National Institutes of Health (NIH)

Today marks the beginning of Open Access Week, a six-day celebration and reflection on the global movement to promote “the free, immediate, online access to the results of scholarly research, and the right to use and re-use those results as you need.” Read More »

Open access to academic research has never been a hotter topic. But it's still held back by myths and misunderstandings repeated by people who should know better. The good news is that open access has been successful enough to attract comment from beyond its circle of pioneers and experts. The bad news is that a disappointing number of policy-makers, journalists and academics opine in public without doing their homework. Read More »

In their work toward 3-D printing transplantable tissues and organs, bioengineers and scientists from Rice University and Baylor College of Medicine have demonstrated a key step on the path to generate implantable tissues with functioning capillaries. In a paper published online in the journal Biomaterials Science, a team from the laboratories of Rice bioengineer Jordan Miller and Baylor College of Medicine biophysicist Mary Dickinson showed how to use a combination of human endothelial cells and mesenchymal stem cells to initiate a process called tubulogenesis that is crucial to the formation of blood-transporting capillaries...

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Brown University computer scientists have developed a new interactive tool to help researchers and clinicians explore the genetic underpinnings of cancer. The tool — dubbed MAGI, for Mutation Annotation and Genome Interpretation — is an open-source web application that enables users to search, visualize, and annotate large public cancer genetics datasets, including data from The Cancer Genome Atlas (TCGA) project. “The main motivation for MAGI has been to reduce the computational burden required for researchers or doctors to explore and annotate cancer genomics data,” said Max Leiserson, a Ph.D. student at Brown who led the development of the tool.

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The Penn Medicine Institute for Biomedical Informatics has launched a free, open-source automated machine learning system for data analysis that is designed for anyone to use, from a high school student looking to gain insight on their baseball team's statistics, to trained researchers looking for associations between cancer and environmental factors. "Penn AI," the first widely available tool of its kind, seeks to lower the barrier for entry into artificial intelligence, allowing users to bring in their own datasets or use the several hundred that are available for download within the tool. With a user-friendly dashboard easily run on a laptop, Penn AI is also designed to learn as it goes, ultimately making analysis suggestions based on the "experience" it gains through use.

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