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Related Experiment Video

Updated: Dec 9, 2025

Global Identification of Co-Translational Interaction Networks by Selective Ribosome Profiling

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Published on: October 7, 2021

Cohort Identification for Translational Bioinformatics Studies.

Tiffany A Lin^1,2, Zeynep Eroglu^2,3, Rodrigo Carvajal⁴

¹Collaborative Data Services Core, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.

Methods in Molecular Biology (Clifton, N.J.)

|September 14, 2020

Summary

This summary is machine-generated.

Identifying patient cohorts for translational research is crucial for therapeutic development. This study details a standard process using health information systems and database querying for efficient cohort identification in oncology.

Keywords:

Bioinformatics Cohort identification Informatics Resource paper Translational science

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Oncology
Translational Research
Bioinformatics

Background:

Translational studies require precise patient cohort identification for therapeutic development.
Accessing health information systems presents challenges for selecting biological specimens.
Standardized methods are needed for efficient cohort identification in cancer research.

Purpose of the Study:

To describe the standard process for cohort identification used by Moffitt Cancer Center's Cutaneous Oncology Program and Collaborative Data Services Core (CDSC).
To outline the regulatory and procedural aspects of using health information management systems for patient cohort filtering.

Main Methods:

Utilized graphical user interfaces (GUIs) for data visualization and interaction.
Employed database querying to efficiently filter patient data.
Integrated health information management systems for cohort identification.

Main Results:

A standard operating procedure for cohort identification was established.
The described methods facilitate the selection of appropriate biological specimens for translational studies.
Regulatory and procedural guidelines for utilizing health information systems were outlined.

Conclusions:

The described methodology provides a robust framework for cohort identification in translational oncology.
Effective utilization of health information systems is key to advancing therapeutic development through precise patient selection.