MACHA MARINKA PDF

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Marinka Zitnik investigates machine learning for science and medicine. Her methods leverage biomedical data at the scale of billions of interactions among millions of entities, blend machine learning with statistics and data science, and infuse biomedical knowledge into deep learning. Problems she investigates are motivated by network biology and medicine, genomics, drug discovery, and health. Zitnik's research vision is that in the future data science and artificial intelligence will be routinely used to give clinicians diagnostic recommendations; give scientists testable hypotheses they can confirm experimentally and offer them insights into safe and precise treatments; and give patients guidance on self-care, e.

To realize this vision, Dr. Zitnik develops methods to reason over rich interconnected data and translates the methods into solutions for biomedical problems. Before joining Harvard, Dr. She received her Ph. Marinka Zitnik, PhD. Zitnik Lab. Inf Fusion View full abstract on Pubmed. Cross-type biomedical named entity recognition with deep multi-task learning. Bioinformatics View full abstract on Pubmed.

Evolution of resilience in protein interactomes across the tree of life. Fast optimization of non-negative matrix tri-factorization. Front Genet View full abstract on Pubmed. Network enhancement as a general method to denoise weighted biological networks. Nat Commun View full abstract on Pubmed.

Modeling polypharmacy side effects with graph convolutional networks. Prioritizing network communities. Large-scale analysis of disease pathways in the human interactome.

Pac Symp Biocomput View full abstract on Pubmed. Brigitta Gough. Research and Administrative Assistant.

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Marinka Zitnik, PhD

The system can't perform the operation now. Try again later. Citations per year. Duplicate citations. The following articles are merged in Scholar.

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Marinka Zitnik

Marinka Zitnik investigates machine learning for science and medicine. Her methods leverage biomedical data at the scale of billions of interactions among millions of entities, blend machine learning with statistics and data science, and infuse biomedical knowledge into deep learning. Problems she investigates are motivated by network biology and medicine, genomics, drug discovery, and health. Zitnik's research vision is that in the future data science and artificial intelligence will be routinely used to give clinicians diagnostic recommendations; give scientists testable hypotheses they can confirm experimentally and offer them insights into safe and precise treatments; and give patients guidance on self-care, e.

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