Julián Candia, Ph.D.

Julián Candia, Ph.D.
Staff Scientist
Laboratory of Human Carcinogenesis

Team Member of:

Xin Wei Wang, 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.

Areas of Expertise

1) computational biology 2) bioinformatics 3) machine learning 4) systems biology 5) cancer genomics 6) statistics

Contact Info

Julián Candia, Ph.D.
Center for Cancer Research
National Cancer Institute
Building 37, Room 3050A
Bethesda, MD 20892
Ph: 240-760-7341
julian.candia@nih.gov

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.

NIH Scientific Focus Areas:
Cancer Biology, Cell Biology, Computational Biology, Genetics and Genomics, Molecular Biology and Biochemistry

Selected Recent Publications

  1. The genomic landscape of Mongolian hepatocellular carcinoma.
    Candia J, Bayarsaikhan E, Tandon M, Budhu A, Forgues M, Tovuu LO, Tudev U, Lack J, Chao A, Chinburen J, Wang XW
    Nature Communications. 11: 4383, 2020. [ Journal Article ]
  2. eNetXplorer: an R package for the quantitative exploration of elastic net families for generalized linear models.
    Candia J, Tsang JS
    BMC Bioinformatics . 20: 189, 2019. [ Journal Article ]
  3. Assessment of variability in the SOMAscan assay.
    Candia J, Cheung F, Kotliarov Y, Fantoni G, Sellers B, Griesman T, Huang J, Stuccio S, Zingone A, Ryan BM, Tsang JS, Biancotto A.
    Scientific Reports. 7: 14248, 2017. [ Journal Article ]
  4. Uncovering low-dimensional, miR-based signatures of acute myeloid and lymphoblastic leukemias with a machine-learning-driven network approach.
    Candia J, Cherukuri S, Guo Y, Doshi KA, Banavar JR, Civin CI, Losert W.
    Convergent Science Physical Oncology. 1: 025002, 2015. [ Journal Article ]
  5. From cellular characteristics to disease diagnosis: uncovering phenotypes with supercells.
    Candia J, Maunu R, Driscoll M, Biancotto A, Dagur P, McCoy JP Jr, Sen HN, Wei L, Maritan A, Cao K, Nussenblatt RB, Banavar JR, Losert W.
    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 cancer research. He authored over 60 original research peer-reviewed articles and 2 book chapters.