Multidimensional Boolean Patterns in Multi-omics Data
Golovko, G.; Reyes, V.; Pinchuk, I.; Fofanov, Y.
Show abstract
MotivationVirtually all biological systems are governed by a set of complex relations between their components. Identification of relations within biological systems involves a rigorous search for patterns among variables/parameters. Two-dimensional (involving two variables) patterns are identified using correlation, covariation, and mutual information approaches. However, these approaches are not suited to identify more complicated multidimensional relations, which simultaneously include 3, 4, and more variables. ResultsWe present a novel pattern-specific method to quantify the strength and estimate the statistical significance of multidimensional Boolean patterns in multiomics data. In contrast with dimensionality reduction and AI solutions, patterns identified by the proposed approach may provide a better background for meaningful mechanistic interpretation of the biological processes. Our preliminary analysis suggests that multidimensional patterns may dominate the landscape of multi-omics data, which is not surprising because complex interactions between components of biological systems are unlikely to be reduced to simple pairwise interactions.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data. 94%
- Investigating metabolic interactions in a microbial co-culture through integrated modelling and experiments 93%
- Topological embedding and directional feature importance in ensemble classifiers for multi-class classification 93%
Similar papers in this journal
- Novel AI-powered computational method using tensor decomposition for identification of common optimal bin sizes when integrating multiple Hi-C datasets 94%
- Detecting SARS-CoV-2 lineages and mutational load in municipal wastewater; a use-case in the metropolitan area of Thessaloniki, Greece 93%
- Visibility Graph Based Community Detection for Biological Time Series 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.