Julián Candia, Ph.D.
Dr. Candia's research focuses on genome-scale analyses of human liver cancer using a variety of bioinformatics approaches, including computational modeling and machine learning. He is part of a basic/translational research program aimed at uncovering the molecular underpinnings of liver carcinogenesis and their clinical applications towards personalized cancer medicine.
1) computational biology
3) machine learning
4) systems biology
5) cancer genomics
Dr. Candia's research interests are focused on the development and application of analysis tools to provide new insight into biological processes relevant to human carcinogenesis. In particular, the availability of high-throughput, multiparametric datasets presents unique opportunities--and challenges--for the development of novel cross-disciplinary tools and frameworks. In this context, his goal is to apply his expertise at the crossroads of bioinformatics, machine learning, network science, and statistical physics to contribute innovative ideas to key problems in cancer systems biology.
His role as Staff Scientist is to work in collaboration with basic, translational, and clinical research scientists to develop quantitative methods of data analysis to better leverage the potential of state-of-the-art technologies in the biomedical realm.
Selected Recent Publications
eNetXplorer: an R package for the quantitative exploration of elastic net families for generalized linear models.BMC Bioinformatics . 20: 189, 2019. [ Journal Article ]
RefCell: multi-dimensional analysis of image-based high-throughput screens based on 'typical cells'.BMC Bioinformatics. 19: 427, 2018. [ Journal Article ]
- Scientific Reports. 7: 14248, 2017. [ Journal Article ]
Uncovering low-dimensional, miR-based signatures of acute myeloid and lymphoblastic leukemias with a machine-learning-driven network approach.Convergent Science Physical Oncology. 1: 025002, 2015. [ Journal Article ]
- PLoS Computational Biology. 9: e1003215, 2013. [ Journal Article ]
Dr. Julián Candia obtained his Licenciado (1999) and Ph.D. in Physics (2004) degrees from the University of La Plata (Argentina). He earned the 2000 “Dr. Joaquin V. Gonzalez” award for highest GPA. In subsequent years, he worked on a variety of problems involving computational and mathematical modeling of far-from-equilibrium dynamical systems, as well as on data mining and machine learning. Before joining NIH in 2014, he worked as a Postdoctoral Research Scientist at the University of Maryland with a joint appointment between the Department of Physics (College Park) and the School of Medicine (Baltimore), where he was a recipient of an NIH T32 Cancer Biology Training Grant to apply novel data analysis techniques to stem cell and leukemia research. He authored over 50 original research peer-reviewed articles and 2 book chapters.