. p. 31–6.Zhang L, Zhang C, Gao R, Yang R, Song Q. Sequence based prediction of antioxidant proteins using a classifier selection strategy. PLoS ONE. 2016;11(9):e0163274.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Xu L, Liang G, Shi S, Liao C. SeqSVM: a sequence-based support vector machine method for identifying antioxidant proteins. Int J Mol Sci. 2018;19(6):1773.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Meng C, Jin S, Wang L, Guo F, Zou Q. AOPs-SVM: a sequence-based classifier of antioxidant proteins using a support vector machine. Front Bioeng Biotechnol. 2019;7:224.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Butt AH, Rasool N, Khan YD. Prediction of antioxidant proteins by incorporating statistical moments based features into Chou’s PseAAC. J Theor Biol. 2019;473:1–8.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Ahmad A, Akbar S, Hayat M, Ali F, Khan S, Sohail M. Identification of antioxidant proteins using a discriminative intelligent model of k-space amino acid pairs based descriptors incorporating with ensemble feature selection. Biocybern Biomed Eng. 2022;42(2):727–35.ArticleÂ
Google ScholarÂ
Ho Thanh Lam L, Le NH, Van Tuan L, Tran Ban H, Nguyen Khanh Hung T, Nguyen NTK, Huu Dang L, Le NQK. Machine learning model for identifying antioxidant proteins using features calculated from primary sequences. Biology. 2020;9(10):325.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Tran HV, Nguyen QH. iAnt: combination of convolutional neural network and random forest models using PSSM and BERT features to identify antioxidant proteins. Curr Bioinform. 2022;17(2):184–95.ArticleÂ
CASÂ
Google ScholarÂ
Zhai Y, Zhang J, Zhang T, Gong Y, Zhang Z, Zhang D, Zhao Y. AOPM: application of antioxidant protein classification model in predicting the composition of antioxidant drugs. Front Pharmacol. 2022;12:818115.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Meng C, Pei Y, Zou Q, Yuan L. DP-AOP: a novel SVM-based antioxidant proteins identifier. Int J Biol Macromol. 2023;247:125499.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Usman M, Khan S, Park S, Lee J-A. AoP-LSE: antioxidant proteins classification using deep latent space encoding of sequence features. Curr Issues Mol Biol. 2021;43(3):1489–501.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ahmed S, Arif M, Kabir M, Khan K, Khan YD. PredAoDP: Accurate identification of antioxidant proteins by fusing different descriptors based on evolutionary information with support vector machine. Chemom Intell Lab Syst. 2022;228:104623.ArticleÂ
CASÂ
Google ScholarÂ
Qin D, Jiao L, Wang R, Zhao Y, Hao Y, Liang G. Prediction of antioxidant peptides using a quantitative structure−activity relationship predictor (AnOxPP) based on bidirectional long short-term memory neural network and interpretable amino acid descriptors. Comput Biol Med. 2023;154:106591.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Xi Q, Wang H, Yi L, Zhou J, Liang Y, Zhao X, Zuo Y. ANPrAod: identify antioxidant proteins by fusing amino acid clustering strategy and-peptide combination. Comput Math Methods Med. 2021;2021:1–10.ArticleÂ
Google ScholarÂ
Olsen TH, Yesiltas B, Marin FI, Pertseva M, GarcÃa-Moreno PJ, Gregersen S, Overgaard MT, Jacobsen C, Lund O, Hansen EB. AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides. Sci Rep. 2020;10(1):21471.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ahmad S, Charoenkwan P, Quinn JM, Moni MA, Hasan MM, Lio’ P, Shoombuatong W. SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins. Sci Rep. 2022;12(1):4106.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Chen Q, Wan Y, Lei Y, Zobel J, Verspoor K. Evaluation of CD-HIT for constructing non-redundant databases. In: 2016 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE; 2016. p. 703–6.Ullah M, Akbar S, Raza A, Zou Q. DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm. Bioinformatics. 2024;40:btae305.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Akbar S, Khan S, Ali F, Hayat M, Qasim M, Gul S. iHBP-DeepPSSM: identifying hormone binding proteins using PsePSSM based evolutionary features and deep learning approach. Chemom Intell Lab Syst. 2020;204:104103.ArticleÂ
CASÂ
Google ScholarÂ
Yu B, Li S, Qiu W, Wang M, Du J, Zhang Y, Chen X. Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction. BMC Genom. 2018;19:1–17.ArticleÂ
Google ScholarÂ
Nanni L, Brahnam S, Lumini A. Wavelet images and Chou’s pseudo amino acid composition for protein classification. Amino Acids. 2012;43:657–65.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Ahmad A, Akbar S, Tahir M, Hayat M, Ali F. iAFPs-EnC-GA: identifying antifungal peptides using sequential and evolutionary descriptors based multi-information fusion and ensemble learning approach. Chemom Intell Lab Syst. 2022;222:104516.ArticleÂ
CASÂ
Google ScholarÂ
Lu W, Song Z, Ding Y, Wu H, Cao Y, Zhang Y, Li H. Use Chou’s 5-step rule to predict DNA-binding proteins with evolutionary information. BioMed Res Int. 2020;2020:1–9.ArticleÂ
Google ScholarÂ
Zhang L, Zhao X, Kong L. Predict protein structural class for low-similarity sequences by evolutionary difference information into the general form of Chou׳ s pseudo amino acid composition. J Theor Biol. 2014;355:105–10.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Sun D, Liu Z, Mao X, Yang Z, Ji C, Liu Y, Wang S. ANOX: a robust computational model for predicting the antioxidant proteins based on multiple features. Anal Biochem. 2021;631:114257.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Wang J, Yang B, Revote J, Leier A, Marquez-Lago TT, Webb G, Song J, Chou K-C, Lithgow T. POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles. Bioinformatics. 2017;33(17):2756–8.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Hayat M, Khan A. Mem-PHybrid: hybrid features-based prediction system for classifying membrane protein types. Anal Biochem. 2012;424(1):35–44.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Hayat M, Khan A. WRF-TMH: predicting transmembrane helix by fusing composition index and physicochemical properties of amino acids. Amino Acids. 2013;44:1317–28.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Suvarna Vani K, Durga Bhavani S. SMOTE based protein fold prediction classification. In: Advances in computing and information technology: proceedings of the second international conference on advances in computing and information technology (ACITY) July 13–15, 2012, Chennai, India-Volume 2. Springer; 2013. p. 541–50.Akbar S, Hayat M, Kabir M, Iqbal M. iAFP-gap-SMOTE: an efficient feature extraction scheme gapped dipeptide composition is coupled with an oversampling technique for identification of antifreeze proteins. Lett Org Chem. 2019;16(4):294–302.ArticleÂ
CASÂ
Google ScholarÂ
Hu J, He X, Yu D-J, Yang X-B, Yang J-Y, Shen H-B. A new supervised over-sampling algorithm with application to protein-nucleotide binding residue prediction. PLoS ONE. 2014;9(9):e107676.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Elreedy D, Atiya AF. A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance. Inf Sci. 2019;505:32–64.ArticleÂ
Google ScholarÂ
Sun Y, Robinson M, Adams R, Te Boekhorst R, Rust AG, Davey N. Using sampling methods to improve binding site predictions. In: Proceedings of the 14th European symposium on artificial neural networks, ESANN 2006; 2006.Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–57.ArticleÂ
Google ScholarÂ
Peng H, Long F, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell. 2005;27(8):1226–38.ArticleÂ
PubMedÂ
Google ScholarÂ
Charoenkwan P, Chiangjong W, Nantasenamat C, Hasan MM, Manavalan B, Shoombuatong W. StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides. Brief Bioinform. 2021;22(6):bbab172.ArticleÂ
PubMedÂ
Google ScholarÂ
Mishra A, Pokhrel P, Hoque MT. StackDPPred: a stacking based prediction of DNA-binding protein from sequence. Bioinformatics. 2019;35(3):433–41.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Basith S, Lee G, Manavalan B. STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction. Brief Bioinform. 2022;23(1):bbab376.ArticleÂ
PubMedÂ
Google ScholarÂ
Liang X, Li F, Chen J, Li J, Wu H, Li S, Song J, Liu Q. Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification. Brief Bioinform. 2021;22(4):bbaa312.ArticleÂ
PubMedÂ
Google ScholarÂ
Jiang M, Zhao B, Luo S, Wang Q, Chu Y, Chen T, Mao X, Liu Y, Wang Y, Jiang X. NeuroPpred-Fuse: an interpretable stacking model for prediction of neuropeptides by fusing sequence information and feature selection methods. Brief Bioinform. 2021;22(6):bbab310.ArticleÂ
PubMedÂ
Google ScholarÂ
Guo Y, Yan K, Lv H, Liu B. PreTP-EL: prediction of therapeutic peptides based on ensemble learning. Brief Bioinform. 2021;22(6):bbab358.ArticleÂ
PubMedÂ
Google ScholarÂ
Cao Z, Pan X, Yang Y, Huang Y, Shen H-B. The lncLocator: a subcellular localization predictor for long non-coding RNAs based on a stacked ensemble classifier. Bioinformatics. 2018;34(13):2185–94.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Zhang Q, Liu P, Wang X, Zhang Y, Han Y, Yu B. StackPDB: predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier. Appl Soft Comput. 2021;99:106921.ArticleÂ
Google ScholarÂ
Akbar S, Raza A, Zou Q. Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model. BMC Bioinform. 2024;25(1):102.ArticleÂ
CASÂ
Google ScholarÂ
Akbar S, Ali H, Ahmad A, Sarker MR, Saeed A, Salwana E, Gul S, Khan A, Ali F. Prediction of amyloid proteins using embedded evolutionary & ensemble feature selection based descriptors with extreme gradient boosting model. IEEE Access; 2023.Bukhari SNH, Webber J, Mehbodniya A. Decision tree based ensemble machine learning model for the prediction of Zika virus T-cell epitopes as potential vaccine candidates. Sci Rep. 2022;12(1):7810.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Akbar S, Rahman AU, Hayat M, Sohail M. cACP: classifying anticancer peptides using discriminative intelligent model via Chou’s 5-step rules and general pseudo components. Chemom Intell Lab Syst. 2020;196:103912.ArticleÂ
CASÂ
Google ScholarÂ
Ao C, Zhou W, Gao L, Dong B, Yu L. Prediction of antioxidant proteins using hybrid feature representation method and random forest. Genomics. 2020;112(6):4666–74.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Dwivedi AK. Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Comput Appl. 2018;29:685–93.ArticleÂ
Google ScholarÂ
Ali F, Akbar S, Ghulam A, Maher ZA, Unar A, Talpur DB. AFP-CMBPred: computational identification of antifreeze proteins by extending consensus sequences into multi-blocks evolutionary information. Comput Biol Med. 2021;139:105006.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Akbar S, Zou Q, Raza A, Alarfaj FK. iAFPs-Mv-BiTCN: predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks. Artif Intell Med. 2024;151:102860.ArticleÂ
PubMedÂ
Google ScholarÂ
Raza A, Uddin J, Almuhaimeed A, Akbar S, Zou Q, Ahmad A. AIPs-SnTCN: predicting anti-inflammatory peptides using fastText and transformer encoder-based hybrid word embedding with self-normalized temporal convolutional networks. J Chem Inf Model. 2023;63:6537–54.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Raza A, Uddin J, Akbar S, Alarfaj FK, Zou Q, Ahmad A. Comprehensive analysis of computational methods for predicting anti-inflammatory peptides. Arch Comput Methods Eng. 2024. https://doi.org/10.1007/s11831-024-10078-7.ArticleÂ
Google ScholarÂ
Akbar S, Hayat M. iMethyl-STTNC: identification of N6-methyladenosine sites by extending the idea of SAAC into Chou’s PseAAC to formulate RNA sequences. J Theor Biol. 2018;455:205–11.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Charoenkwan P, Ahmed S, Nantasenamat C, Quinn JM, Moni MA, Lio’ P, Shoombuatong W. AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning. Sci Rep. 2022;12(1):7697.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz R, Himmelfarb J, Bansal N, Lee S-I. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell. 2020;2(1):56–67.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Garreau D, Luxburg U. Explaining the explainer: a first theoretical analysis of LIME. In: International conference on artificial intelligence and statistics. PMLR; 2020. p. 1287–96.Du Z, Ding X, Xu Y, Li Y. UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity. Brief Bioinform. 2023;24(3):1–10.ArticleÂ
Google ScholarÂ