Data_Sheet_1_sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and t Statistic.docx حسب Pijush Das (3196647)

"…The “sigFeature” R package is centered around a function called “sigFeature,” which provides automatic selection of features for the binary classification. Using six publicly available microarray data sets (downloaded from Gene Expression Omnibus) with different biological attributes, we further compared the performance of “sigFeature” to three other feature selection algorithms. …"

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

Partial dependence plots (A – G) and the resulting clustered feature importance (H) for each feature and trained model. حسب Daniel Walke (21680915)

Fairness in Machine Learning: A Review for Statisticians حسب Xianwen He (22529252)

"…We organize these fairness-enhancing mechanisms into three categories—pre-processing, in-processing, and post-processing—corresponding to different stages of the machine learning lifecycle and varying levels of access to the underlying algorithm. The discussion focuses on fairness in binary classification models using numerical tabular data, which serve as a foundation for addressing fairness in more complex algorithms. …"

PathOlOgics_RBCs Python Scripts.zip حسب Ahmed Elsafty (16943883)

"…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …"

Predicting childhood obesity using electronic health records and publicly available data حسب Robert Hammond (3631525)

"…</p><p>Methods and findings</p><p>We trained a variety of machine learning algorithms to perform both binary classification and regression. …"

Demonstration data on the set up of consumer wearable device for exposure and health monitoring in population studies حسب Antonis Michanikou (8996667)

"…The Variables included in the first three excel tabs were the following: Participant ID (Unique serial number for patient participating in the study), % Time Before (Percentage of time with data before protocol implementation), % Time After (Percentage of time with data after protocol implementation), Timestamp (Date and time of event occurrence), Indoor/Outdoor (Categorical- Classification of GPS signals to Indoor and Outdoor and null(missing value) based on distance from participant home), Filling algorithm (Imputation algorithm), SSID (Wireless network name connected to the smartwatch), Wi-Fi Signal Strength (Connection strength via Wi-Fi between smartwatch and home’s wireless network. (0 maximum strength), IMEI (International mobile equipment identity. …"

DataSheet1_Comparison of Descriptor- and Fingerprint Sets in Machine Learning Models for ADME-Tox Targets.docx حسب Álmos Orosz (12828104)

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

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

DataSheet1_A tumor-associated endothelial signature score model in immunotherapy and prognosis across pan-cancers.PDF حسب Shuzhao Chen (9649139)

DataSheet3_A tumor-associated endothelial signature score model in immunotherapy and prognosis across pan-cancers.PDF حسب Shuzhao Chen (9649139)

DataSheet2_A tumor-associated endothelial signature score model in immunotherapy and prognosis across pan-cancers.PDF حسب Shuzhao Chen (9649139)

Data_Sheet_1_Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor.docx حسب Xueyan Zhang (277449)

DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx حسب Yuhong Huang (115702)

Data_Sheet_1_Machine learning models identify micronutrient intake as predictors of undiagnosed hypertension among rural community-dwelling older adults in Thailand: a cross-sectio... حسب Niruwan Turnbull (11506910)

Supplementary Material for: Detecting atrial fibrillation by artificial intelligence enabled neuroimaging examination. حسب figshare admin karger (2628495)

"…We hypothesise that machine learning algorithm increases the accurate classification of MRIs of stroke patients into those due to AF vs large artery atherosclerosis. …"

The Value of Dynamic Grip Force Modulation as a Potential Biomarkerfor Hand Function Recovery Following Stroke حسب Kirstin-Friederike Heise (7518953)

"…</p><p dir="ltr">We used a supervised machine learning algorithm (support vector machine, SVM, with k-fold cross-validation) for binary classification of groups (stroke versus control group), task conditions (uni- versus bimanual), and to quantify the active range of motion evaluated with upper extremity Fugl-Meyer Assessment (UEFMA) within the stroke group alone.…"

Accessibility of translation initiation sites is the strongest predictor of heterologous protein expression in <i>E. coli</i>. حسب Bikash K. Bhandari (11524776)

"…This partition function approach can be customised and executed using the algorithm implemented in RNAplfold. B: mRNA features ranked by Gini importance for random forest classification of the expression outcomes of the PSI:Biology targets (N = 8,780 and 2,650, ‘success’ and ‘failure’ groups, respectively). …"

An intelligent decision-making system for embryo transfer in reproductive technology: a machine learning-based approach حسب Sanaa Badr (20628838)

"…Binary classification models were developed to classify cases into two groups: those transferring two or fewer embryos and those transferring three or four. …"