Short Communication
Open Access
Translating High-Dimensional Gene Expression Patterns into Musical Representations to Decipher Complex Biological Dynamics
Gil Alterovitz1,2,3* and Sophia Yuditskaya4
1Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, USA
2Division of Health Sciences and Technology, Harvard Medical School and Massachusetts Institute of Technology, Boston, MA, USA
3Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
4Cyber Analytics and Decision Systems Group at MIT Lincoln Laboratory, Cambridge, MA, USA
2Division of Health Sciences and Technology, Harvard Medical School and Massachusetts Institute of Technology, Boston, MA, USA
3Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
4Cyber Analytics and Decision Systems Group at MIT Lincoln Laboratory, Cambridge, MA, USA
Elmehrath G, Tirado O. Human Amniotic Membrane Scaffold Use in Wound Care: Macrophage Activation and Healing Outcomes, Accounts of Biotechnology Research. 2023, Vol. 11 No. 2: 106
Abstract
Recent advances in real-time monitoring of gene expression and protein abundance
enable unprecedented opportunities for personalized pharmacodynamics,
gene therapy, and patient assessment. However, the sheer complexity of
multidimensional genomic and proteomic datasets poses significant challenges
for immediate interpretation. Here, we present a novel approach that transforms
high-dimensional gene and protein dynamics into auditory representations, or
sonification, facilitating intuitive real-time monitoring. Using principal component
analysis (PCA), we reduced thousands of gene dimensions to a manageable number
of principal components, which were mapped to musical notes and instruments.
Application to colon cancer datasets revealed that normal samples produced
harmonious sequences, whereas cancer samples exhibited discordant musical
patterns reflecting underlying network perturbations. This work demonstrates
that sonification provides an effective modality for detecting temporal changes
in gene and protein networks, offering a promising tool for clinical and research
applications.
enable unprecedented opportunities for personalized pharmacodynamics,
gene therapy, and patient assessment. However, the sheer complexity of
multidimensional genomic and proteomic datasets poses significant challenges
for immediate interpretation. Here, we present a novel approach that transforms
high-dimensional gene and protein dynamics into auditory representations, or
sonification, facilitating intuitive real-time monitoring. Using principal component
analysis (PCA), we reduced thousands of gene dimensions to a manageable number
of principal components, which were mapped to musical notes and instruments.
Application to colon cancer datasets revealed that normal samples produced
harmonious sequences, whereas cancer samples exhibited discordant musical
patterns reflecting underlying network perturbations. This work demonstrates
that sonification provides an effective modality for detecting temporal changes
in gene and protein networks, offering a promising tool for clinical and research
applications.
Keywords
Gene expression; Protein abundance; Sonification; Principal component analysis; Real-time monitoring; Colon cancer.
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