Dana-Farber Repository for Machine Learning in Immunology

This data repository bridges the gap between immunological and computer science/machine learning communities by providing preprocessed and scaled immunological data sets suitable for use in machine learning applications. These datasets include major publically available data sets (from IEDB, CBS, and our group) as well as carefully selected independent validation data sets. The recommendations for scaling and comparison of performance of prediction systems are included in the system too. Some of the data sets in the DFRMLI were used for an earlier machine learning competition.

For details of the repository, please refer to the papers below:
Zhang GL, Lin HH, Keskin DB, Reinherz EL, Brusic V. Dana-Farber repository for machine learning in immunology. J Immunol Methods. 2011; 374(1-2):18-25.
Zhang GL, Ansari HR, Bradley P, ... Brusic V. Machine learning competition in immunology–prediction of HLA class I binding peptides. J Immunol Methods. 2011;374(1):1-4.

Cited by 25 papers   hide/show

  1. Lin HH, Ray S, Tongchusak S, Reinherz EL, Brusic V. Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research. BMC Immunol. 2008; 9:8.
  2. Lin HH, Zhang GL, Tongchusak S, Reinherz EL, Brusic V. Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research. BMC Bioinformatics. 2008 Dec 12;9 Suppl 12:S22.
  3. Singh SP, Mishra BN. Prediction of MHC binding peptide using Gibbs motif sampler, weight matrix and artificial neural network. Bioinformation. 2008;3(4):150-5.
  4. Dimitrov I, Garnev P, Flower DR, Doytchinova I. EpiTOP--a proteochemometric tool for MHC class II binding prediction. Bioinformatics. 2010; 26(16):2066-8.
  5. Bordner AJ, Mittelmann HD. MultiRTA: a simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes. BMC Bioinformatics. 2010; 24;11:482.
  6. Huang JC, Jojic N. Modeling major histocompatibility complex binding by nonparametric averaging of multiple predictors and sequence encodings. J Immunol Methods. 2011; 374(1-2):35-42.
  7. Hu X, Mamitsuka H, Zhu S. Ensemble approaches for improving HLA class I-peptide binding prediction. J Immunol Methods. 2011; 374(1-2):47-52.
  8. Zhang GL, Ansari HR, Bradley P, et al. Machine Learning Competition in Immunology Prediction of HLA class I molecules. J Immunol Methods. 2011 Nov 30;374(1-2):1-4.
  9. Zhang L, Chen Y, Wong HS, Zhou S, Mamitsuka H, Zhu S. TEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules. PLoS One. 2012;7(2):e30483
  10. Chaves FA, Lee AH, Nayak JL, Richards KA, Sant AJ. The Utility and Limitations of Current Web-Available Algorithms To Predict Peptides Recognized by CD4 T Cells in Response to Pathogen Infection. J Immunol. 2012;188(9):4235-48.
  11. Rock MT, McKinney BA, Yoder SM, Prudom CE, Wright DW, Crowe JE Jr. Identification of potential human respiratory syncytial virus and metapneumovirus T cell epitopes using computational prediction and MHC binding assays. J Immunol Methods. 2011;374(1-2):13-7.
  12. Alvarellos-Gonzalez A, Pazos A, Porto-Pazos AB. Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks. Comput Math Methods Med. 2012; 2012:476324.
  13. Singh SP, Mishra BN. Prediction Model of MHC Class-II Binding Peptide Motifs Using Sequence Weighting Method for Vaccine Design. In Proceedings of the Advances in Computing and Communications (ICACC), 2012 International Conference Pages 234 - 237, 9-11 Aug. 2012.
  14. Charoentong P, Angelova M, Efremova M, Gallasch R, Hackl H, Galon J, Trajanoski Z. Bioinformatics for cancer immunology and immunotherapy. Cancer Immunol Immunother. 2012 Nov;61(11):1885-903.
  15. Schonbach C, Tongsima S, Chan J, Brusic V, Tan TW, Ranganathan S. InCoB2012 Conference: from biological data to knowledge to technological breakthroughs.BMC Bioinformatics. 2012;13 Suppl 17:S1. doi: 10.1186/1471-2105-13-S17-S1.
  16. Soam SS, Khan F, Bhasker B, Mishra BN. Classification using Negative Selection Algorithm: Application to MHC Class II Binders/Non-Binders used in Peptide based Vaccine Designing. In International Conference on Computer and Automation Engineering ICCAE 2012, Mumbai. Proceedings published by ASME Press, 2012.
  17. Oyarzun P, Ellis JJ, Boden M, Kobe BT. PREDIVAC: CD4+ T-cell epitope prediction for vaccine design that covers 95% of HLA class II DR protein diversity. BMC Bioinformatics. 2013 Feb 14;14(1):52.
  18. Pappalardo F, Chiacchio F, Motta S. Cancer Vaccines: State of the Art of the Computational Modeling Approaches. BioMed Research International. 2013. 2013:106407
  19. Giguere, S., Drouin, A., Lacoste, A., Marchand, M., Corbeil, J., Laviolette, F. MHC-NP: Predicting peptides naturally processed by the MHC. Journal of Immunological Methods 2013;400:30-36.
  20. Foote SJ. Genome-Based Bioinformatic Prediction of Major Histocompatibility Complex (MHC) Epitopes. Immunoproteomics 2013;1061:309-22.
  21. Shen WJ, Wei YT, Guo X, Smale S, Wong HS, Li SC. MHC binding prediction with KernelRLSpan and its variations. Journal of immunological methods 2014; pii: S0022-1759(14)00053-2.
  22. Angelova M, Charoentong P, Hackl H, Fischer ML, Snajder R, Krogsdam AM, Waldner M, Bindea G, Mlecnik B, Galon J, Trajanoski Z. Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy. Genome Biol. 2015 Mar 31;16:64.
  23. Backert L, Kohlbacher O. Immunoinformatics and epitope prediction in the age of genomic medicine. Genome medicine. 2015;7(1):1-2.
  24. Tang Q, Nie F, Kang J, Ding H, Zhou P, Huang J. NIEluter: Predicting peptides eluted from HLA class I molecules. Journal of immunological methods. 2015;422:22-7.
  25. Singh A, Singh R. IMMUNOLOGICAL DATABASES AND ITS ROLE IN IMMUNOLOGICAL RESEARCH. Life Chemistry Research: Biological Systems. 2015; 265.

Version 1.0, Sep 2009. Developed by Bioinformatics Core at Cancer Vaccine Center, Dana-Farber Cancer Institute.