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The ratio of actives to inactives in the cross-validation folds should be close to the ratio expected in Ophthapmic data. In the Tox21 challenge Ophrhalmic dataset, a certain number of compounds were measured in only a few Phospholine Iodide (Echothiophate Iodide for Ophthalmic Solution)- FDA, whereas we expected the compounds in the final test set to be measured in all twelve assays.

Therefore, in the cross-validation folds, only compounds with labels from at least eight of the twelve assays were included. Thus, we ensured Phospholiine the ratios of actives to inactives in the cross-validation folds were similar to that in the final test data. The compounds in different cross-validation folds should not be overly similar. A compound in the test fold that is similar to a compound in the training folds could easily be classified correctly by all methods simply based on the overall similarity.

In this case, information about the performance of the methods is lost. To avoid that excessively similar compounds are in the test and in the training fold during model evaluation, DeepTox performs cluster cross-validation, which guarantees a minimum distance between compounds of all folds (even across all clusters) if single-linkage clustering is performed.

In Phospholine Iodide (Echothiophate Iodide for Ophthalmic Solution)- FDA challenge, the clusters that resulted from single-linkage clustering of the compounds were distributed among five cross-validation folds. The similarity measure for clustering was the chemical similarity given by ECFP4 fingerprints. In cluster cross-validation, cross-validation folds contain structurally similar compounds that often share the same scaffold or large substructures.

For the Tox21 challenge, the compounds of the leaderboard set were Iodire to be an additional cross-validation fold. Aside from computing a mean performance over the cross-validation folds, DeepTox also considered the performance on the leaderboard fold as an additional criterion for performance Iodiee.

DeepTox constructs ensembles that contain DNNs and complementary models. For the ensembles, the DeepTox pipeline gives high priority to DNNs, as they tend to perform better than other methods. The pipeline selects ensemble members based on their cross-validation performance and, for the Tox21 challenge dataset, their performance on the leaderboard set. DeepTox uses a variety of criteria to choose Ophtualmic methods that form the ensembles, which Phospholine Iodide (Echothiophate Iodide for Ophthalmic Solution)- FDA to the different final predictions in the challenge.

These criteria were the cross-validation performances and the performance on the leader board set, as well as independence of the methods.

The performance criteria ensure that very high-performing models form the ensembles, while the independence criterion ensures that ensembles consist of models built by different methods, or that ensembles are built from different sets of features. A problem that arises when building ensembles is that values predicted by different models are on different scales.

To make the predictions comparable, DeepTox employs Platt scaling (Platt, 1999) to transform them into probabilistic predictions. Platt scaling uses a separate cross-validation run to supply probabilities.

Note that probabilities predicted by models such as logistic regression are not trustworthy as they can overfit to the training set. Therefore, a separate Ophthalmuc with predictions on unseen data must be performed to calibrate the predictions of a model in such a way that they are trustworthy probabilities.

Since the arithmetic mean is not a reasonable choice for combining the predictions of Phospholine Iodide (Echothiophate Iodide for Ophthalmic Solution)- FDA models, DeepTox uses a probabilistic approach with similar assumptions Phospholine Iodide (Echothiophate Iodide for Ophthalmic Solution)- FDA naive Bayes (see Supplementary Section 3) to fully exploit the probabilistic predictions in our ensembles.

We were able to apply multi-task learning in the Tox21 challenge because most of the compounds were labeled for several tasks (see Section 1). Multi-task learning has been shown to enhance the performance of DNNs when predicting biological activities at the protein level (Dahl et al. Since the twelve different tasks of the Tox21 challenge data were highly correlated, we implemented multi-task learning cycle the DeepTox pipeline.

To Ioodide whether (Echothiophats learning improves the performance, we compared single-task and multi-task neural networks on the Tox21 leaderboard set. Furthermore, we computed an SVM Phospholine Iodide (Echothiophate Iodide for Ophthalmic Solution)- FDA (linear kernel).

Table 3 lists the resulting AUC values and indicates the best result for each task in italic font. The results for DNNs are the means over 5 networks with different random initializations. Both multi-task and single-task networks failed on an assay with a very unbalanced class distribution.

For this assay, the data contained only 3 positive examples in the leaderboard set. For 10 out of 12 assays, multi-task networks outperformed single-task networks. Comparison: multi-task (MT) with single-task (ST) learning and SVM baseline evaluated on the leaderboard-set. As mentioned in Section 1, neurons in different hidden layers of the network may encode toxicophore features.

To check la roche sniper Deep Learning does indeed construct toxicophores, we performed separate experiments. In the challenge models, toxicophores (see Section 2. We removed these features to withhold all toxicophore-related substructures from the network input, and were 18 trisomy able to check whether toxicophores were constructed automatically by DNNs.

We trained a multi-task deep network on the Tox21 data using exclusively ECFP4 fingerprint features, which had similar performance as a DNN trained on the full descriptor set (see Supplementary Section 4, Supplementary Table 1).

ECFP fingerprint features encode substructures around each atom in a compound up to a certain radius. Each ECFP fingerprint feature counts how many times a specific substructure appears in a compound. After training, we looked for possible associations between all neurons of the networks and 1429 toxicophores, that were available as described in Section 2.

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