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Pavel P. Kuksa, Martin Renqiang Min, Rishabh Dugar, and Mark Gerstein. High-order neural networks and kernel methods for peptide-MHC binding prediction. Bioinformatics, 31(22):3600–3607, 2015.
Motivation: Effective computational methods for peptide-protein binding prediction can greatly help clinical peptide vaccine search and design. However, previous computational methods fail to capture key nonlinear high-order dependencies between different amino acid positions. As a result, they often produce low-quality rankings of strong binding peptides. To solve this problem, we propose nonlinear high-order machine learning methods including high-order neural networks (HONNs) with possible deep extensions and high-order kernel support vector machines to predict major histocompatibility complex-peptide binding.Results: The proposed high-order methods improve quality of binding predictions over other prediction methods. With the proposed methods, a significant gain of up to 25--40% is observed on the benchmark and reference peptide datasets and tasks. In addition, for the first time, our experiments show that pre-training with high-order semi-restricted Boltzmann machines significantly improves the performance of feed-forward HONNs. Moreover, our experiments show that the proposed shallow HONN outperform the popular pre-trained deep neural network on most tasks, which demonstrates the effectiveness of modelling high-order feature interactions for predicting major histocompatibility complex-peptide binding.Availability and implementation: There is no associated distributable software.Contact: renqiang@nec-labs.com or mark.gerstein@yale.eduSupplementary information: Supplementary data are available at Bioinformatics online.
@article{bioinf2015peptide, Abstract = {Motivation: Effective computational methods for peptide-protein binding prediction can greatly help clinical peptide vaccine search and design. However, previous computational methods fail to capture key nonlinear high-order dependencies between different amino acid positions. As a result, they often produce low-quality rankings of strong binding peptides. To solve this problem, we propose nonlinear high-order machine learning methods including high-order neural networks (HONNs) with possible deep extensions and high-order kernel support vector machines to predict major histocompatibility complex-peptide binding.Results: The proposed high-order methods improve quality of binding predictions over other prediction methods. With the proposed methods, a significant gain of up to 25--40% is observed on the benchmark and reference peptide datasets and tasks. In addition, for the first time, our experiments show that pre-training with high-order semi-restricted Boltzmann machines significantly improves the performance of feed-forward HONNs. Moreover, our experiments show that the proposed shallow HONN outperform the popular pre-trained deep neural network on most tasks, which demonstrates the effectiveness of modelling high-order feature interactions for predicting major histocompatibility complex-peptide binding.Availability and implementation: There is no associated distributable software.Contact: renqiang@nec-labs.com or mark.gerstein@yale.eduSupplementary information: Supplementary data are available at Bioinformatics online.}, Author = {Kuksa, Pavel P. and Min, Martin Renqiang and Dugar, Rishabh and Gerstein, Mark}, Bib2Html_Pubtype = {Journal}, Date-Added = {2015-12-28 18:43:20 +0000}, Date-Modified = {2020-03-06 13:18:11 -0500}, Doi = {10.1093/bioinformatics/btv371}, Eprint = {http://bioinformatics.oxfordjournals.org/content/31/22/3600.full.pdf+html}, Journal = {Bioinformatics}, Number = {22}, Pages = {3600-3607}, Title = {High-order neural networks and kernel methods for peptide-{MHC} binding prediction}, Url = {http://bioinformatics.oxfordjournals.org/content/31/22/3600.abstract}, Volume = {31}, Year = {2015}, Bdsk-Url-1 = {http://bioinformatics.oxfordjournals.org/content/31/22/3600.abstract}, Bdsk-Url-2 = {http://dx.doi.org/10.1093/bioinformatics/btv371}}
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