<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.…”

<b>Gene Catalog and Associated Functional Data</b> for BioProject PRJNA1311452 by Satheeswaran Thangaraj (22187269)

The eVIP algorithm uses RNA-seq data to predict the function of somatic mutations. by Alexis M. Thornton (11068403)

Subjects:

Gene function enrichment analysis: GO function enrichment analysis (A) and KEGG pathway enrichment analysis (B). by Yaling Hu (11648397)

MSVM-RFE algorithm for screening key genes. by Ya xi Zhu (11624730)

(a-b) The LASSO algorithm was used for hyperparameter tuning and identification of the important predictive features. by Devi Soorya Narayana Sasikumar (22331743)

Subjects:

FAR-1: A Fast Integer Reduction Algorithm Compared to Collatz and Half-Collatz by Faizan Ali (21689861)

Subjects:

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

“…<i>(b)</i> Metabolic byproducts move the relevant unperturbed state from <b>R</b>⁰ (gray ‘x’) to (black ‘x’), which is itself a function of the current environmental conditions. …”

<b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

Verification of the in-line PS function. by Raul Castañeda (13932971)

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

Algorithm of genetic workup. by Idoia Martinez de Lapiscina (16528261)

Subjects:

Functional validation of the metastasis-predictive genes inferred by PLUS from TCGA data. by Junyi Zhou (495644)

The number of core promoter elements and TF binding sites predicted for four promoters with YAPP, GPMiner and ElemeNT algorithms. by Ali Hosseini Rad S. M. (9167497)

The common genes and their functional analysis. by Bing Yang (142278)

IDEN algorithm and gene pair-based drug repurposing. by Yao Jiang (1469614)

The results of LASSO regression algorithm. by Xue Fu (549661)

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. …”

Results of the application of different clustering algorithms to average functional connectivity from healthy subjects. by Francisco Páscoa dos Santos (16510676)

“…Inertia was calculated using the scikit learn module in Python. B) Resulting cluster distance from hierarchical clustering to averaged functional connectivity from healthy subjects, with different numbers of clusters. …”

Functional enrichment of 17 gene clusters identified in down-regulated drought tolerant network (dDTN) using MCL algorithm. by Sanchari Sircar (198485)