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Related Experiment Video

Updated: May 5, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

Robust prediction-based analysis for genome-wide association and expression studies.

Skanda K Koppula¹, Amin Zollanvari, Ning An

¹Center for Biomedical Informatics, Harvard Medical School [Boston, MA 02115]. ; Department of Electrical Engineering and Computer Science at Massachussetts Institute of Technology [Cambridge, MA 02139].

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science

|December 5, 2013

Summary

This study introduces a new framework for analyzing omic data to predict complex diseases like alcoholism, lung cancer, and schizophrenia. It uses cellular pathways to build accurate diagnostic and biomarker models.

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Last Updated: May 5, 2026

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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

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Published on: November 3, 2010

Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Complex diseases involve intricate molecular mechanisms.
Analyzing multi-omic data is crucial for understanding disease.
Current methods often lack biological pathway integration.

Purpose of the Study:

To present a novel prediction-based framework for omic data analysis.
To generate predictive models for disease diagnosis and pathway identification.
To leverage underlying biology (cellular pathways/gene-sets) for improved models.

Main Methods:

Developed a prediction-based framework for omic data analysis.
Integrated cellular pathway information into feature selection.
Applied the framework to multi-omic datasets for alcoholism, lung cancer, and schizophrenia.

Main Results:

The framework robustly analyzed diverse omic data types and sources.
Identified significant feature sets linked to complex diseases.
Generated accurate predictive models for disease diagnosis.

Conclusions:

The framework effectively analyzes omic data for disease prediction.
Incorporating biological pathways enhances model accuracy and interpretability.
Demonstrated utility in modeling complex diseases like alcoholism, lung cancer, and schizophrenia.