Selected article for: "neural network and Supplementary table"
Author: Delli Ponti, Riccardo; Marti, Stefanie; Armaos, Alexandros; Tartaglia, Gian Gaetano
Title: A high-throughput approach to profile RNA structure
  • Document date: 2017_3_17
    • ID: k23xlzj0_6
    Snippet: We trained CROSS models using an artificial neural network with one hidden layer and two adaptive weight matrices ω i k and k that are optimized using backpropagation. In our approach, we use the 4-mer notation to represent each nucleotide: A = (1, 0, 0, 0), C = (0, 1, 0, 0), G = (0, 0, 1, 0) and U = (0, 0, 0, 1). The input of our method (Supplementary Material: Data sets) is the vector F i encoding the information on fragments of fixed length (.....
    Document: We trained CROSS models using an artificial neural network with one hidden layer and two adaptive weight matrices ω i k and k that are optimized using backpropagation. In our approach, we use the 4-mer notation to represent each nucleotide: A = (1, 0, 0, 0), C = (0, 1, 0, 0), G = (0, 0, 1, 0) and U = (0, 0, 0, 1). The input of our method (Supplementary Material: Data sets) is the vector F i encoding the information on fragments of fixed length (Supplementary Material: Selection of the optimal window). The input information required to predict the structural state of a specific nucleotide was extracted using a sliding window spanning the precedent and subsequent 6 residues (i.e. 13 nucleotides; longer fragments do not substantially improve the method; Supplementary Material: Selection of the optimal window; Supplementary Table S1 ).

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