Read2Pheno classifier architecture: The input is a one-hot coded 16S rRNA sequence with length <i>T</i>. حسب Zhengqiao Zhao (6400832)

Comparison of phenotypic predictions and metabolite composition between the three steps of BOFdat, the original <i>i</i>ML1515 BOF, SEED, and BOSS. حسب Jean-Christophe Lachance (6619307)

الموضوعات:

BOFdat Step 1: Calculating the biomass objective function stoichiometric coefficients (BOFsc) for the 4 principal macromolecular categories of the cell. حسب Jean-Christophe Lachance (6619307)

الموضوعات:

BOFdat Step 3: Identifying species-specific metabolic end goals. حسب Jean-Christophe Lachance (6619307)

الموضوعات:

Identification of metabolic end goals by BOFdat Step 3. حسب Jean-Christophe Lachance (6619307)

الموضوعات:

BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data حسب Jean-Christophe Lachance (6619307)

الموضوعات:

The three-step workflow for generating biomass objective functions from experimental data with BOFdat. حسب Jean-Christophe Lachance (6619307)

الموضوعات:

BOFdat Step 2: Identifying and calculating the stoichiometric coefficients of coenzymes and inorganic ions. حسب Jean-Christophe Lachance (6619307)

الموضوعات:

Toxicity profile analysis of top six hits along KDE plot which represent joint distribution of carcinogenicity and oral rat acute toxicity by employing Python Seaborn module. حسب Iqra Ali (15372725)

Performance metrics for ProSmith and previously published methods for DTA prediction on the random split of the Davis dataset. حسب Alexander Kroll (9304225)

الموضوعات:

Replacing the final prediction layer of end-to-end trained Transformer Networks with gradient boosting models improves performance. حسب Alexander Kroll (9304225)

الموضوعات:

ProSmith outperforms the ESP model in the prediction of enzyme-substrate pairs especially for molecules with limited representation in the test data. حسب Alexander Kroll (9304225)

الموضوعات:

Performance metrics for all three trained gradient boosting models with different input vectors and for their combined weighted mean prediction. حسب Alexander Kroll (9304225)

الموضوعات:

Coefficient of determination <i>R</i>² of ProSmith for different splitting scenarios of the Davis dataset. حسب Alexander Kroll (9304225)

الموضوعات:

We divided the ESP test set into subsets according to the number of positive data points with the same small molecule in the training set. حسب Alexander Kroll (9304225)

الموضوعات:

We divided the ESP test set into subsets according to the maximal sequence identity of an enzyme compared to all training enzymes. حسب Alexander Kroll (9304225)

الموضوعات:

Performance metrics of ProSmith and previously published methods for the prediction of Michaelis constants <i>K</i>_M. حسب Alexander Kroll (9304225)

الموضوعات:

ProSmith outperforms previous models in the prediction of Michaelis constants <i>K</i>_M especially for enzymes not highly similar to proteins in the training set. حسب Alexander Kroll (9304225)

الموضوعات:

ProSmith model overview. حسب Alexander Kroll (9304225)

الموضوعات:

Hyperparameters of the ProSmith Transformer Network. حسب Alexander Kroll (9304225)

الموضوعات: