An integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features.

Abstract excerpt

Prediction of essential genes helps to identify a minimal set of genes that are absolutely required for the appropriate functioning and survival of a cell. The available machine learning techniques for essential gene prediction have inherent problems, like imbalanced provision of training datasets, biased choice of the best model for a given balanced dataset, choice of a complex machine learning algorithm, and...

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