Application of Deep Neural Networks to Biomarker Development
With the almost exponential growing of transcriptomics data now it is possible and even necessary to apply sophisticated machine learning techniques to the field. Applications of deep neural networks combined with domain expertise can help optimize biomarker development process through intelligent analysis of high-throughput screening experiments and large repositories of biomedical data. This presentation will cover aspects of creating multi-modal biomarkers human age trained on human blood biochemistry and transcriptomics data.
Polina Mamoshina is a senior research scientist at Insilico Medicine, Inc, a Baltimore-based bioinformatics and deep learning company focused on reinventing drug discovery and biomarker development and a part of the computational biology team of Oxford University Computer Science Department. Polina graduated from the Department of Genetics of the Moscow State University. She was one of the winners of GeneHack a Russian nationwide 48-hour hackathon on bioinformatics at the Moscow Institute of Physics and Technology attended by hundreds of young bioinformaticians. Polina is involved in multiple deep learning projects at the Pharmaceutical Artificial Intelligence division of Insilico Medicine working on the drug discovery engine and developing biochemistry, transcriptome, and cell-free nucleic acid-based biomarkers of aging and disease. She recently co-authored seven academic papers in peer-reviewed journals.