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Robinder Gauba

Researcher at Georgetown University

Publications -  10
Citations -  166

Robinder Gauba is an academic researcher from Georgetown University. The author has contributed to research in topics: Systems medicine & Colorectal cancer. The author has an hindex of 5, co-authored 10 publications receiving 152 citations. Previous affiliations of Robinder Gauba include Georgetown University Medical Center.

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G-DOC: A Systems Medicine Platform for Personalized Oncology

TL;DR: The Georgetown Database of Cancer (G-DOC) as discussed by the authors is a Web platform that enables basic and clinical research by integrating patient characteristics and clinical outcome data with a variety of high-throughput research data in a unified environment.
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Genome-wide multi-omics profiling of colorectal cancer identifies immune determinants strongly associated with relapse.

TL;DR: The immune response was the biologically most coherent signature that emerged from analyses among several other biological processes and corroborates other studies showing a strong immune response in patients less likely to relapse.
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A case study for cloud based high throughput analysis of NGS data using the globus genomics system

TL;DR: A case study of a practical solution that simplifies terabyte scale data handling and provides advanced tools for NGS data analysis using the Globus Genomics system, which is an enhanced Galaxy workflow system made available as a service.
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In Silico Discovery of Mitosis Regulation Networks Associated with Early Distant Metastases in Estrogen Receptor Positive Breast Cancers

TL;DR: A subset of 44 differently expressed genes (DEGs) was found common to all three studies and characterized by mitotic checkpoint genes and pathways that regulate mitotic spindle and chromosome dynamics, which may reflect the clinical utility of mitotic poisons in metastatic breast cancer.

G-DOC: A Systems Medicine Platform for Personalized Oncology 1

TL;DR: The Georgetown Database of Cancer is described, a Web platform that enables basic and clinical research by integrating patient characteristics and clinical outcome data with a variety of high-throughput research data in a unified environment and will help facilitate systems medicine by providing identification of trends and patterns in integrated data sets.