Multivariate Mutual Information based Feature Selection for Predicting Histone Post-Translational Modifications in Epigenetic Datasets
Dhanasekhar, V. K.; Pillai, S. R. B.; Ramakrishnan, N.
10.1101/2025.05.28.656539 bioRxivShow abstract
Mutual information (MI) has been traditionally employed in many areas including biology to identify the non-linear relationships between features. This technique is particularly useful in the biological context to identify features such as genes, histone post-translational modifications (PTMs), transcriptional factors etc. In this work, instead of considering the conventional pairwise MI between PTM features, we evaluate multivariate mutual information (MMI) between PTM triplets, to identify a set of outlier features. This enables us to form a small subset of PTMs that serve as principal features for the prediction of the values of any histone PTM across the epigenome. We also compare the principal MMI features with those from the traditional feature selection techniques such as PCA and Orthogonal Matching Pursuit. We predict all the remaining histone PTM intensities using XGBoost based regression on the selected features. The accuracy of this technique is demonstrated on the ChIP-seq datasets from the yeast and the human epigenomes.The results indicate that the proposed MMI based feature selection technique can serve as a useful method across various biological datasets.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Controlled Noise: Evidence of Epigenetic Regulation of Single-Cell Expression Variability 94%
- Differential co-expression network analysis with DCoNA reveals isomiR targeting aberrations in prostate cancer 94%
- Exploring High-Dimensional Biological Data with Sparse Contrastive Principal Component Analysis 93%
Similar papers in this journal
Similar papers in this journal
- A Quantitative Modelling Approach for DNA Repair on a Population Scale 94%
- Application of Modular Response Analysis to Medium- to Large-Size Biological Systems 94%
- Mcadet: a feature selection method for fine-resolution single-cell RNA-seq data based on multiple correspondence analysis and community detection 94%
Similar papers in this journal
- Extracting physical characteristics of higher-order chromatin structures from 3D image data 93%
- Dissecting the binding mechanisms of transcription factors to DNA using a statistical thermodynamics framework. 93%
- Multi-scale phase separation by explosive percolation with single chromatin loop resolution 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.