Biology subjects

Aygun, S.

Publications and source records attributed to Aygun, S..

4 recordsLinked to original sources

bioRxiv2026

HyperSketch: de Bruijn graph sketching for genomic similarity estimation with Hyperdimensional Computing

The exponential growth of genomic databases necessitates alignment-free methods for comparing genomes. While MinHash-based tools have revolutionized this field by efficiently estimating the Average Nucleotide Identity based on k-mer sets, they inherently discard structural genomic information. We introduce HyperSketch, a novel sketching tool that encodes the de Bruijn graph structure of a genome into a fixed-size, topology-aware vector using Hyperdimensional Computing (HDC). Unlike set-based sketches, HyperSketch encodes the transitions between adjacent k-mers into a superposition of orthogonal hypervectors. To formalize parameter selection, we also propose an analytical framework proving that graph-based sketches fundamentally require a smaller k-mer size than set-based models due to their expanded k+1 biological footprint. We benchmarked HyperSketch against Mash and HyperGen using a dataset of ~26 thousand viral reference genomes from NCBI GenBank. Under optimal parameters, we demonstrate a strong linear correlation (>99%) between the graph-based similarity computed by HyperSketch and standard MinHash distance estimates. Crucially, we show that the mathematical formulation of HyperSketch introduces a distance scaling effect that expands the dynamic range of estimates for closely related strains, providing a higher-resolution metric for sub-lineage clustering than purely compositional estimators. HyperSketch provides a computationally efficient, structure-aware alternative to traditional sketching. By natively encoding genomic syntax, it offers a new dimension of genomic comparison that excels at both high-resolution strain differentiation and deep evolutionary scaling, complementing existing nucleotide identity metrics without requiring sequence alignment.

bioinformatics↗

bioRxiv2025

Predicting the toxicity of chemical compounds via Hyperdimensional Computing

Accurately and efficiently assessing the potential toxicity of chemical compounds is critical given their wide application across pharmaceutical, industrial, and environmental domains. Traditional toxicological evaluations, which predominantly rely on intensive in vitro and in vivo assays, are frequently slow and expensive processes. Here, we introduce a novel application of Hyperdimensional Computing (HDC), an emerging computational paradigm inspired by the way the human brain works in encoding information, for the efficient classification of chemical compounds as either toxic or non-toxic. Our methodology employs Simplified Molecular Input Line Entry System (SMILES) representations of compounds, drawing data from the comprehensive Tox21 dataset. We delineate a pipeline wherein these chemical structures are encoded into high-dimensional binary vectors, which subsequently serve as the foundation for training and classification within the HDC framework. This approach leverages HDCs inherent advantages, including its resilience to noise, parallel processing capabilities, and efficacy in identifying intricate patterns. This work demonstrates the viability of HDC as a promising alternative for large-scale toxicity prediction, offering a computationally efficient and scalable solution. This research significantly contributes to the field of cheminformatics by validating HDCs potential in chemical property prediction, thereby facilitating accelerated identification of hazardous substances and mitigating the reliance on intensive laboratory experimentations.

bioinformatics↗

bioRxiv2025

A novel Vector-Symbolic Architecture for graph encoding and its application to viral pangenome-based species classification

Viral species classification is crucial for understanding viral evolution, epidemiology, and developing effective diagnostics and treatments. Traditional methods often rely on sequence similarity, which can be challenging for rapidly evolving viruses. Pangenomes, offering a comprehensive representation of species genomic diversity, provide a richer perspective, but their analysis often requires advanced computational methods. We investigate the use of Hyperdimensional Computing (HDC), also known as Vector-Symbolic Architecture (VSA), an emerging computing paradigm that relies on vectors in high-dimensional spaces to encode a multi-species viral pangenome. We develop a new method for encoding graph-structured viral pangenomes using high-dimensional vectors. Pangenomes are represented as weighted de Bruijn graphs constructed using sequences of consecutive k-mers from the genomes, while information about the genome species (their class) is encoded as specific weights on the edges of the graph. The weighted de Bruijn graph representation is encoded into a single high-dimensional vector. We tested three classification strategies: a flat model at the species level, a flat model at the genus level, and a two-step hierarchical model. We applied our method to a pangenome comprising 542 viral species from NCBI GenBank. Our results reveal a complex relationship between model architecture and classification accuracy. The flat species-level model achieved the highest accuracy, correctly classifying 87.08% of test genomes. Counter-intuitively, simplifying the problem to the genus level or using a hierarchical approach degraded performance, with accuracies dropping to 60.51% and 33.57% respectively. These outcomes highlight critical challenges in alignment-free classification, such as signal dilution in overly broad taxonomic groups and error propagation in multi-step models. The models reconstruction rate proved to be a reliable measure of confidence, rather than a direct predictor of correctness. This novel approach offers a promising new direction for viral classification, not only for its predictive power but its ability to reveal underlying challenges in genomic taxonomy.

bioinformatics↗

bioRxiv2024

Fluence rate-dependent kinetics of light-triggered liposomal doxorubicin assessed by quantitative fluorescence-based endoscopic probe

Liposomal doxorubicin (Dox), a treatment option for recurrent ovarian cancer, often suffers from suboptimal biodistribution and efficacy, which might be addressed with precision drug delivery systems. Here, we introduce a catheter-based endoscopic probe designed for multispectral, quantitative monitoring of light-triggered drug release. This tool utilizes red-light photosensitive porphyrin-phospholipid (PoP), which is encapsulated in liposome bilayers to enhance targeted drug delivery. By integrating diffuse reflectance and fluorescence spectroscopy, our approach not only corrects the effects of tissue optical properties but also ensures accurate drug delivery to deep-seated tumors. Pre-liminary results validate the probe effectiveness in controlled settings, highlighting its potential for future clinical adaptation. This study sets the stage for in vivo applications, enabling the exploration of next-generation treatment paradigms for the management of cancer by optimizing chemotherapy administration with precision and control.

bioengineering↗