Detail Reliable prediction of T-cell epitopes using neural networks with novel sewuence representations (from Protein Science 2003, 12, 1007-1017) Bibliografi Author: Nielsen, Morten ; Lundegaard, Claus ; Worning, Peder ; Lauemoller, Sanne Lise ; Lamberth, Kasper ; Buus, Soren ; Brunak, Soren Topik: T-cell class I epitope; HLA-A2; Artificial neural network; Hidden Markov model; Sequence encoding; Mutual information; Seminar - Thesis lit Bahasa: (EN )     Penerbit: Cold Spring Harbor Laboratory Press     Tempat Terbit: New York    Tahun Terbit: 2003     Jenis: Article - diterbitkan di jurnal ilmiah internasional Fulltext: 0121007.pdf (892.77KB; 0 download) Abstract In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design. [seminar - thesis lit] Opini Anda Klik untuk menuliskan opini Anda tentang koleksi ini! Lihat Sejarah Pengadaan  Konversi Metadata   Kembali

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