Dataset for 'An Automated Workflow of Deep Learning Ensemble Model with Principal Component Analysis for Enhanced Material Characterisation in Energy Dispersive Spectroscopy' by Fanzhi Su (21005174)

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Code snippets to add user-specific custom functionality in PyNoetic. by Gursimran Singh (575288)

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

“…</p><p>Results</p><p>Using an independent validation dataset of ACMG & AMP classified variants, as well as a patient set of functionally validated variants, we showed how both algorithms perform and can be used to classify large numbers of variants in clinical as well as research settings.…”

Fitness function parameters. by Ankita Sharma (3183054)

Trainable parameters for different algorithms. by Anwar Hossain Efat (19942283)

LOTUS: A single- and multitask machine learning algorithm for the prediction of cancer driver genes by Olivier Collier (7444844)

Pair Potentials as Machine Learning Features by Jun Pei (1765720)

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Pair Potentials as Machine Learning Features by Jun Pei (1765720)

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Pair Potentials as Machine Learning Features by Jun Pei (1765720)

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Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. by Gursimran Singh (575288)

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

Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart. by Gursimran Singh (575288)

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ROC analysis of the Machine Learning Algorithm. by Stephan Ellmann (314565)

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

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). …”

<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.…”

DE genes detected by different DEA algorithms. by Jie Xue (417504)

“…Here, we compare 6 performance metrics on both simulated and real scRNA-seq datasets to assess the adaptability of 8 DEA approaches, with a particular emphasis on how well they function under small biological replications. …”

Variations of loss functions versus epoch number. by Xueliang Guo (4797057)

10-fold cross-validation evaluation of accuracy of animal tracking algorithms. by Catalin Mitelut (5587493)

“…<p>We labeled 1,000 frames per cohort under both day and night conditions (total 6,000 human labeled frames across three cohorts), and studied the accuracy of the animal tracking algorithm (SLEAP) as a function of the number of frames (<i>x</i>-axis) trained. …”

Data_Sheet_1_Decoding emotional resilience in aging: unveiling the interplay between daily functioning and emotional health.PDF by Minhua Guo (18392646)