Quantifying cumulative phenotypic and genomic evidence for procedural generation of metabolic network reconstructions
Fig 5
The E. coli CANYUNs Model performs better than iML1515 and CarveMe when simulating growth on all known phenotypic data.
(A) The CarveMe model without gapfilling has a base accuracy of 52% and a Matthews Correlation Coefficient (MCC) of 0.09. (B) The CarveMe model we trained using all of the phenotypic data performs with an accuracy of 76% and an MCC of 0.29. However, there is a strong bias toward false positive predictions. (C) The manually curated E. coli K-12 model, iML1515, was not trained using all of the growth conditions. However, it performs with 75% accuracy and an MCC of 0.40 while maintaining a relatively even split between false positive predictions and false negative predictions. (D) The CANYUNs model we generated performs with 92% accuracy and an MCC of 0.78. The increased accuracy is primarily due to an improvement in true negative prediction rate.