PineBioML 1.2.1

PineBioML is a easy use ML toolkit.

Overview

This package aims to help analysising biomedical data using ML method in python.

System requirements

1. Python 3.10+
2. The following python module dependencies are required:

pandas openpyxl xlrd tqdm seaborn gprofiler-official jupyter jupyterlab optuna scikit-learn umap-learn pacmap statsmodels mljar-supervised joblib

Installation

1. Install Python

Please follow the tutorial to install python (the sections "Visual Studio Code" and "Git" are optional):

https://learn.microsoft.com/en-us/windows/python/beginners 

Please skip this step if you already have python 3.10+ installed in your PC.

2. Install dependencies and execute the scripts

Step 1. Download the examples from Release and unzip it.

https://github.com/ICMOL/undetermined/releases

Step 2. Please open Windows PowerShell, and execute the following command.

pip install PineBioML          

Step 3. Move to the directory of the unzipped examples, and open the jupyter interface.

Please execute the following command to open jupyter. You will see the figure, if the scripts execute correctly.

> python -m notebook

Input Table Format

The input data should be tabular and placed in the ./input folder. We accept .csv, .tsv, .xlsx and R-table in .txt formats.

Process

0. Document

API

1. Missing value preprocess

ID	Option	Definition
1	Deletion	Remove the features that are too empty.
2	Imputation with a constant value	Impute missing values with a constant value, such as 0 or the feature mean.
3	Imputation using K-NN algorithm	Impute missing values with the mean or median of the k nearest samples.

2. Data transformation

ID	Option	Definition
1	PCA	Principal component transform.
2	Power transform	To make data more Gaussian-like, you can use either Box-Cox transform or Yeo-Johnson transform.
3	Feature clustering	Group similar features into a cluster.
4	Feature expansion	Generating new features by add/product/ratio in random pair of existing features.

3. Feature selection

ID	Option	Definition
1	Volcano plot	Selecting by group p-value and fold change
2	Lasso regression	Selecting by Linear models with L1 penalty
3	Decision stump	Selecting by 1-layer decision tree
4	Random Forest	Selecting by Gini impurity or permutation importance over a Random Forest
5	AdaBoost	Selecting by Gini impurity over a AdaBoost model
6	Gradient boosting	Selecting by Gini impurity over a gradient boosting, such as XGboost or LightGBM
7	Linear SVM	Selecting by support vector from support vector machine

4. Model building

ID	Option	Definition
1	ElasticNet	Using Optuna to find a not-bad hyper parameters on given dataset.
2	SVM	Using Optuna to find a not-bad hyper parameters on given dataset.
3	Decision Tree	Using Optuna to find a not-bad hyper parameters on given dataset.
4	Random Forest	Using Optuna to find a not-bad hyper parameters on given dataset.
5	AdaBoost	Using Optuna to find a not-bad hyper parameters on given dataset.
6	XGBoost	Using Optuna to find a not-bad hyper parameters on given dataset.
7	LightGBM	Using Optuna to find a not-bad hyper parameters on given dataset.
8	CatBoost	Using Optuna to find a not-bad hyper parameters on given dataset.

5. Report and visualization

ID	Option	Definition
1	data_overview	Giving a glance to input data.
2	classification_summary	Summarizing a classification task

Examples for Program Demonstration

Chosse one of the following examples, double click it in jupyter interface:

ID	Name	Description
1	example_BasicUsage.ipynb	Demonstrate the basic features of PineBioML
2	example_Proteomics.ipynb	An example on proteomics data analysis
3	example_PipeLine.ipynb	Demonstrate how to use the pipeline to store the whole data processing flow
4	example_Pine.ipynb	Demonstrate how to use Pine ml to finding the best data processing flow in an efficient way
5	example_UsingExistingModel.ipynb	An example of unsing existing models/pipeline gained from 3. , 4. or 5.

Click the buttom and the script should start.

Cites

The example data is from LinkedOmicsKB

A proteogenomics data-driven knowledge base of human cancer, Yuxing Liao, Sara R. Savage, Yongchao Dou, Zhiao Shi, Xinpei Yi, Wen Jiang, Jonathan T. Lei, Bing Zhang, Cell Systems, 2023.