# TriSig: Assessing the statistical significance of triclusters

arXiv: https://arxiv.org/abs/2306.00643 · HTML: https://mlsanity.com/paper/2306.00643/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 52 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 2 of 2)

- [Towards Social Artificial Intelligence: Nonverbal Social Signal Prediction in A Triadic Interaction](https://mlsanity.com/paper/1906.04158/) (2019)
- [Discovering Patterns in Time-Varying Graphs: A Triclustering Approach](https://mlsanity.com/paper/1608.07929/) (2016)

## Built on by (most cited first, 1 of 1)

- [Integrating Statistical Significance and Discriminative Power in Pattern Discovery](https://mlsanity.com/paper/2401.12000/) (2024)

## Abstract (arXiv)

Tensor data analysis allows researchers to uncover novel patterns and relationships that cannot be obtained from matrix data alone. The information inferred from the patterns provides valuable insights into disease progression, bioproduction processes, weather fluctuations, and group dynamics. However, spurious and redundant patterns hamper this process. This work aims at proposing a statistical frame to assess the probability of patterns in tensor data to deviate from null expectations, extending well-established principles for assessing the statistical significance of patterns in matrix data. A comprehensive discussion on binomial testing for false positive discoveries is entailed at the light of: variable dependencies, temporal dependencies and misalignments, and textitp-value corrections under the Benjamini-Hochberg procedure. Results gathered from the application of state-of-the-art triclustering algorithms over distinct real-world case studies in biochemical and biotechnological domains confer validity to the proposed statistical frame while revealing vulnerabilities of some triclustering searches. The proposed assessment can be incorporated into existing triclustering algorithms to mitigate false positive/spurious discoveries and further prune the search space, reducing their computational complexity. Availability: The code is freely available at https://github.com/JupitersMight/TriSig under the MIT license.