Analysis of ABIDE dataset: Conventional regression frameworks for optimized algorithmic value. by Alexandria M. Jensen (6494957)

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Distribution of cross correlations in functional connectivity in ABIDE sample. by Alexandria M. Jensen (6494957)

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

“…<div><p>Community detection algorithms have been widely used to study the organization of complex networks like the brain. …”

Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline. by Borjan Milinkovic (21349908)

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

“…<i>(c)</i> Pseudocode for self-consistently computing <b>R</b>* and , which is identical to standard expectation-maximization algorithms employed for problems with latent variables in machine learning.…”

Examples of between- and within-community Beta distributions. by Alexandria M. Jensen (6494957)

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Workflow of algorithm for whole brain flow simulation. by Erlend Hodneland (391541)

<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)

Reverse engineering information flow using stimulus-encoding and stimulus-transfer estimation algorithms. by Gabriel Matías Lorenz (21094672)

Logistic kernel: Power and Type I error for cluster evaluation metric simulations. by Alexandria M. Jensen (6494957)

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ABIDE dataset subject demographics. by Alexandria M. Jensen (6494957)

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Linear kernel: Power and Type I error for cluster evaluation metric simulations. by Alexandria M. Jensen (6494957)

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P-values from analysis of ABIDE dataset for binary outcome of diagnostic group. by Alexandria M. Jensen (6494957)

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Examples of four simulated networks and adjacency matrices. by Alexandria M. Jensen (6494957)

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P-values from analysis of ABIDE dataset for continuous outcome of VIQ. by Alexandria M. Jensen (6494957)

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

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). …”

Consistency of model-derived predictive patterns as a function of sample diversity. by Oualid Benkarim (9707986)

Precise spatial delineation of brain cell types in adult mouse brain. by Nuray Sogunmez Erdogan (21533872)