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_23
Snippet: The CROSS algorithm CROSS predicts the structural profile of an RNA sequence at single-nucleotide resolution and without sequence length restrictions. The algorithm is an artificial neural network with one hidden layer and two adaptive weight matrices to predict the structural state of a nucleotide considering its flanking residues (Materials and Methods: CROSS architecture; Supplementary Material: Selection of the optimal window). We built five .....
Document: The CROSS algorithm CROSS predicts the structural profile of an RNA sequence at single-nucleotide resolution and without sequence length restrictions. The algorithm is an artificial neural network with one hidden layer and two adaptive weight matrices to predict the structural state of a nucleotide considering its flanking residues (Materials and Methods: CROSS architecture; Supplementary Material: Selection of the optimal window). We built five independent models using data from SHAPE ( either single-stranded (negative cases) or double-stranded (positive cases) configuration. Each model was then tested on all the other data sets. Negligible overlap exists between training and testing sets (Jaccard index < 0.002 between each couple of sets analyzed; Supplementary Table S5) .
Search related documents:
Co phrase search for related documents- neural network and optimal window selection: 1
- neural network and optimal window selection supplementary material: 1
- neural network and positive case: 1, 2, 3
- neural network and RNA sequence: 1
- neural network and sequence length: 1, 2, 3
- neural network and single nucleotide: 1
- neural network and structural state: 1
- neural network and supplementary material: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32
- neural network and testing training: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72
- neural network and testing training set: 1, 2, 3, 4
- neural network and weight matrix: 1
- optimal window and RNA sequence: 1
- optimal window and structural state: 1
- optimal window and supplementary material: 1
- optimal window selection and structural state: 1
- optimal window selection and supplementary material: 1
- optimal window selection supplementary material and structural state: 1
- optimal window selection supplementary material and supplementary material: 1
- positive case and RNA sequence: 1
Co phrase search for related documents, hyperlinks ordered by date