Approximating mutual information of high-dimensional variables using learned representations
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View abstract on PubMed
Summary
This summary is machine-generated.We introduce latent mutual information (LMI) approximation, a new method to estimate statistical dependence in high-dimensional biological data. LMI successfully approximates mutual information for variables exceeding 1000 dimensions, overcoming limitations of existing techniques.
Area Of Science
- Computational Biology
- Information Theory
- Machine Learning
Background
- Mutual information (MI) quantifies statistical dependence but is difficult to estimate in high dimensions due to sample size requirements.
- Existing MI estimation methods fail for datasets with more than tens of dimensions, limiting their application in complex biological systems.
Purpose Of The Study
- To develop a novel method for approximating MI in high-dimensional data by leveraging underlying low-dimensional structure.
- To demonstrate the effectiveness of the proposed latent MI (LMI) approximation method on benchmark datasets and real-world biological problems.
Main Methods
- Developed latent MI (LMI) approximation, a method applying nonparametric MI estimation to low-dimensional data representations.
- Utilized a theoretically-motivated model architecture to learn low-dimensional representations from high-dimensional data.
- Validated LMI on benchmark datasets, comparing its performance against existing MI estimation techniques.
Main Results
- LMI accurately approximates MI for variables with over 1000 dimensions, provided the data exhibits low intrinsic dimensionality.
- Demonstrated LMI's capability to analyze complex biological data, including protein language model representations and single-cell RNA sequencing data.
- Identified non-trivial information encoded by protein language models about protein-protein interactions.
Conclusions
- Latent MI (LMI) approximation offers a scalable solution for estimating mutual information in high-dimensional biological datasets.
- The method successfully quantifies information in protein representations and reveals dynamic changes in cell fate information during hematopoietic stem cell differentiation.
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