contextualized-ml 0.2.8

A statistical machine learning toolbox for estimating models, distributions, and functions with sample-specific parameters.

An SKLearn-style toolbox for estimating and analyzing models, distributions, and functions with context-specific parameters.

Context-specific parameters:

• Find hidden heterogeneity in data -- are all samples the same?
• Identify context-specific predictors -- are there different reasons for outcomes?
• Enable domain adaptation -- can learned models extrapolate to new contexts?

Quick Start

Installation

pip install contextualized-ml

Take a look at the easy demo for a quickstart with sklearn-style wrappers.

Build a Contextualized Model

from contextualized.easy import ContextualizedRegressor
model = ContextualizedRegressor()
model.fit(C, X, Y)

Predict Context-Specific Parameters

model.predict_params(C)

See the docs for more examples.

Important links

• Documentation
• Pypi package index

Contextualized Family

Context-dependent modeling is a universal problem, and every domain presents unique challenges and opportunities. Here are some layers that others have added on top of Contextualized. Feel free to add your own page(s) by sending a PR or request an improvement by creating an issue. See CONTRIBUTING.md for more information about the process of contributing to this project.

bio-contextualized.ml

Contextualized and analytical tools for modeling medical and biological heterogeneity

Thanks to all our contributors

Contextualized ML was originally implemented by Caleb Ellington (CMU) and Ben Lengerich (MIT).

Many people have helped. Check out ACKNOWLEDGEMENTS.md!

Related Publications and Pre-prints

• Contextualized Machine Learning
• Contextualized Networks Reveal Heterogeneous Transcriptomic Regulation in Tumors at Sample-Specific Resolution
• Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning
• Automated Interpretable Discovery of Heterogeneous Treatment Effectiveness: A COVID-19 Case Study
• NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters
• Discriminative Subtyping of Lung Cancers from Histopathology Images via Contextual Deep Learning
• Personalized Survival Prediction with Contextual Explanation Networks
• Contextual Explanation Networks

Videos

• Cold Spring Harbor Laboratory: Contextualized Graphical Models Reveal Sample-Specific Transcriptional Networks for 7000 Tumors
• Sample-Specific Models for Interpretable Analysis with Applications to Disease Subtyping

Contact Us

Please get in touch with any questions, feature requests, or applications by using the GitHub discussions page.