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Updated: Nov 26, 2025

High Throughput Yeast Strain Phenotyping with Droplet-Based RNA Sequencing

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Published on: May 21, 2020

High-throughput phenotyping with temporal sequences.

Hossein Estiri^1,2,3, Zachary H Strasser^1,2,3, Shawn N Murphy^1,2,3

¹Harvard Medical School, Boston, Massachusetts, USA.

Journal of the American Medical Informatics Association : JAMIA

|December 14, 2020

Summary

This summary is machine-generated.

This study introduces a novel high-throughput phenotyping algorithm that utilizes temporal sequential patterns from electronic health records (EHRs). The new method enhances disease classification accuracy by analyzing patient data sequences, improving translational research outcomes.

Keywords:

electronic health records phenotyping sequential pattern mining temporal data representation

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Area of Science:

Computational biology
Health informatics
Translational research

Background:

Electronic health records (EHRs) contain valuable temporal data often underutilized in phenotyping.
High-throughput phenotyping algorithms accelerate translational research but can improve temporal data integration.

Purpose of the Study:

To develop a high-throughput phenotyping method leveraging temporal sequential patterns from EHRs.
To improve the accuracy and efficiency of computational phenotyping for translational research.

Main Methods:

Developed a representation mining algorithm to extract 5 classes of representations from EHR diagnosis and medication records.
Utilized aggregated vector, standard sequential patterns, transitive sequential patterns, and two hybrid classes.
Trained and validated phenotyping algorithms using EHR data for 10 phenotypes from the Mass General Brigham Biobank.

Main Results:

Phenotyping with temporal sequences demonstrated superior classification performance across all 10 phenotypes compared to standard representations.
The high-throughput algorithm achieved performance superior or similar to previously published methods.
Characterized and evaluated top transitive sequences of diagnosis records paired with related medical information.

Conclusions:

The proposed approach facilitates the discovery of complex sequential record combinations from raw EHR data.
Transitive sequences provide more accurate phenotype characterization, reflecting patients' lived experiences.
Sequential data representations offer a precise mechanism for integrating EHR data into machine learning models, prioritizing user interpretability.