AI-driven bioinformatics for cancer and ageing research
Core Expertise
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AI and machine learning for biomedical data analysis
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Cancer genomics and transcriptomics
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Genetics of ageing and ageing-associated diseases
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Multi-omics data integration and analysis
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Systems biology and network-based approaches
Research
1
AI-driven Cancer Genomics
Application of artificial intelligence and statistical bioinformatics approaches to analyse genomic and transcriptomic data from cancer samples, with the aim of identifying molecular patterns, disease mechanisms, and candidate biomarkers relevant to precision oncology.
2
Computational Biology of Ageing
Computational analysis of genetic and molecular mechanisms of ageing and age-related diseases, with a focus on how ageing-driven biological changes contribute to disease susceptibility and progression, using systems biology, network-based models, and integrative bioinformatics approaches.
3
Multi-omics for Drug Discovery and Target Identification
Integration and analysis of multi-omics datasets, including genomic and transcriptomic data, to characterise disease-associated molecular networks and pathways, supporting computational target identification, target prioritisation, and in-silico screening approaches for drug discovery in cancer and age-related diseases.
Larisa Atanasiu, Ph.D
Founder & Scientific Lead
Redwood Genetics
Fulbright Alumna
Redwood Genetics is a research-driven company interested in collaborative projects with academic institutions, hospitals, research organisations, and industry partners, particularly within EU-funded health programmes.
