General flow of processing collected data. by Reinhard Chun Wang Chau (12733662)

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System specification of high-performance computer. by Reinhard Chun Wang Chau (12733662)

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Fig 6 - by Reinhard Chun Wang Chau (12733662)

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SPIRIT diagram showing the schedule of enrollment, allocation, interventions, and assessments of this study. by Reinhard Chun Wang Chau (12733662)

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Fig 5 - by Reinhard Chun Wang Chau (12733662)

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Brief architecture of the Generative Adversarial Network (GAN) of this study. by Reinhard Chun Wang Chau (12733662)

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Fig 7 - by Reinhard Chun Wang Chau (12733662)

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

“…Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a <b>B</b>iomass <b>O</b>bjective <b>F</b>unction from experimental <b>dat</b>a. …”

Convergence curve of the GL-SSL Model’s training loss function, illustrating the trend of loss reduction over 300 iterations, reflecting the model’s stability and rapid convergence... by Jiangjiang He (9676377)

Constructing the dynamical law. by Robert Marsland (8616483)

Scatterplot of class probability vs the actual ACMG class for the XGBoost model across the validation set of 156 variants. by Aastha Vatsyayan (9981916)

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Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. by Gursimran Singh (575288)

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Scatterplot of class probability vs the actual ACMG class for the TabNet model across the validation set of 156 variants. by Aastha Vatsyayan (9981916)

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PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. by Gursimran Singh (575288)

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A complete list of the 73 features used in training the model, along with the ACMG attribute they provide information about, along with their description, as well as their datatype... by Aastha Vatsyayan (9981916)

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Functional enrichment analysis. by Junjie Su (368807)

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Scatterplot of class probability vs the predicted class for the XGBoost model across all VUS variants 251 exonic VUS variants that were a part of the validation dataset. by Aastha Vatsyayan (9981916)

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<b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

Scatterplot of class probability vs the predicted class for the TabNet model across all VUS variants 251 exonic VUS variants that were a part of the validation dataset. by Aastha Vatsyayan (9981916)

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Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling by Giacomo Janson (8138517)

“…The most widely employed tool to perform this task is MODELLER. This program implements the “modeling by satisfaction of spatial restraints” strategy and its core algorithm has not been altered significantly since the early 1990s. …”