Comparison of algorithms across metrics in test problems. by Jihui Hu (19812736)

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Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

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

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Algorithm comparison experiment on the RSOD dataset. by Pingping Yan (462509)

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Function <i>χ</i>_<i>opt</i> superimposed with the phase-field function indicating the channel geometry. by Martina Bukač (20161460)

ANOVA was performed to test the differences between the algorithms for each indicator. by Jihui Hu (19812736)

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

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Classification algorithm implemented in SPARTA. by Baptiste Ruiz (20285217)

Study proposed algorithm. by Ainhoa Pérez-Guerrero (21377457)

“…The index of microvascular resistance (IMR) is a specific physiological parameter used to assess microvascular function. Invasive coronary assessment has been shown to be both feasible and safe. …”

Algorithm comparison experiment on the NWPU VHR-10 dataset. by Pingping Yan (462509)

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The phase-field function for three different network configurations considered in this work. by Martina Bukač (20161460)

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

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

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Biological Function Assignment across Taxonomic Levels in Mass-Spectrometry-Based Metaproteomics via a Modified Expectation Maximization Algorithm by Gelio Alves (51850)

“…To overcome this limitation, we implemented an expectation-maximization (EM) algorithm, along with a biological function database, within the MiCId workflow. …”

The real NGD values, one-step ahead training set predictions, and multi-step ahead test set predictions using the AI-AFTER algorithm for Kayseri. by Burak Alakent (463512)

Identification and functional analysis of hub genes. by Wei Ya Lan (22403712)

“…(B) PPI network visualized using Cytoscape software. (C, D) Top 10 hub genes identified using the Maximal Clique Centrality (MCC) algorithm; darker colors indicate higher centrality within the PPI network. …”

Comparison the results with and without Fourier Loss Function for Rain100H. by Chaobing Zheng (20896579)

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

Supplementary file 1_Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI.zip by Nina de Lacy (6559520)

“…Introduction<p>Internalizing disorders (depression, anxiety, somatic symptom disorder) are among the most common mental health conditions that can substantially reduce daily life function. Early adolescence is an important developmental stage for the increase in prevalence of internalizing disorders and understanding specific factors that predict their onset may be germane to intervention and prevention strategies.…”

Example of how the Resnet34UNet model functions. by Henrik Mustonen (20378529)