Predictive Modeling-Based Non-Invasive System for Myeloproliferative Neoplasms Detection via Peripheral Blood Sampling
Budget: $30 – $250 USD
I am currently working on a research project focused on developing a Predictive Modeling-Based Non-Invasive System for Myeloproliferative Neoplasms Detection via Peripheral Blood Sampling. I have a CSV dataset containing 412 samples of healthy individuals and MPN patients, including gene expression data, sex, and age. I am seeking your expertise to assist with the following tasks:
1. Organizing and Harmonizing the Data:
- Ensure all gene identifiers under the ID_ref column, such as (1007_s_at) and (TC04002953.hg.1), are consistent and harmonized across the dataset, and document each gene’s actual meaning before harmonizing them, I need to know what each new gene identifier reference to.
- Properly label and organize the dataset to distinguish healthy samples from MPN patients using "healthy" for healthy individuals and the specific MPN subtype for diagnosed patients.
2. Feature Engineering to Identify Key Genes:
- Identify the most important genes that indicate the existence of MPNs and the specific subtype using feature engineering techniques : Random Forest Feature Importance.
- Provide a list of these genes along with their significance in MPN detection as a data graph.
3. Training a Machine Learning Model:
- Train a Random Forest model using an 80/20 split to predict whether a person has an MPN.
- If MPN is detected, classify the specific type of MPN.
- Use gene expressions, sex, and age as features for the model.
4. Delivering Results:
- The output should indicate:
- Whether a person is infected with MPNs or not.
- If infected, the specific type of MPN along with a likelihood percentage.
- Provide an explanation of the results, including the key genes that contributed to the prediction and their importance in the model's decision-making process.
Notes:
- Missing Data Handling: Implement strategies to address any missing values in the dataset, such as imputation methods or exclusion criteria, to ensure data integrity.
- Implement data visualizations for the following performance metrics: Confusion Matrix, Accuracy, Precision, Recall, F1 Score, ROC Curve with AUC, Specificity, Matthews Correlation Coefficient (MCC), Logarithmic Loss, and Calibration Curve.
1. Organizing and Harmonizing the Data:
- Ensure all gene identifiers under the ID_ref column, such as (1007_s_at) and (TC04002953.hg.1), are consistent and harmonized across the dataset, and document each gene’s actual meaning before harmonizing them, I need to know what each new gene identifier reference to.
- Properly label and organize the dataset to distinguish healthy samples from MPN patients using "healthy" for healthy individuals and the specific MPN subtype for diagnosed patients.
2. Feature Engineering to Identify Key Genes:
- Identify the most important genes that indicate the existence of MPNs and the specific subtype using feature engineering techniques : Random Forest Feature Importance.
- Provide a list of these genes along with their significance in MPN detection as a data graph.
3. Training a Machine Learning Model:
- Train a Random Forest model using an 80/20 split to predict whether a person has an MPN.
- If MPN is detected, classify the specific type of MPN.
- Use gene expressions, sex, and age as features for the model.
4. Delivering Results:
- The output should indicate:
- Whether a person is infected with MPNs or not.
- If infected, the specific type of MPN along with a likelihood percentage.
- Provide an explanation of the results, including the key genes that contributed to the prediction and their importance in the model's decision-making process.
Notes:
- Missing Data Handling: Implement strategies to address any missing values in the dataset, such as imputation methods or exclusion criteria, to ensure data integrity.
- Implement data visualizations for the following performance metrics: Confusion Matrix, Accuracy, Precision, Recall, F1 Score, ROC Curve with AUC, Specificity, Matthews Correlation Coefficient (MCC), Logarithmic Loss, and Calibration Curve.