Predicting essential genes in prokaryotic genomes using a linear method: ZUPLS.
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An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms.Selection of key sequence-based features for prediction of essential genes in 31 diverse bacterial speciesAn integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features.Sequence-based information-theoretic features for gene essentiality prediction.A Comprehensive Overview of Online Resources to Identify and Predict Bacterial Essential Genes.Aspartate-β-semialdeyhyde dehydrogenase as a potential therapeutic target of Mycobacterium tuberculosis H37Rv: Evidence from in silico elementary mode analysis of biological network model.
P2860
Predicting essential genes in prokaryotic genomes using a linear method: ZUPLS.
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Predicting essential genes in prokaryotic genomes using a linear method: ZUPLS.
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Predicting essential genes in prokaryotic genomes using a linear method: ZUPLS.
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Predicting essential genes in prokaryotic genomes using a linear method: ZUPLS.
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Predicting essential genes in prokaryotic genomes using a linear method: ZUPLS.
@ast
Predicting essential genes in prokaryotic genomes using a linear method: ZUPLS.
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Predicting essential genes in prokaryotic genomes using a linear method: ZUPLS.
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Predicting essential genes in prokaryotic genomes using a linear method: ZUPLS.
@ast
Predicting essential genes in prokaryotic genomes using a linear method: ZUPLS.
@en
Predicting essential genes in prokaryotic genomes using a linear method: ZUPLS.
@nl
P2093
P2860
P356
P1433
P1476
Predicting essential genes in prokaryotic genomes using a linear method: ZUPLS.
@en
P2093
P2860
P304
P356
10.1039/C3IB40241J
P577
2014-03-07T00:00:00Z