A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data by Anthony S. Barente (14035175)

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An expectation-maximization algorithm for finding noninvadable stationary states. by Robert Marsland (8616483)

Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements by Pascal Wang (10130612)

“…<div>This directory contains Python 3 scripts implementing the Trajectory Adaptive Multilevel Sampling algorithm (TAMS), a variant of Adaptive Multilevel Splitting (AMS), for the study of rare events. …”

The ERAD pathway also contributes to ROMK mutant turnover in HEK293 cells. by Nga H. Nguyen (2075920)

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Maximum values of overlapping normalized mutual information (LFK) producced by clustering algorithms. by Alexander Ponomarenko (3163383)

Top 15 biological process concepts from GO that are enriched and linked to the TMGs. by Ravinder Sharma (13154079)

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GLIM: Greedy algorithm for edge selection in LIM. by Maurits H. W. Oostenbroek (11331123)

EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit by Gonzalo Colmenarejo (650249)

“…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …”

Functional enrichment of DEGs. by Man Wang (110466)

“…</p><p>Methods</p><p>The study employed a combination of differential expression analysis, weighted gene co-expression network analysis (WGCNA), and various machine learning algorithms to screen for characteristic genes. Gene set enrichment analysis (GSEA), gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to evaluate relevant biological functions and pathways. …”

Performance of optimization algorithms in six different network types. by Maurits H. W. Oostenbroek (11331123)

Plot depicting the feature importance of the top 15 features of each model. by Aastha Vatsyayan (9981916)

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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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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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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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Model hyperparameters used for the XGBoost model. by Aastha Vatsyayan (9981916)

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The Precision-Recall curve of both the models. by Aastha Vatsyayan (9981916)

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S4 Table - by Aastha Vatsyayan (9981916)

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Train and validation accuracies of the TabNet model across 187 epochs. by Aastha Vatsyayan (9981916)

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Primers used in site-directed mutagenesis. by Aastha Vatsyayan (9981916)

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The overview of the workflow followed for model development and variant classification with TabNet and XGBoost models. by Aastha Vatsyayan (9981916)

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