News
Research that develops over years sometimes finds its place in one of the most prestigious journals in the world. For us, the publication in Nature Genetics is a special moment.
The paper, titled "Genetic variation at transcription factor binding sites largely explains phenotypic heritability in maize," describes a novel approach to identifying functional gene regulatory variations at the population level. The method reveals how cis-regulatory variations contribute to trait variation and provides new insights into the genetic mechanisms that shape phenotype.
Why is this relevant for an AI consulting company? Because this research exemplifies how data-driven approaches and machine learning can unlock insights in the life sciences that classical methods cannot reach. For us at Perelyn, this kind of fundamental research is directly connected to our daily work: structuring and analysing complex data so that knowledge emerges from it.
Michael Banf, our Chief AI Scientist, contributed to this work. His academic background in the computational life sciences feeds directly into the methods we apply for our clients at Perelyn.
The full publication is accessible via Nature Genetics.
Event
Liliya Imasheva presented a validation pipeline for evaluating AI summaries at Conf42 Large Language Models 2026.
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At the IOCMA 2026 conference, Perelyn presented our method, which transforms graph data such as supply chains or networks so that AI models can learn more reliably from it, even over long distances.
