An immensely complex molecular network of interactions forms the foundation of human biology and disease. Genomic approaches provide a particularly illuminating window to biological systems, and when combined with advanced analysis allow us to learn and model this complexity. The goal of SCDA Genomics research is to interpret and distill this complexity through accurate analysis and modeling of molecular pathways, particularly those in which malfunctions lead to the manifestation of disease. We are inventing integrative methods for systems-level pathway modeling through integrative analysis of genome-scale datasets. We apply these approaches in studying challenging biological problems, such as how pathways function in diverse cell types and how they change dynamically, for instance during cellular differentiation or in response to genetic and pharmacological perturbations. Read more here.
Olga Troyanskaya became deputy director for genomics in 2014 after working with SCDA as a consultant since 2013. Troyanskaya is also a professor at the Lewis-Sigler Institute for Integrative Genomics and in the department of computer science at Princeton University, where she has been on the faculty since 2003. At Princeton, she runs the Laboratory for Bioinformatics and Functional Genomics. She holds a Ph.D. in biomedical informatics from Stanford University. Troyanskaya is a recipient of the Sloan Research Fellowship, the National Science Foundation CAREER award, the Overton Prize from the International Society for Computational Biology, and the Ira Herskowitz Award from the Genetics Society of America. Contact Olga otroyanskaya(replace this with the @ sign)simonsfoundation.org.
- Predicting Effects of Noncoding Variants with Deep Learning-Based Sequence Model, Nature Methods
- Understanding Multicellular Function and Disease with Human Tissue-Specific Networks, Nature Genetics
- Targeted Exploration and Analysis of Large Cross-Platform Human Transcriptomic Compendia, Nature Methods
- Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases, Immunity
- Implications of Big Data for Cell Biology, Molecular Biology of the Cell
- Ontology-Aware Classification of Tissue and Cell-Type Signals in Gene Expression Profiles across Platforms and Technologies, Bioinformatics
- More publications here.
- Diving Deeper to Predict Noncoding Sequence Function
- Molecular Networks in Context, Nature Biotechnology
- Olga Troyanskaya Brings Order to Big Data of Human Biology
- Cancer: smoother journeys for molecular data, Nature Methods
- From Dogs, Answers About Breast Cancer, New York Times
- Cell types in profile, Nature Reviews Genetics
- GIANT: ‘Genome-scale Integrated Analysis of gene Networks in Tissues‘
- IMP v2.0: ‘Integrated Multi-species Predictions‘
- DeepSEA: Deep learning-based algorithmic framework for predicting the chromatin effects
- FNTM: A server for predicting ‘Functional Networks of Tissues in Mouse‘
- SEEK: ‘Search-based Exploration of Expression Compendium‘
- Sleipnir: ‘Library for Computational Functional Genomics‘
- More software here.
Research scientist and postdoctoral fellow positions: Develop sophisticated computational approaches to advance biomedical research and tackle complex human disease. Projects at the intersection of big data analysis, machine learning and functional genomics, with opportunities for close collaborations with experimentalists and high-impact clinical researchers.
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