A sample confusion matrix of binary classifier. by Eric Komla Anku (17959299)

Smaller gene set of DFC was selected by a unique selection criterion. by Takeru Fujii (11731550)

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

Parameter settings. by Yang Cao (53545)

“…Finally, the paper incorporates the sampling concept of elite individuals from the Estimation of Distribution Algorithm (EDA) to regenerate new solutions through the selection process in DE. …”

Example depiction of the autoML process. by Cynthia Lokker (3813832)

“…The final selected algorithm, combining a LightGBM (gradient boosting machine) model trained in each dataset, maintained high sensitivity and achieved 57% specificity in the retrospective validation test and 53% in the prospective study. …”

Illustration of the workflow combining BayesFlow with missing data encoding. by Zijian Wang (481291)

Interpretability analysis. by Michela C. Massi (14599915)

Flowchart scheme of the ML-based model. by Noshaba Qasmi (20405009)

Processed dataset to train and test the WGAN-GP_IMOA_DA_Ensemble model by Ramya Chinnasamy (21633527)

Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield... by Uttam Khatri (12689072)

“…Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. …”

Equivalent Mutants Analysed via Deductive Verification by Serge Demeyer (5133878)

“…</p><p dir="ltr">Most of the samples selected above lacked loops. We therefore added three search algorithms: BinarySearch, FindLast and FindMin.…”

Structure-based antibody paratope prediction with 3D Zernike descriptors and SVM by Sebastian Daberdaku (4391767)

“…<br><br><b>seq_descriptors_4.5.tar.gz -</b> Contains the sequence descriptors generated from the antibody structures in the development/validation set which were used for feature selection with the Randomized Logistic Regression algorithm.…”

DataSheet_1_Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.docx by Jun Zhang (48506)

“…The dataset was divided randomly into four distinct subsets: a training set comprising 712 samples, an internal validation set with 178 samples, an external validation set containing 111 samples, and a prospective validation set consisting of 75 samples. …”

Psoas muscle CT radiomics-based machine learning models to predict response to infliximab in patients with Crohn’s disease by Zhuoyan Chen (12193358)

“…<i>Z</i> score standardization and independent sample <i>t</i> test were applied to identify optimal predictive features, which were then utilized in seven ML algorithms for training and validation. …”

Image_1_Validation of miRNA signatures for ovarian cancer earlier detection in the pre-diagnosis setting using machine learning approaches.pdf by Konrad Stawiski (4753380)

“…We employed the extreme gradient boosting (XGBoost) algorithm to train a binary classification model using 70% of the available data, while the model was tested on the remaining 30% of the dataset.…”

Gene Scores - Unadjusted - Regular by David Groenewoud (11131485)

“…<div>Gene scores for selected combinations of phenotypes and</div><div>SNV-to-gene mappings as calculated using genuine summary </div><div>statistics with MAGMA's (v1.08) SNP-Wise Mean algorithm. …”

Raw LC-MS/MS and RNA-Seq Mitochondria data by Stefano Martellucci (16284377)

“…The data were filtered as follows: (a) binary expression of a protein (i.e., protein exclusively identified in either scLRP1+/+ or scLRP1-/-) was only considered relevant if all scLRP1+/+ samples or all scLRP1-/- samples expressed the protein. …”

Data_Sheet_3_Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ.xlsx by Kirsty E. Waddington (5754545)

“…Thirty patients became ADA positive during the first year of treatment (ADA+). We tested the efficacy of six binary classification models using 10-fold cross validation; k-nearest neighbors, decision tree, random forest, support vector machine and lasso (Least Absolute Shrinkage and Selection Operator) logistic regression with and without interactions.…”

Image_1_Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ.JPEG by Kirsty E. Waddington (5754545)

“…Thirty patients became ADA positive during the first year of treatment (ADA+). We tested the efficacy of six binary classification models using 10-fold cross validation; k-nearest neighbors, decision tree, random forest, support vector machine and lasso (Least Absolute Shrinkage and Selection Operator) logistic regression with and without interactions.…”

Image_2_Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ.JPEG by Kirsty E. Waddington (5754545)

“…Thirty patients became ADA positive during the first year of treatment (ADA+). We tested the efficacy of six binary classification models using 10-fold cross validation; k-nearest neighbors, decision tree, random forest, support vector machine and lasso (Least Absolute Shrinkage and Selection Operator) logistic regression with and without interactions.…”