Dr. Goldenberg develops machine learning methods that combine diverse sets of biological and phenotypic measurements to refine understanding of complex human diseases, identifying the best treatments, and the individual patient outcomes, guiding decisions to improve the quality of life for patients. Dr. Goldenberg’s new data integration method called Similarity Network Fusion (SNF) was the first to integrate patient data (omics, imaging, etc) using patient networks. It improved survival outcome predictions in 5 different cancers. Dr Goldenberg brings her expertise in data integration to Child and Brain Development group.
More than two thirds of mental health issues have their onset during childhood or adolescence. Identifying children at risk for mental illness later in life and predicting the type of illness is not easy. There are no blood or genetic ‘tests’; instead, psychiatrists and health professionals use an individual’s and his/her family’s clinical history to note persistent symptoms and make a diagnosis. Dr Goldenberg believes that, by modeling and combining trajectories of social-emotional and neurocognitive measures over time and genome-wide genetic and DNA methylation data, it is possible to identify clinically relevant subtypes of childhood and adolescent development, and the corresponding biomarkers that predict traits underlying psychiatric disorders. The easy-to-use computational tools developed to ascertain longitudinal subtypes and their associated biomarkers will be made publicly available to the larger research community. Our findings will implicate both short and long term predictors and biological contributors of psychopathology helping to unravel the complex relationship between genomics and social-emotional development. These findings will also hopefully generate candidate biological targets for novel treatments.
2016, Department of Computer Science Award for exceptional mentoring and outstanding commitment to graduate student recruitment, University of Toronto.
2016, Early Researcher Award from the Ministry of Research and Innovation.
2013, Winner of the best poster presentation award at the Young Investigator Meeting, organized by the CIHR Institute of Cancer Research.
2010, MITACS Award - for a workshop entitled Networks Across Disciplines: Theory and Applications Prize/Award.
C. Corre, G. Shinoda, H. Zhu, D.L. Cousminer, C. Crossman, C. Bellissimo, A. Goldenberg, G.Q. Daley, M.R. Palmert. (2016) Sex specific regulation of weight and puberty by Lin28/let7 axis. Journal of Endocrinology, 228(3), pg 179-91.
L. Rampasek, A. Goldenberg. (2016). TensorFlow: Biology’s Gateway to Deep Learning? Cell systems, 2(1), 1214.
Saria, S., A. Goldenberg (2015). Subtyping: What it is and its role in precision medicine. IEEE Intelligent Systems Magazine, 30(4), p. 7075.
R. Colak, T. Kim, H. Kazan, Y. Oh, M Cruz, A. Valladares, J. Peralta, J. Escobedo, E. Parra, P.M. Kim and A. Goldenberg (2015) JBASE: Joint Bayesian Analysis of Subphenotypes and Epistasis. Bioinformatics, 32(2), 201-10.
B. Wang, A. Mezlini, F. Demir, M. Fiume, T. Zu, M. Brudno, B. Haibe-Kains and A. Goldenberg (2014) Similarity network fusion for aggregating data types on genomic scale. Nature Methods, 11(3), p. 3377.
A. Mezlini, B. Wang, A. Deshwar, Q. Morris and A. Goldenberg (2013) Identifying cancer specific functionally relevant miRNAs from gene expression and miRNA to gene networks using regularized regression. PloS One, 8(10). E73168.
A Goldenberg, S. Mostafavi, G. Quon, P. Boutros, and Q. Morris. (2011) Unsupervised detection of genes of influence in lung cancer using biological networks. Bioinformatics, 27(22), p. 316672.
Fellow Child & Brain Development
The Hospital for Sick Children, University of TorontoGenetics and Genome Biology, Department of Computer Science
PhD (Machine Learning) Carnegie Mellon University
MSc (Knowledge Discovery & Data Mining) Carnegie Mellon University
BEng (Engineering Mathematics and Computer Science) University of Louisville
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