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Related Experiment Video

Updated: Dec 19, 2025

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

Phenotype Inference with Semi-Supervised Mixed Membership Models.

Victor A Rodriguez¹, Adler Perotte¹

¹Columbia University, Department of Biomedical Informatics, New York City, NY, USA.

Proceedings of Machine Learning Research

|June 4, 2020

Summary

This summary is machine-generated.

This study introduces a new Semi-Supervised Mixed Membership Model (SS3M) for disease phenotyping using partially labeled clinical data. SS3M generates interpretable phenotypes and shows competitive predictive performance.

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Area of Science:

Computational biology
Medical informatics
Machine learning

Background:

Disease phenotyping algorithms identify patients using clinical data.
Supervised methods need extensive labeled data.
Unsupervised methods may identify irrelevant phenotypes.

Purpose of the Study:

Propose the Semi-Supervised Mixed Membership Model (SS3M) for disease phenotyping.
Develop a method to learn phenotypes from partially labeled clinical data.
Address limitations of existing supervised and unsupervised phenotyping approaches.

Main Methods:

Developed a probabilistic graphical model, SS3M.
Utilized partially labeled clinical data for phenotype learning.
Evaluated SS3M against established phenotyping baselines.

Main Results:

SS3M generates interpretable and disease-specific phenotypes.
Phenotypes accurately capture clinical features of labeled disease concepts.
SS3M demonstrates competitive predictive performance.

Conclusions:

SS3M offers an effective approach for disease phenotyping with limited labeled data.
The model provides interpretable and clinically relevant phenotypes.
SS3M shows promise for improving patient identification in clinical data.