Representation of the feature selection algorithm. by Betty Wutzl (6945248)

Binary confusion matrix. by Chen Zhu (445894)

The maximum accuracy (lowest error rate) with the least number of ANOVA-ranked genes achieved by different feature filtering methods and classification algorithms refined by SIRRFE... by Song Cui (553627)

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging by D. Andrew Brown (6584837)

Schematic view of the overall algorithm. by Betty Wutzl (6945248)

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

“…<b>(C)</b> The whole training data is then scanned, searching for these sequences and deriving a re-encoded dataset where interaction terms are binary features (i.e., 1 if sequence <i>i</i> is observed in <i>j</i>-th patient genotype, 0 otherwise). …”

Frequency-comparison plots for the eleven categories of linguistic features. by Olga Kellert (12254765)

Model training, optimisation, validation, and selection. by Maya Wardeh (4112014)

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

Super.Complex identifies likely protein complexes within a PPI network using a distributed supervised AutoML method. by Meghana Venkata Palukuri (11890672)

Schematic overview of SINATRA Pro: A novel framework for discovering biophysical signatures that differentiate classes of proteins. by Wai Shing Tang (9541179)

“…<p><b>(A)</b> The SINATRA Pro algorithm requires the following inputs: <i>(i)</i> (<i>x</i>, <i>y</i>, <i>z</i>)-coordinates corresponding to the structural position of each atom in every protein; <i>(ii)</i> <b>y</b>, a binary vector denoting protein class or phenotype (e.g., mutant versus wild-type); <i>(iii)</i> <i>r</i>, the cutoff distance for simplicial construction (i.e., constructing the mesh representation for every protein); <i>(iv)</i> <i>c</i>, the number of cones of directions; <i>(v)</i> <i>d</i>, the number of directions within each cone; <i>(vi)</i> <i>θ</i>, the cap radius used to generate directions in a cone; and <i>(vii)</i> <i>l</i>, the number of sublevel sets (i.e., filtration steps) used to compute the differential Euler characteristic (DEC) curve along a given direction. …”

Segmentation, trace denoising and spike extraction framework. by Changjia Cai (10647521)

Data generation process. by Nicola Dietler (12551766)

“…The obtained sequences feature pairwise correlations and conservation arising from selection on the trait (via the Hamiltonian in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012091#pcbi.1012091.e004" target="_blank">Eq 1</a>). …”

DataSheet_2_MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation.pdf by Francesca Piludu (10706391)

“…The model with the final feature set was achieved using the support vector machine binary classification algorithm.…”

DataSheet_1_MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation.xlsx by Francesca Piludu (10706391)

“…The model with the final feature set was achieved using the support vector machine binary classification algorithm.…”

Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf by Cecilia Lindig-León (7889777)

“…In this way, for each binary problem, the CSP algorithm produces features to determine if the specific body part is engaged in the task or not. …”

DataSheet_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx by Massaine Bandeira e Sousa (7866242)

“…Despite the increased complexity associated with binary classification, it remained more efficient, offering higher classification accuracy for samples and facilitating the selection of the most relevant time or variables, such as cooking time ≤ 30 minutes. …”

Table_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx by Massaine Bandeira e Sousa (7866242)

“…Despite the increased complexity associated with binary classification, it remained more efficient, offering higher classification accuracy for samples and facilitating the selection of the most relevant time or variables, such as cooking time ≤ 30 minutes. …”

Table_2_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.docx by Bettina Katalin Budai (13951317)

“…After filtering out the highly correlated and poorly reproducible features, the LASSO algorithm selected 10 CM phase RFs that were then used for model construction. …”

Table_1_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.DOCX by Bettina Katalin Budai (13951317)

“…After filtering out the highly correlated and poorly reproducible features, the LASSO algorithm selected 10 CM phase RFs that were then used for model construction. …”