PMID- 16180919
OWN - NLM
STAT- MEDLINE
DCOM- 20051223
LR  - 20050926
IS  - 1549-9596 (Print)
IS  - 1549-9596 (Linking)
VI  - 45
IP  - 5
DP  - 2005 Sep-Oct
TI  - Prediction of T-cell epitopes using biosupport vector machines.
PG  - 1424-8
AB  - The immune system is concerned with the recognition and disposal of foreign or 
      "non self" molecules or cells that enter the body of an immunologically competent 
      individual. The generation of an immune response depends on the interaction of 
      components, namely, the immunogen (nonself or foreign cell or molecule), antibody 
      producing humoral immune system, and sensitized lymphocyte producing cellular 
      immune system. An immunogen possesses surface structures referred to as epitopes; 
      the precise pattern of each epitope enables an individual's immune system to 
      recognize cells or molecules as self or immunogens. During the recognition 
      process, the specific cells known as macrophages identify the epitope structures 
      on the immunogen and save them in the form of short peptides 10-18 
      amino-acids-long known as immune dominant peptides (IDPs). IDPs are then bound 
      with surface proteins on macrophages known as MHC protein complexes. The 
      macrophages then present this IDP-MHC complex to a T cell that possesses a 
      specific receptor that is specific for the foreign epitope on the IDP bound to 
      MHC complex. This initiates an immune system cascade that results in the disposal 
      of the immunogen. The study and accurate prediction of T-cell epitopes is, thus, 
      very important for designing vaccines against pathogenic diseases. The present 
      study applied the newly developed biosupport vector machine to the T-cell epitope 
      data. This new algorithm introduces a biobasis function into the conventional 
      support vector machines so that the nonnumerical attributes (amino acids) in 
      protein sequences can be recognized without a feature extraction process, which 
      often fails to properly code the biological content in protein sequences. The 
      prediction accuracy of a 10-fold cross validation is 90.31%, compared with 87.86% 
      using support vector machines reported as the best compared with other algorithms 
      in an earlier study.
FAU - Yang, Zheng Rong
AU  - Yang ZR
AD  - Department of Computer Science, University of Exeter, United Kingdom. 
      z.r.yang@ex.ac.uk
FAU - Johnson, Felicia Charles
AU  - Johnson FC
LA  - eng
PT  - Journal Article
PL  - United States
TA  - J Chem Inf Model
JT  - Journal of chemical information and modeling
JID - 101230060
RN  - 0 (Epitopes, T-Lymphocyte)
RN  - 0 (Histocompatibility Antigens)
SB  - IM
MH  - Algorithms
MH  - Computational Biology/*methods
MH  - Epitope Mapping/*methods
MH  - Epitopes, T-Lymphocyte/*chemistry/*immunology
MH  - Histocompatibility Antigens/immunology
MH  - Macrophages/immunology
MH  - Sensitivity and Specificity
MH  - Software
MH  - T-Lymphocytes/*immunology
EDAT- 2005/09/27 09:00
MHDA- 2005/12/24 09:00
CRDT- 2005/09/27 09:00
PHST- 2005/09/27 09:00 [pubmed]
PHST- 2005/12/24 09:00 [medline]
PHST- 2005/09/27 09:00 [entrez]
AID - 10.1021/ci050004t [doi]
PST - ppublish
SO  - J Chem Inf Model. 2005 Sep-Oct;45(5):1424-8. doi: 10.1021/ci050004t.