Speaker
Description
Ladderpath is a compression-based framework, grounded in Algorithmic Information Theory, for extracting repeated, nested, and hierarchically organized structure from symbolic sequences. Rather than treating biological sequences as flat strings, it reconstructs reusable building blocks and their dependency relations, yielding interpretable representations together with quantitative measures of complexity, hierarchical reuse, and sequence distance.
This poster presents Ladderpath as a unified mathematical and computational framework for biological sequence analysis. I introduce its core formalism, including ladderons, laddergraphs, and indices of repetition and compressibility, and show how the same representation supports multiple problems in mathematical biology. For example, Ladderpath-derived distances provide an alternative to purely alignment-based similarity measures for phylogenetic and comparative sequence analysis, enabling the study of duplication, modular reuse, and evolutionary innovation; Ladderpath-based tokenization offers a compression-guided alternative to amino-acid-level and BPE-style segmentation in protein and DNA language models, with the potential to capture longer-range regularities and biologically meaningful reusable units.
Overall, the poster argues that compression-based hierarchical decomposition provides an interpretable analytical tool and a mathematical interface connecting sequence complexity, evolution, and learning.
