<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

“…</li></ul><p dir="ltr">The scripts rely on the following Python packages. Where available, repository links are provided:</p><ol><li><b>NumPy</b>, version 1.22.1</li><li><b>SciPy</b>, version 1.7.3</li><li><b>PyGAD</b>, version 3.0.1 — https://pygad.readthedocs.io/en/latest/#</li><li><b>bees-algorithm</b>, version 1.0.2 — https://pypi.org/project/bees-algorithm</li><li><b>KrakenOS</b>, version 1.0.0.19 — https://github.com/Garchupiter/Kraken-Optical-Simulator</li><li><b>matplotlib</b>, version 3.5.2</li></ol><p dir="ltr">All scripts are modular and organized to reflect the design stages described in the manuscript.…”

Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

Link function. by Alina Bazarova (17138191)

Python-based Monte Carlo Simulation Tool by Yangfan Jiang (9372782)

Synthetic Realness: Authenticity as Algorithm (Reality Drift Working Paper Series, 2025) by Reality Drift Working Papers Series 2020-2025 (22445446)

“…<p dir="ltr">This paper explores the concept of synthetic realness: how authenticity is increasingly engineered by algorithms until the line between real and fake becomes functionally irrelevant. …”

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

“…This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evolution algorithm (DEA) and Nelder–Mead method (NMM). …”

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

Subjects:

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)

Subjects:

Copper exposure in <i>ATP7B</i> Knock-out HepG2 cells overexpressed with the plasmid containing wildtype and mutant <i>ATP7B</i> gene. by Aastha Vatsyayan (9981916)

Subjects:

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)

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

S4 Table - by Aastha Vatsyayan (9981916)

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

S2 Table - by Aastha Vatsyayan (9981916)

Subjects:

Barplot comparing the accuracy, MCC, NPV and PPV of 13 models with TabNet and XGBoost. by Aastha Vatsyayan (9981916)

Subjects:

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

Subjects: