PineBioML 1.2.6.1

PineBioML is a easy use ML toolkit.

Overview

In today’s data-driven world, making informed decisions requires more than just raw data—it demands intelligent insights. PineBioML is designed to provide a comprehensive workflow that guides users through every step of data analysis, from preprocessing to visualization. Whether you are a data scientist, researcher, or biomedical data analyst, this software tool empowers you with state-of-the-art machine learning algorithms, advanced feature selection techniques, and dynamic data visualization tools to extract valuable insights effortlessly.

System requirements

Python 3.9, 3.10, 3.11

Installation

PineBioML is available on PyPI. You can access it through:

pip install PineBioML

For those who do not know how to use python, you can follow our step by step Installation tutorials.

• Windows10/11
• MacOs

Examples

After installation, you can download examples from release.

https://github.com/ICMOL/PineBioML/releases/download/example/examples126.zip

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

ID	Name	Description
1	example_BasicUsage multi class.ipynb	Demonstrate the basic features of PineBioML on a multi-class classification task.
2	example_BasicUsage regression.ipynb	Demonstrate the basic features of PineBioML on a regression classification task.
3	example_Proteomics.ipynb	An example on proteomics data analysis
4	example_PipeLine.ipynb	Demonstrate how to use the pipeline to store the whole data processing flow
5	example_Pine.ipynb	Demonstrate how to use Pine ml to finding the best data processing flow in an efficient way

Execute the scripts

Click the buttom and the script should start.

Features

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

Contact us

[e-mail]

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.