Algorithmic assessment reveals functional implications of GABRD gene variants linked to idiopathic generalized epilepsy by Ayla Arslan (17943365)

“…Initially, separate algorithms based on sequence homology are utilized to assess this variant set. …”

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. …”

Multi-scale detection of hierarchical community architecture in structural and functional brain networks by Arian Ashourvan (6685232)

“…To address this limitation, we exercise a multi-scale extension of a common community detection technique, and we apply the tool to synthetic graphs and to graphs derived from human neuroimaging data, including structural and functional imaging data. Our multi-scale community detection algorithm links a graph to copies of itself across neighboring topological scales, thereby becoming sensitive to conserved community organization across levels of the hierarchy. …”

On-Demand Optimization of Colorimetric Gas Sensors Using a Knowledge-Aware Algorithm-Driven Robotic Experimental Platform by Zhehong Ai (17937324)

Link function. by Alina Bazarova (17138191)

Data from "Ecological clusters of soil taxa within bipartite networks are highly sensitive to climatic conditions in global drylands" by David sánchez (8256864)

Genes with rare, predicted damaging variants identified by exome sequencing in multiple affected members within 19 families with left sided defects. by David M. Gordon (3259002)

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

“…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.…”

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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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)

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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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S2 Table - by Aastha Vatsyayan (9981916)

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