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. …”

Automated diagnosis of heart valve degradation using novelty detection algorithms and machine learning by Bernhard Vennemann (7434353)

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Generated blended data using mouse brain cells and human embryonic heart cells, labeled as <i>Mix</i>[<i>a</i>,<i>b</i>,<i>c</i>], indicates the proportion of mouse brain cells fou... by Nuray Sogunmez Erdogan (21533872)

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. …”

Boxplot of IWOAIKFS versus other algorithms over all datasets (<i>k</i> = 5). by Wei Liu (20030)

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. …”

Table 1_Establishment of reliable identification algorithms for acute heart failure or acute exacerbation of chronic heart failure using clinical data from a medical information da... by Ryusuke Inoue (4617505)

“…Introduction<p>This study aimed to evaluate the validity of algorithms based on electronic health data in identifying cases of acute heart failure and acute exacerbation of chronic heart failure at multiple institutions using the Medical Information Database Network (MID-NET®) in Japan.…”

Effect of model hyperparameters on decision function. by Bernhard Vennemann (7434353)

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The schematic view of the Radial Basis Function (RBF) algorithm. by Mohsen Yoosefzadeh-Najafabadi (6917399)

EALM algorithm. by Weibo Wang (782424)

Comparisons of M-DATA and mTADA under different strengths of annotations. by Yuhan Xie (9937449)

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Power and FDR of M-DATA under model misspecification. by Yuhan Xie (9937449)

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Simulation of comparison with mTADA. by Yuhan Xie (9937449)

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Pleiotropic effect boosts power for M-DATA multi-trait models. by Yuhan Xie (9937449)

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Venn diagram of identified genes in different models. by Yuhan Xie (9937449)

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Comparison of M-DATA multi-trait models with mTADA. by Yuhan Xie (9937449)

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Venn diagram of genes identified by M-DATA and mTADA for CHD. by Yuhan Xie (9937449)

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Power performance under different strengths of annotations. by Yuhan Xie (9937449)

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Results of real data application. by Yuhan Xie (9937449)

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Multi-trait analysis can accurately estimate the proportion of shared risk genes and single-trait risk genes. by Yuhan Xie (9937449)

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