Angel Angelov

COMPARATIVE METAGENOMIC ASSESSMENT OF SHORT- AND LONG-READ SEQUENCING TECHNOLOGIES REVEALS UNKNOWN MICROBIAL INFORMATION IN A COMPLEX ENVIRONMENTAL SAMPLE

The paper compares short- and long-read sequencing technologies in metagenomics, showing that longer short reads with optimal prep improve assembly and protein detection without needing more sequencing depth.

A personal, reference quality, fully annotated genome from a Saudi individual

The paper presents a fully annotated reference genome from a Saudi individual using de novo assembly and AI-based annotation, making all data publicly available.

Swarm Learning as a privacy-preserving machine learning approach for disease classification

This paper introduces Swarm Learning, a decentralized machine learning approach for disease classification using transcriptomic data, demonstrating its effectiveness in developing classifiers for COVID-19, tuberculosis, and leukemias while preserving privacy.