We are analyzing https://link.springer.com/article/10.1007/s12539-010-0075-0.

Title:
A novel locally linear embedding and wavelet transform based encoding method for prediction of MHC-II binding affinity | Interdisciplinary Sciences: Computational Life Sciences
Description:
The binding between peptides and MHC molecules is an important event to the cellular immunity against pathogens. The binding peptides are recognized as the epitopes, which are useful for the epitope-based vaccine design. Accurate prediction of the MHC-II binding peptides has long been a challenge in bioinformatics. Recently, most researchers are interested in predicting the binding affinity instead of categorizing peptides as “binders” or “non-binders”. In this paper, we introduced a novel encoding scheme based on Locally Linear Embedding (LLE) and Wavelet Transform (WT), in which important amino acid properties were firstly selected from all properties (described in AAindex database) by using LLE, and then amino acids of peptides were replaced with these novel properties. Further, WT was adopted to extract the frequency attributes of the numerical sequences; thereby the peptides were transformed into homogeneous-length vectors. Finally, Support Vector machine Regression (SVR) was used to make quantitative prediction models based on these numerical vectors. When applied to the 16 datasets from IEDB database, our encoding scheme produced consistently better performance than other encoding schemes, indicating that our encoding scheme is an effective tool for the prediction of MHC-II binding affinity.
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Keywords {🔍}

article, google, scholar, binding, prediction, pubmed, class, mhc, affinity, peptides, bioinformatics, cas, encoding, based, peptide, transform, mhcii, liu, support, access, privacy, cookies, content, search, linear, embedding, wavelet, vector, machine, bmc, publish, sciences, locally, method, zhang, epitopes, models, data, information, log, journal, research, computational, life, juan, molecules, immunity, scheme, amino, properties,

Topics {✒️}

qing-jiao li & wen zhang mhc-ii binding affinity support vector machines locally linear embedding mhc-ii binding peptides month download article/chapter cellular immunity class ii epitopes wavelet package transform epitope-based vaccine design local linear embedding related subjects multi-objective evolutionary algorithms mhc-binding peptides encoding scheme based class ii structures wavelet transform article interdisciplinary sciences artificial neural network binding affinity thewavelet transform approach machine learning approach full article pdf check access instant access privacy choices/manage cookies mhc class affinity prediction predicting peptides binding cell receptor recognition iterative learning model encoding scheme universal sequence models svrmhc prediction server machine learning mhc molecules arb matrix applications european economic area encoding schemes ten lectures onwavelets gibbs sampling approach nonlinear dimensionality reduction conditions privacy policy ieee computational science binding peptides homogeneous-length vectors immunity 7 accepting optional cookies epitopes tw/~cjlin/libsvm

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         description:The binding between peptides and MHC molecules is an important event to the cellular immunity against pathogens. The binding peptides are recognized as the epitopes, which are useful for the epitope-based vaccine design. Accurate prediction of the MHC-II binding peptides has long been a challenge in bioinformatics. Recently, most researchers are interested in predicting the binding affinity instead of categorizing peptides as “binders” or “non-binders”. In this paper, we introduced a novel encoding scheme based on Locally Linear Embedding (LLE) and Wavelet Transform (WT), in which important amino acid properties were firstly selected from all properties (described in AAindex database) by using LLE, and then amino acids of peptides were replaced with these novel properties. Further, WT was adopted to extract the frequency attributes of the numerical sequences; thereby the peptides were transformed into homogeneous-length vectors. Finally, Support Vector machine Regression (SVR) was used to make quantitative prediction models based on these numerical vectors. When applied to the 16 datasets from IEDB database, our encoding scheme produced consistently better performance than other encoding schemes, indicating that our encoding scheme is an effective tool for the prediction of MHC-II binding affinity.
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