Aleksandr Sahakyan
Senior Researcher in Machine Learning Prediction of Epigenetics
computational biology, machine learning, data integration, molecular medicine, genome, transcriptome and proteome
Alex is a senior researcher focused on integrative computational biology and machine learning in the MRC Weatherall Institute of Molecular Medicine and Radcliffe Department of Medicine. In past, he did his undergraduate studies in pharmaceutical sciences while actively researching in the areas of quantum/structural chemistry and NMR spectroscopy. He next moved to the University of Cambridge, first obtaining an MPhil degree in computational biology with distinction (Department of Applied Mathematics and Theoretical Physics, supported by Cambridge Trust), followed by a PhD in theoretical chemical biology (Department of Chemistry) as a Herchel Smith Scholar. He then became an interdisciplinary research fellow in computational genomics and epigenetics (Department of Chemistry and Cancer Research UK Cambridge Institute). Alex joined the University of Oxford in 2017, when he started to lead a group in Integrative Computational Biology and Machine Learning as a principal investigator. He supervised multiple DPhil and post-doctoral trainees and summer interns, many getting notable awards. His research aims at combining machine learning, computational biology, computational chemistry with experimental data from genomics and biophysical techniques, to reach a new level of precision in biology, at both genome and proteome levels.
Recent publications
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Analysis of long-range contacts across cell types outlines a core sequence determinant of 3D genome organisation
Tamon L. et al, (2025)
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Prior knowledge on context-driven DNA fragmentation probabilities can improvede novogenome assembly algorithms
Pflughaupt P. and Sahakyan A., (2025)
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Towards the genomic sequence code of DNA fragility for machine learning.
Pflughaupt P. et al, (2024), Nucleic Acids Res
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Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learning.
Masuda K. et al, (2024), Sci Data, 11
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Generalised interrelations among mutation rates drive the genomic compliance of Chargaff's second parity rule.
Pflughaupt P. and Sahakyan AB., (2023), Nucleic Acids Res