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JBEI Paper Awarded ‘Publication of the Year’ by the Journal of Biological Engineering
The 2016 Joint BioEnergy Institute (JBEI) publication “End-to-end automated microfluidic platform for synthetic biology: from design to functional analysis” has been selected as the Journal of Biological Engineering Publication of the Year. Gregory Linshiz, former post-doctoral researcher and Director of Synthetic Biology Informatics Nathan Hillson conceived the project that served as the basis of this research. In the paper the authors present a programmable, multipurpose microfluidic platform and associated software and apply the platform to major steps of the synthetic biology research cycle: design, construction, testing, and analysis. The formal announcement of the award will be made at this year’s Annual…
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Bissell Awarded Fourteenth Annual AACR Award for Lifetime Achievement in Cancer Research
Mina J. Bissell, Distinguished Scientist in the Biological Systems and Engineering Division will be awarded the American Association for Cancer Research (AACR) Award for Lifetime Achievement in Cancer Research at the annual meeting on April 1. The Award was first established in 2004 to honor individuals who have made significant fundamental contributions to cancer research, either through a single scientific discovery or a collective body of work. The AACR honors Bissell for her pioneering work, which has identified the roles of the extracellular matrix and three-dimensional architecture in programs of gene expression in tissue morphogenesis and cancer. Her research contributions…
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Researchers Probe for Cancer Clues In a Sample of Blood
One day, patients may be able to monitor their body’s response to cancer therapy just by having their blood drawn. A new study, led by Amy Herr, faculty engineer in the Biological Systems & Engineering Division, has taken an important step in that direction by measuring a panel of cancer proteins in rare, individual tumor cells that float in the blood. Read more in Berkeley News.
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New Machine Learning Technique Provides Translational Results
A team of scientists has developed an unsupervised multi-scale machine learning technique that can automatically and specifically capture biomedical events or concepts directly from raw data. They have initiated the multi-disciplinary platform: Berkeley Biomedical Data Science Center (BBDS), which aims at facilitating and nurturing data-intensive biomedical science. They will apply this technique to three ongoing projects related to cancer risk assessment and diagnosis, as well as personalized medicine.
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What a Genome-Wide Screening Can Reveal about Cancer Survival
Berkeley Lab researchers in the Biological Systems and Engineering Division have developed a 12-gene score tied to the odds of relapse-free breast cancer survival. The scoring system is based on an analysis of large genomic datasets and patient data, and it could eventually be developed for clinical use. Antoine Snijders, research scientist, led the team that included Xuan Mao, Matthew Lee, Jeffrey Zhu and Carissa Zhu, all students from Campolindo High School in Moraga, California, who worked as interns at Berkeley Lab in 2016. “Berkeley Lab is committed to training the next generation of future cancer research scientists,” said Snijders. Under the…
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