Automated Author ProfileTalebi, Amin
Department of Physiology and Medical Physics, School of Medicine, Baqiyatallah University of Medical Sciences, Tehran, Iran0009-0006-0676-9816
Talebi, Amin
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.9 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This repository contains the complete source code and dataset for the manuscript "The Application of Radiomics and Machine Learning in Diagnosis of Pancreatic Cancer" submitted to the Iranian Journal of Medical Sciences.The study develops and validates machine learning models using CT-based radiomics features to distinguish pancreatic adenocarcinoma from normal pancreatic tissue. The repository enables full reproducibility of all analyses presented in the manuscript, including:Feature Selection: Implementation of three feature selection methods (Mutual Information, LASSO, Recursive Feature Elimination)Machine Learning Modeling: Three classifiers (Random Forest, Support Vector Machine, Logistic Regression) evaluated using 5-fold stratified cross-validationPerformance Evaluation: Comprehensive metrics including Accuracy, Precision, Sensitivity, F1-score, PPV, NPV, and AUC-ROCModel Interpretability: SHAP (SHapley Additive exPlanations) analysis for feature importance interpretation
Authors
- Shankayi, Zeinab ;
- akhavan moghadam, jamal ;
- Sepandi, Mojtaba ;
- Chartab Mohammadi, Taha ;
- Rahmati zadeh, Ali ;
- Talebi, Amin
This repository contains the complete source code and dataset for the manuscript "The Application of Radiomics and Machine Learning in Diagnosis of Pancreatic Cancer" submitted to the Iranian Journal of Medical Sciences.The study develops and validates machine learning models using CT-based radiomics features to distinguish pancreatic adenocarcinoma from normal pancreatic tissue. The repository enables full reproducibility of all analyses presented in the manuscript, including:Feature Selection: Implementation of three feature selection methods (Mutual Information, LASSO, Recursive Feature Elimination)Machine Learning Modeling: Three classifiers (Random Forest, Support Vector Machine, Logistic Regression) evaluated using 5-fold stratified cross-validationPerformance Evaluation: Comprehensive metrics including Accuracy, Precision, Sensitivity, F1-score, PPV, NPV, and AUC-ROCModel Interpretability: SHAP (SHapley Additive exPlanations) analysis for feature importance interpretation
Authors
- Shankayi, Zeinab ;
- akhavan moghadam, jamal ;
- Sepandi, Mojtaba ;
- Chartab Mohammadi, Taha ;
- Rahmati zadeh, Ali ;
- Talebi, Amin