WGCNA and machine learning algorithms. by Shushu Wang (6870191)

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

Identification of hub genes from the PPI network by bottleneck algorithm using the cytoHubba Cytoscape plugin. by Cihat Erdogan (12529243)

Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

“…<div><p>Chest X-ray image classification plays an important role in medical diagnostics. Machine learning algorithms enhanced the performance of these classification algorithms by introducing advance techniques. …”

Two artificial intelligence algorithms give similar predicted 3D structures of <i>MTB</i> PhoR. by Wladimir Malaga (239246)

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

Genes that contribute to PCA and GO terms of enriched and differentially expressed genes. GO, gene ontology; PCA, principal component analysis. by Tomokatsu Udagawa (11675636)

Subjects:

Fitting mean gene expression to Hill function for gained SE and TE associated genes. by Johannes N. Wibisana (12709828)

“…<p>Hill function was fitted to the fold-change of mean gene expression compared to dose 0. …”

Genes associated with cluster analyses. by Tomokatsu Udagawa (11675636)

Subjects:

Analysis of gene clusters of the damaged GER. by Tomokatsu Udagawa (11675636)

Subjects:

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). …”

Information on the 23 Food and Drug Administration (FDA)-approved drugs that likely target three of the six hub genes. by Ravinder Sharma (13154079)

Subjects:

<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.…”

Gene expression of GLAST-Cre⁺ cells. by Tomokatsu Udagawa (11675636)

Subjects:

Highly enriched genes in Glast-Cre⁺ cells. by Tomokatsu Udagawa (11675636)

Subjects:

Sorting time is nominally similar between automated and manual sorting, but automation enables walk-away functionality. by Diane M. Wiener (1956583)

Subjects:

Top GO terms identified by the pglm (GLM-Net) algorithm. by F. William Townes (3216846)

Differential gene expression in the GER after ablation of Lgr5⁺ cells. by Tomokatsu Udagawa (11675636)

Subjects:

Gene content overlap in <i>RNF43 G659fs</i> mutation-specific pathways. by Alexis M. Thornton (11068403)

Subjects:

DEGs between control and damaged Glast-Cre⁺ cells. DEG, differentially expressed gene. by Tomokatsu Udagawa (11675636)

Subjects: