Drug-target affinity predictions improve with increasing drug similarity score compared to the training drugs. حسب Alexander Kroll (9304225)

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

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

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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)

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

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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)

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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)

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

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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)

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ProSmith model overview. حسب Alexander Kroll (9304225)

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

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Hyperparameters of the ProSmith gradient boosting models. حسب Alexander Kroll (9304225)

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Source Code for the Hierarchical Key Assignment Scheme (HKAS) حسب Baris Celiktas (22804778)

Tools used in implementing the RNN models. حسب Fahmi H. Quradaa (17896527)

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Table_2_BayesPI-BAR2: A New Python Package for Predicting Functional Non-coding Mutations in Cancer Patient Cohorts.docx حسب Kirill Batmanov (3722374)

"…It is evaluated in follicular lymphoma and skin cancer patients, by focusing on sequence variants in gene promoter regions. BayesPI-BAR2 is a useful tool for predicting functional non-coding mutations in whole genome sequencing data: it allows identification of novel transcription factors (TFs) whose binding is altered by non-coding mutations in cancer. …"

CSDMS @ HydroShare: find, access, operate, and couple data-model integration tools for reproducible research حسب Tian Gan (458720)

Python code that implements the Hoshen Kopleman algorithm used to identify the clusters shown in Figs 9 and 10. حسب Leonardo Costa Ribeiro (10439243)

"…<p>Python code that implements the Hoshen Kopleman algorithm used to identify the clusters shown in Figs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0285630#pone.0285630.g009" target="_blank">9</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0285630#pone.0285630.g010" target="_blank">10</a>.…"

PHOTONAI explorer. حسب Ramona Leenings (11167294)

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Secure Python Code Manager: A Tool for Protected Python Code Distribution and Management حسب Pavel Izosimov (20096259)

"…</li><li><b>Commercial Distribution</b>: Safely share Python code with clients or customers, implementing advanced <b>Python code protection tools</b> for sales or rentals.…"

Table_1_BayesPI-BAR2: A New Python Package for Predicting Functional Non-coding Mutations in Cancer Patient Cohorts.xlsx حسب Kirill Batmanov (3722374)

"…It is evaluated in follicular lymphoma and skin cancer patients, by focusing on sequence variants in gene promoter regions. BayesPI-BAR2 is a useful tool for predicting functional non-coding mutations in whole genome sequencing data: it allows identification of novel transcription factors (TFs) whose binding is altered by non-coding mutations in cancer. …"