Parallel scalable simulations of biological neural networks using TensorFlow: A beginner's guide by Rishika Mohanta (7327013)

“…TensorFlow is a Python-based open-source package designed for machine learning algorithms. …”

Quanfima: An open source Python package for automated fiber analysis of biomaterials by Roman Shkarin (6580823)

“…Though biomaterials processing workflows are implemented, there are no software tools for fiber analysis that can be easily integrated into such workflows. …”

Presentation1_NeuroPack: An Algorithm-Level Python-Based Simulator for Memristor-Empowered Neuro-Inspired Computing.pdf by Jinqi Huang (12433320)

“…In this study, we present NeuroPack, a modular, algorithm-level Python-based simulation platform that can support studies of memristor neuro-inspired architectures for performing online learning or offline classification. …”

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

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Performance metrics for ProSmith and previously published methods for DTA prediction for different splitting scenarios. by Alexander Kroll (9304225)

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For accurate DTA predictions, ProSmith requires training on identical drugs but not on similar proteins. by 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. by Alexander Kroll (9304225)

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

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Performance metrics for ProSmith and ESP for the prediction of enzyme-substrate pairs. by 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. by Alexander Kroll (9304225)

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

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ProSmith model overview. by Alexander Kroll (9304225)

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

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Enzyme-substrate pair predictions improve with increasing substrate similarity compared to training substrates. by Alexander Kroll (9304225)

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

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