Image 2_Development and validation of machine learning-based MRI radiomics models for preoperative lymph node staging in T3 rectal cancer.tif by Xuelei Qubie (22195975)

“…Radiomics features were extracted from high-resolution T2-weighted imaging (T2WI) of primary tumor. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm. …”

Table 1_Development and validation of machine learning-based MRI radiomics models for preoperative lymph node staging in T3 rectal cancer.doc by Xuelei Qubie (22195975)

“…Radiomics features were extracted from high-resolution T2-weighted imaging (T2WI) of primary tumor. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm. …”

Image 4_Development and validation of machine learning-based MRI radiomics models for preoperative lymph node staging in T3 rectal cancer.tif by Xuelei Qubie (22195975)

“…Radiomics features were extracted from high-resolution T2-weighted imaging (T2WI) of primary tumor. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm. …”

Data Sheet 1_Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a breakthrough with interpretable machine learning.csv by Weijun Zhou (2215300)

“…</p>Methods<p>From December 2016 to December 2023, we retrospectively analyzed 710 PTMC patients who underwent thyroidectomies. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) regression method, alongside the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm in conjunction with multivariate logistic regression. …”

Data Sheet 1_Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model.docx by Fuyu Guo (4588312)

“…Objective<p>To explore the value of combined radiomics and deep learning models using different machine learning algorithms based on mammography (MG) and magnetic resonance imaging (MRI) for predicting axillary lymph node metastasis (ALNM) in breast cancer (BC). …”

Workflow visualization of the application, illustrating the required steps and exportable outputs. by Nicolai Kraus (19949667)

Comparison data 7 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

“…Several filtering options (selection of behaviors and individuals) together with options to set node and edge properties (in the network drawings) allow for interactive customization of the output drawings, which can also be downloaded afterwards. …”

Sample data for <i>Neolamprologus multifasciatus</i>. by Nicolai Kraus (19949667)

“…Several filtering options (selection of behaviors and individuals) together with options to set node and edge properties (in the network drawings) allow for interactive customization of the output drawings, which can also be downloaded afterwards. …”

Sample data for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

“…Several filtering options (selection of behaviors and individuals) together with options to set node and edge properties (in the network drawings) allow for interactive customization of the output drawings, which can also be downloaded afterwards. …”

Comparison data 3 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

“…Several filtering options (selection of behaviors and individuals) together with options to set node and edge properties (in the network drawings) allow for interactive customization of the output drawings, which can also be downloaded afterwards. …”

Sample data for <i>Telmatochromis temporalis</i>. by Nicolai Kraus (19949667)

“…Several filtering options (selection of behaviors and individuals) together with options to set node and edge properties (in the network drawings) allow for interactive customization of the output drawings, which can also be downloaded afterwards. …”

Comparison data 4 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

“…Several filtering options (selection of behaviors and individuals) together with options to set node and edge properties (in the network drawings) allow for interactive customization of the output drawings, which can also be downloaded afterwards. …”

Comparison data 1 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

“…Several filtering options (selection of behaviors and individuals) together with options to set node and edge properties (in the network drawings) allow for interactive customization of the output drawings, which can also be downloaded afterwards. …”

Comparison data 2 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

“…Several filtering options (selection of behaviors and individuals) together with options to set node and edge properties (in the network drawings) allow for interactive customization of the output drawings, which can also be downloaded afterwards. …”

Comparison data 5 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

“…Several filtering options (selection of behaviors and individuals) together with options to set node and edge properties (in the network drawings) allow for interactive customization of the output drawings, which can also be downloaded afterwards. …”

Comparison data 6 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

“…Several filtering options (selection of behaviors and individuals) together with options to set node and edge properties (in the network drawings) allow for interactive customization of the output drawings, which can also be downloaded afterwards. …”

Data Sheet 1_The deep learning radiomics nomogram helps to evaluate the lymph node status in cervical adenocarcinoma/adenosquamous carcinoma.docx by Mei Ling Xiao (6657629)

“…The radscore (RS) and DL score (DLS) were independently obtained after repeatability test, Pearson correlation coefficient (PCC), minimum redundancy maximum relevance (MRMR), and least absolute shrinkage and selection operator (LASSO) algorithm performed on the radiomics and DL feature sets. …”

Data Sheet 1_Application of radiomics-based prediction model to predict preoperative lymph node metastasis in prostate cancer: a systematic review and meta-analysis.docx by Yanghuang Zheng (21575123)

“…The subgroup analysis showed that the least absolute shrinkage and selection operator regression algorithm had the higher diagnostic sensitivity, with a pooled sensitivity of 0.96 (95% CI [0.90 - 1.00]) (p = 0.02), while the random forest algorithm was the opposite, with a pooled sensitivity of 0.48 (95% CI [0.16 - 0.80]) (p = 0.01). …”

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

Results of machine learning based biomarker screening analysis. by Yatan Li (21439415)