2026-08-13 –, Room 4
Phylogenetic reconstruction and comparative analysis are fundamental to understanding evolutionary relationships and biological diversity. Traditional algorithms rely heavily on multiple sequence alignments and statistical modelling, which face significant computational challenges with large-scale datasets. Furthermore, integrating information from multiple data sources, such as sequences, structures, and functional annotations, at the time of reconstruction, remains technically challenging, limiting the feasibility of phylogenetic reconstruction using today’s diversity of biological annotations.
Hyperdimensional computing (HDC) is a novel computational paradigm that employs high-dimensional representations of atomic entities (e.g., amino acids) and combines them via algebraic operations to represent more complex data structures (e.g., proteins). This paradigm, parallel to connectionist modelling, is characterised by modelling the brain's distributed memory and the operations underlying its processing. HDC exhibits several properties advantageous for biological data analysis: robustness to noise, holographic information distribution, and the ability to integrate heterogeneous data sources seamlessly. Recent applications in DNA sequencing, pattern matching, and molecular classification have demonstrated HDC's potential in bioinformatics, where its computational efficiency, interpretability, and natural capacity for multimodal data fusion make it particularly well suited to complex phylogenetic analyses.
In this talk, we showcase the potential of HDC for phylogenetic reconstruction and comparative analysis. Here, we present PhyloHD.jl, a Julia package for representing biological data as hypervectors and reconstructing phylogenetic trees from these representations. We will showcase how to calculate branch support using the HDC paradigm and present a multimodal tree reconstruction approach that integrates multiple heterogeneous data sources, including sequences, structures, and functional annotations. Finally, we will showcase how HDC learning techniques can be used for family-based phylogenetic tree reconstruction and ancestral sequence reconstruction. This work represents the first attempt to use hyperdimensional computing as a computational paradigm for phylogenetics and opens new avenues for research in this field.
The talk will be present:
What is hyperdimensional computing?: A brief primer on how to build your own brain and computing with concepts (hypervectors) + HyperdimensionalComputing.jl package introduction.
Why is it useful for phylogenetic reconstruction and comparative analysis?: A discussion of the properties of HDC that make it particularly well-suited for biological data analysis, and a review of recent applications in bioinformatics.
How do we represent biological data as hypervectors?: A presentation on a novel framework for representing biological data as hypervectors, including sequences, three-dimensional structures, and functional annotations, and how this can be combined to represent multimodal biological entities.
Alignment-free phylogenetic reconstruction using HDC: From biological data to hypervectors to phylogenetic tree, showcase of cvigilv/PhyloHD.jl
Branch support calculation for HDC-based phylogenetics: Showcase on hypervector perturbation, bootstrapping, jack-knifing, and other techniques for calculating branch support based on the HDC paradigm.
Multimodal tree reconstruction using HDC: A showcase of how to integrate multiple data sources at the time of reconstruction using HDC, showcasing 3 distinct Tree-of-Life based on data fusion to represent more complex biological entities.
HDC learning techniques for family-based phylogenetic tree reconstruction and ancestral sequence reconstruction: Preliminary results on using HDC learning techniques for family-based phylogenetic tree reconstruction.
I'm a Chilean biochemist-turned-computational biologist currently pursuing my PhD at the Université de Lille, focusing on reconstructing an evolutionary Tree-of-Life using unconventional computational paradigms for biological system representation.