Adaptive compressive learning for prediction of protein-protein interactions from primary sequence
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Publication:1783649
DOI10.1016/J.JTBI.2011.05.023zbMath1397.92243OpenAlexW2085809045WikidataQ44623420 ScholiaQ44623420MaRDI QIDQ1783649
Hong-Bin Shen, Xiao-Yong Pan, Yan Huang, Ya-nan Zhang
Publication date: 21 September 2018
Published in: Journal of Theoretical Biology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jtbi.2011.05.023
compressed sensingNyquist samplingprotein-protein interactions predictionsequential discrete representation
Related Items (5)
Predicting protein-protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach ⋮ Transcriptional protein-protein cooperativity in POU/HMG/DNA complexes revealed by normal mode analysis ⋮ Interrogating noise in protein sequences from the perspective of protein-protein interactions prediction ⋮ DNN-PPI: A LARGE-SCALE PREDICTION OF PROTEIN–PROTEIN INTERACTIONS BASED ON DEEP NEURAL NETWORKS ⋮ A novel conjoint triad auto covariance (CTAC) coding method for predicting protein-protein interaction based on amino acid sequence
Uses Software
Cites Work
- The restricted isometry property and its implications for compressed sensing
- A simple proof of the restricted isometry property for random matrices
- Wrappers for feature subset selection
- High performance set of PseAAC and sequence based descriptors for protein classification
- Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
- Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
- Stable signal recovery from incomplete and inaccurate measurements
- Compressed sensing
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