From 5c6429a38a58fecec732b254a05602f0dda632ae Mon Sep 17 00:00:00 2001 From: Md Aminul Islam Prodhan Date: Tue, 31 Dec 2024 19:42:09 -0600 Subject: [PATCH] rmd file updated --- README.Rmd | 117 ++++++++++++++++++++++++++++++----------- README.html | 148 ++++++++++++++++++++++++++++++++++++++++----------- README.md | 149 +++++++++++++++++++++++++++++++++++++++++----------- 3 files changed, 321 insertions(+), 93 deletions(-) diff --git a/README.Rmd b/README.Rmd index 176191c..fdd1fd3 100644 --- a/README.Rmd +++ b/README.Rmd @@ -1,55 +1,112 @@ + --- +title: "SENDQSAR" output: github_document --- - +# SENDQSAR: QSAR Modeling with SEND Database -```{r, include = FALSE} -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>", - fig.path = "man/figures/README-", - out.width = "100%" -) -``` +## About -# SENDQSAR +This package facilitates developing Quantitative Structure-Activity Relationship (QSAR) models using the SEND database. It streamlines data acquisition, preprocessing, descriptor calculation, and model evaluation, enabling researchers to efficiently explore molecular descriptors and create robust predictive models. - - +## Features -The goal of SENDQSAR is to ... +- **Automated Data Processing**: Simplifies data acquisition and preprocessing steps. +- **Comprehensive Analysis**: Provides z-score calculations for various parameters such as body weight, liver-to-body weight ratio, and laboratory tests. +- **Machine Learning Integration**: Supports classification modeling, hyperparameter tuning, and performance evaluation. +- **Visualization Tools**: Includes histograms, bar plots, and AUC curves for better data interpretation. -## Installation +## Functions Overview + +### Data Acquisition and Processing + +- `get_compile_data` - Fetches data from the database specified by the database path into a structured data frame for analysis. +- `get_bw_score` - Calculates body weight (BW) z-scores for each animal. +- `get_livertobw_zscore` - Computes liver-to-body weight z-scores. +- `get_lb_score` - Calculates z-scores for laboratory test (LB) results. +- `get_mi_score` - Computes z-scores for microscopic findings (MI). +- `get_liver_om_lb_mi_tox_score_list` - Combines z-scores of LB, MI, and liver-to-BW into a single data frame. +- `get_col_harmonized_scores_df` - Harmonizes column names across studies. + +### Machine Learning Preparation and Modeling + +- `get_ml_data_and_tuned_hyperparameters` - Prepares data and tunes hyperparameters for machine learning. +- `get_rf_model_with_cv` - Builds a random forest model with cross-validation and outputs performance metrics. +- `get_zone_exclusioned_rf_model_with_cv` - Introduces an indeterminate zone for improved classification accuracy. +- `get_imp_features_from_rf_model_with_cv` - Computes feature importance for model interpretation. +- `get_auc_curve_with_rf_model` - Generates AUC curves to evaluate model performance. + +### Visualization and Reporting + +- `get_histogram_barplot` - Creates bar plots for target variable classes. +- `get_reprtree_from_rf_model` - Builds representative decision trees for interpretability. +- `get_prediction_plot` - Visualizes prediction probabilities with histograms. + +### Automated Pipelines + +- `get_Data_formatted_for_ml_and_best.m` - Formats data for machine learning pipelines. +- `get_rf_input_param_list_output_cv_imp` - Automates preprocessing, modeling, and evaluation in one step. +- `get_zone_exclusioned_rf_model_cv_imp` - Similar to the above function, but excludes uncertain predictions based on thresholds. + +## Workflow -You can install the development version of SENDQSAR from [GitHub](https://github.com/) with: +1. **Input Database Path**: Provide the database path containing nonclinical study results for each STUDYID. +2. **Preprocessing**: Use functions 1-8 to clean, harmonize, and prepare data. +3. **Model Building**: Employ machine learning functions (9-18) for training, validation, and evaluation. +4. **Visualization**: Generate plots and performance metrics for better interpretation. -``` r -# install.packages("pak") -pak::pak("aminuldu07/SENDQSAR") +## Dependencies + +- `randomForest` +- `ROCR` +- `ggplot2` +- `reprtree` + +## Installation + +```R +# Install from GitHub +devtools::install_github("aminuldu07/SENDQSAR") ``` -## Example +## Examples -This is a basic example which shows you how to solve a common problem: +### Example 1: Basic Data Compilation -```{r example} +```R library(SENDQSAR) -## basic example code +data <- get_compile_data("/path/to/database") ``` -What is special about using `README.Rmd` instead of just `README.md`? You can include R chunks like so: +### Example 2: Z-Score Calculation -```{r cars} -summary(cars) +```R +bw_scores <- get_bw_score(data) +liver_scores <- get_livertobw_zscore(data) ``` -You'll still need to render `README.Rmd` regularly, to keep `README.md` up-to-date. `devtools::build_readme()` is handy for this. +### Example 3: Machine Learning Model + +```R +model <- get_rf_model_with_cv(data, n_repeats=10) +print(model$confusion_matrix) +``` -You can also embed plots, for example: +### Example 4: Visualization -```{r pressure, echo = FALSE} -plot(pressure) +```R +get_histogram_barplot(data, target_col="target_variable") ``` -In that case, don't forget to commit and push the resulting figure files, so they display on GitHub and CRAN. +## Contribution + +Contributions are welcome! Feel free to submit issues or pull requests via GitHub. + +## License + +This project is licensed under the MIT License - see the LICENSE file for details. + +## Contact + +For more information, visit the project GitHub Page or contact email@example.com. diff --git a/README.html b/README.html index 7a5ee86..7cd6f6e 100644 --- a/README.html +++ b/README.html @@ -601,41 +601,125 @@ - -

SENDQSAR

- - - - -

The goal of SENDQSAR is to …

+

SENDQSAR: QSAR +Modeling with SEND Database

+

About

+

This package facilitates developing Quantitative Structure-Activity +Relationship (QSAR) models using the SEND database. It streamlines data +acquisition, preprocessing, descriptor calculation, and model +evaluation, enabling researchers to efficiently explore molecular +descriptors and create robust predictive models.

+

Features

+ +

Functions Overview

+

Data Acquisition and +Processing

+ +

Machine Learning +Preparation and Modeling

+ +

Visualization and Reporting

+ +

Automated Pipelines

+ +

Workflow

+
    +
  1. Input Database Path: Provide the database path +containing nonclinical study results for each STUDYID.
    2. +
  2. Preprocessing: Use functions 1-8 to clean, +harmonize, and prepare data.
    3. +
  3. Model Building: Employ machine learning functions +(9-18) for training, validation, and evaluation.
    4. +
  4. Visualization: Generate plots and performance +metrics for better interpretation.
    5. +
+

Dependencies

+

Installation

-

You can install the development version of SENDQSAR from GitHub with:

-
# install.packages("pak")
-pak::pak("aminuldu07/SENDQSAR")
-

Example

-

This is a basic example which shows you how to solve a common -problem:

+
# Install from GitHub
+devtools::install_github("aminuldu07/SENDQSAR")
+

Examples

+

Example 1: Basic Data +Compilation

library(SENDQSAR)
-## basic example code
-

What is special about using README.Rmd instead of just -README.md? You can include R chunks like so:

-
summary(cars)
-#>      speed           dist       
-#>  Min.   : 4.0   Min.   :  2.00  
-#>  1st Qu.:12.0   1st Qu.: 26.00  
-#>  Median :15.0   Median : 36.00  
-#>  Mean   :15.4   Mean   : 42.98  
-#>  3rd Qu.:19.0   3rd Qu.: 56.00  
-#>  Max.   :25.0   Max.   :120.00
-

You’ll still need to render README.Rmd regularly, to -keep README.md up-to-date. -devtools::build_readme() is handy for this.

-

You can also embed plots, for example:

- - -

In that case, don’t forget to commit and push the resulting figure -files, so they display on GitHub and CRAN.

+data <- get_compile_data("/path/to/database") +

Example 2: Z-Score +Calculation

+
bw_scores <- get_bw_score(data)
+liver_scores <- get_livertobw_zscore(data)
+

Example 3: Machine Learning +Model

+
model <- get_rf_model_with_cv(data, n_repeats=10)
+print(model$confusion_matrix)
+

Example 4: Visualization

+
get_histogram_barplot(data, target_col="target_variable")
+

Contribution

+

Contributions are welcome! Feel free to submit issues or pull +requests via GitHub.

+

License

+

This project is licensed under the MIT License - see the LICENSE file +for details.

+

Contact

+

For more information, visit the project GitHub Page or contact email@example.com.

diff --git a/README.md b/README.md index 36f0f00..ffc7433 100644 --- a/README.md +++ b/README.md @@ -1,52 +1,139 @@ +SENDQSAR +================ + +# SENDQSAR: QSAR Modeling with SEND Database + +## About + +This package facilitates developing Quantitative Structure-Activity +Relationship (QSAR) models using the SEND database. It streamlines data +acquisition, preprocessing, descriptor calculation, and model +evaluation, enabling researchers to efficiently explore molecular +descriptors and create robust predictive models. + +## Features + +- **Automated Data Processing**: Simplifies data acquisition and + preprocessing steps. +- **Comprehensive Analysis**: Provides z-score calculations for various + parameters such as body weight, liver-to-body weight ratio, and + laboratory tests. +- **Machine Learning Integration**: Supports classification modeling, + hyperparameter tuning, and performance evaluation. +- **Visualization Tools**: Includes histograms, bar plots, and AUC + curves for better data interpretation. + +## Functions Overview + +### Data Acquisition and Processing + +- `get_compile_data` - Fetches data from the database specified by the + database path into a structured data frame for analysis. +- `get_bw_score` - Calculates body weight (BW) z-scores for each animal. +- `get_livertobw_zscore` - Computes liver-to-body weight z-scores. +- `get_lb_score` - Calculates z-scores for laboratory test (LB) results. +- `get_mi_score` - Computes z-scores for microscopic findings (MI). +- `get_liver_om_lb_mi_tox_score_list` - Combines z-scores of LB, MI, and + liver-to-BW into a single data frame. +- `get_col_harmonized_scores_df` - Harmonizes column names across + studies. + +### Machine Learning Preparation and Modeling + +- `get_ml_data_and_tuned_hyperparameters` - Prepares data and tunes + hyperparameters for machine learning. +- `get_rf_model_with_cv` - Builds a random forest model with + cross-validation and outputs performance metrics. +- `get_zone_exclusioned_rf_model_with_cv` - Introduces an indeterminate + zone for improved classification accuracy. +- `get_imp_features_from_rf_model_with_cv` - Computes feature importance + for model interpretation. +- `get_auc_curve_with_rf_model` - Generates AUC curves to evaluate model + performance. + +### Visualization and Reporting + +- `get_histogram_barplot` - Creates bar plots for target variable + classes. +- `get_reprtree_from_rf_model` - Builds representative decision trees + for interpretability. +- `get_prediction_plot` - Visualizes prediction probabilities with + histograms. + +### Automated Pipelines + +- `get_Data_formatted_for_ml_and_best.m` - Formats data for machine + learning pipelines. +- `get_rf_input_param_list_output_cv_imp` - Automates preprocessing, + modeling, and evaluation in one step. +- `get_zone_exclusioned_rf_model_cv_imp` - Similar to the above + function, but excludes uncertain predictions based on thresholds. + +## Workflow + +1. **Input Database Path**: Provide the database path containing + nonclinical study results for each STUDYID. +2. **Preprocessing**: Use functions 1-8 to clean, harmonize, and + prepare data. +3. **Model Building**: Employ machine learning functions (9-18) for + training, validation, and evaluation. +4. **Visualization**: Generate plots and performance metrics for better + interpretation. + +## Dependencies + +- `randomForest` +- `ROCR` +- `ggplot2` +- `reprtree` - +## Installation -# SENDQSAR +``` r +# Install from GitHub +devtools::install_github("aminuldu07/SENDQSAR") +``` - - +## Examples -The goal of SENDQSAR is to … +### Example 1: Basic Data Compilation -## Installation +``` r +library(SENDQSAR) +data <- get_compile_data("/path/to/database") +``` -You can install the development version of SENDQSAR from -[GitHub](https://github.com/) with: +### Example 2: Z-Score Calculation ``` r -# install.packages("pak") -pak::pak("aminuldu07/SENDQSAR") +bw_scores <- get_bw_score(data) +liver_scores <- get_livertobw_zscore(data) ``` -## Example - -This is a basic example which shows you how to solve a common problem: +### Example 3: Machine Learning Model ``` r -library(SENDQSAR) -## basic example code +model <- get_rf_model_with_cv(data, n_repeats=10) +print(model$confusion_matrix) ``` -What is special about using `README.Rmd` instead of just `README.md`? -You can include R chunks like so: +### Example 4: Visualization ``` r -summary(cars) -#> speed dist -#> Min. : 4.0 Min. : 2.00 -#> 1st Qu.:12.0 1st Qu.: 26.00 -#> Median :15.0 Median : 36.00 -#> Mean :15.4 Mean : 42.98 -#> 3rd Qu.:19.0 3rd Qu.: 56.00 -#> Max. :25.0 Max. :120.00 +get_histogram_barplot(data, target_col="target_variable") ``` -You’ll still need to render `README.Rmd` regularly, to keep `README.md` -up-to-date. `devtools::build_readme()` is handy for this. +## Contribution + +Contributions are welcome! Feel free to submit issues or pull requests +via GitHub. + +## License -You can also embed plots, for example: +This project is licensed under the MIT License - see the LICENSE file +for details. - +## Contact -In that case, don’t forget to commit and push the resulting figure -files, so they display on GitHub and CRAN. +For more information, visit the project GitHub Page or contact +.