title: High-order neural networks and kernel methods for peptide-MHC binding prediction.
abstract: BACKGROUND
  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.
  
  
  METHODS
  There is no associated distributable software.
  
  
  BACKGROUND
  renqiang@nec-labs.com or mark.gerstein@yale.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Institute for Biomedical Informatics, Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA, Department of Machine Learning, NEC Laboratories America, Princeton, NJ 08540, USA.
authors.forename: Pavel P
authors.initials: PP
authors.lastname: Kuksa
authors.affiliation: Department of Machine Learning, NEC Laboratories America, Princeton, NJ 08540, USA.
authors.forename: Martin Renqiang
authors.initials: MR
authors.lastname: Min
authors.affiliation: Department of Machine Learning, NEC Laboratories America, Princeton, NJ 08540, USA.
authors.forename: Rishabh
authors.initials: R
authors.lastname: Dugar
authors.affiliation: Program of Computational Biology and Bioinformatics and Department of Molecular Biophysics and Biochemistry and Department of Computer Science, Yale University, New Haven, CT 06511, USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Gerstein
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019540:Area Under Curve; D030562:Databases, Protein; D000939:Epitopes / Q000737:chemistry; D006801:Humans; D008285:Major Histocompatibility Complex; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D010455:Peptides / Q000737:chemistry / Q000378:metabolism*; D011485:Protein Binding; D012372:ROC Curve; D060388:Support Vector Machine
chemical_list: D000939:Epitopes; D010455:Peptides