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Updated: Mar 11, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

PATTERNS IN BIOMEDICAL DATA-HOW DO WE FIND THEM?

Anna O Basile¹, Anurag Verma, Marta Byrska-Bishop

¹The Pennsylvania State University, Department of Biochemistry and Molecular Biology, 328 Innovation Blvd Ste 210, State College, PA 16803, USA, azo121@psu.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|November 30, 2016

Summary

This summary is machine-generated.

Researchers face challenges with large, complex biomedical data. This session explores novel machine learning and data-driven pattern recognition for biomedical and precision medicine applications to address these issues.

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Area of Science:

Biomedical Informatics
Computational Biology
Data Science

Background:

Exponential growth in biomedical data presents significant challenges for information extraction and interpretation.
Biomedical datasets are often large-scale, high-dimensional, incomplete, and noisy, hindering effective analysis.
Developing robust methods for pattern recognition is crucial for advancing biomedical research and precision medicine.

Purpose of the Study:

To explore data-driven pattern recognition techniques for biomedical and precision medicine.
To present novel machine learning methods and applications for heterogeneous biomedical data analysis.
To address the current challenges in analyzing complex biomedical datasets.

Main Methods:

Focus on novel machine learning techniques.
Application of established machine learning methods to heterogeneous data.
Data-driven approaches for pattern discovery.

Main Results:

Selected papers showcase innovative machine learning approaches.
Demonstration of applying existing methods to diverse biomedical data types.
Manuscripts address key challenges in biomedical data analysis.

Conclusions:

Novel machine learning techniques are vital for extracting insights from complex biomedical data.
Data-driven pattern recognition facilitates advancements in precision medicine.
Addressing data challenges is essential for future biomedical discoveries.