… Therefore, this research attempted to use the transfer learning method to train a model to
learn common features among the mixed allele specific epitopes. Then based on this pre-…

… of prediction on both problems: predicting MHC-peptide binding and TCR-pMHC binding.
Moreover, this study integrated ensemble learning to further improve the prediction accuracy. …

… To solve this problem, this paper proposes a classification algorithm based on deep learning
to accurately predict the interaction between TCR and peptides. The experimental results …

… ),也可以預測immunogenicity of MHC class I and II binding peptides,並以此為基礎研究免疫
系統(子計畫二,一). 四. 以演化式最佳化方法等機器學習(machine learning)方法重建基因調控網路,…

… To encode an input vector from peptide sequences for machine learning classifiers, a two-step
method is utilized. The first step determines a vector Dt of 531 index values for each …

… 法[3](binding motif), (2)定量矩阵法[4] (quantitive matrix), (3)机器学习法(machine learning)(即…
CTL 细胞如何识别抗原肽-MHC 复合物分子, 及抗原短 肽peptide如何与主要组织相容性复合体…

… SVM is a supervised learning method developed by Vapnik in 1963 based on the
Structural Risk Minimization Principle. [19,21,22] SVM had been applied widely in the field of …

… 2) Support vector machine Support vector machine (SVM) is a learning model dealing with
binary classification problems. SVM constructs a binary classifier by finding a hyperplane to …

… 预测CTL 表位, 它们主 要分为4 大类: (1)结合基序法(binding motif)[1] ; (2)定量矩 阵法(quantitive
matrix)[2] ; (3)机器学习法(machine learning)(即人工神经网络,隐马尔可夫,支持向量机) [3~6] …

… Recently, machine learning and other artificial intelligence technologies have also been
applied to computational protein engineering, resulting in a series of remarkable applications. …