PredRSA: a gradient boosted regression trees approach for predicting protein solvent accessibility.
about
Accurate prediction of protein relative solvent accessibility using a balanced model.A hydrophobic spine stabilizes a surface-exposed α-helix according to analysis of the solvent-accessible surface area.Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties.A boosting approach for prediction of protein-RNA binding residues.A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.Link synthetic lethality to drug sensitivity of cancer cells.Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network.Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network
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P2860
PredRSA: a gradient boosted regression trees approach for predicting protein solvent accessibility.
description
2016 nî lūn-bûn
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2016年の論文
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2016年学术文章
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name
PredRSA: a gradient boosted re ...... protein solvent accessibility.
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PredRSA: a gradient boosted re ...... protein solvent accessibility.
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PredRSA: a gradient boosted re ...... protein solvent accessibility.
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PredRSA: a gradient boosted re ...... protein solvent accessibility.
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PredRSA: a gradient boosted re ...... protein solvent accessibility.
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PredRSA: a gradient boosted re ...... protein solvent accessibility.
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PredRSA: a gradient boosted re ...... protein solvent accessibility.
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10.1186/S12859-015-0851-2
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17 Suppl 1
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2016-01-11T00:00:00Z
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1048356074