NYU Data Science newsletter – December 7, 2015

GROUP CURATION: N/A

The recent Stanford Genome Technology Center retreat drove home for me why it’s a great idea to put biochemists, geneticists, engineers, and physicians in a lab together.

Set up in 1989 to establish automated methods for the Human Genome Project, SGTC works to increase the speed, accuracy, and cost-effectiveness of genomic, biomedical, and diagnostic technologies. The center integrates personnel from Stanford’s departments of genetics, biochemistry, medicine, and electrical engineering. At the two-day retreat, researchers presented their latest work in areas like synthetic biology, genome sequencing applications, single-cell approaches, and devices for cellular and molecular detection.

Early this week the Austrian security company SEC Consult found that more than three million routers, modems, and other devices are vulnerable to being hijacked over the Internet. Instead of giving each device a unique encryption key to secure its communications, manufacturers including Cisco and General Electric had lazily used a much smaller number of security keys over and over again.

That security screwup was discovered with the help of Censys, a search engine aimed at helping security researchers find the Internet’s dirty little secrets by tracking all the devices hooked up to it. Launched in October by researchers at the University of Michigan, it is likely to produce many more hair-raising findings. Google is providing infrastructure to power the search engine, which is free to use.

This week, Montreal hosts the 29th Annual Conference on Neural Information Processing Systems (NIPS 2015), a machine learning and computational neuroscience conference that includes invited talks, demonstrations and oral and poster presentations of some of the latest in machine learning research. Google will have a strong presence at NIPS 2015, with over 140 Googlers attending in order to contribute to and learn from the broader academic research community by presenting technical talks and posters, in addition to hosting workshops and tutorials.

A new scientific model that incorporates the myriad drivers of depression could lead to more precise treatment for an illness that affects 350 million worldwide.

Developed by scientists at Michigan State University and Massachusetts Institute of Technology, and funded by the National Institute of Mental Health, the model provides a better understanding of depression and the foundation for creating a pioneering tool to attack the complex disorder.

Over the past few years, the first biotech startup incubators have popped up in New York City in places like Brooklyn and West Harlem. And there’s a chance the next one may come from an unlikely source: a nonprofit organization known as the New York Stem Cell Foundation.

According to Susan Solomon, the CEO and co-founder of the NYSCF—a roughly 10-year-old entity that is one of the world leaders in stem cell research—the nonprofit plans to start spinning out its work into companies, and wants to create an in-house incubator as part of that effort. The NYSCF just signed a lease to move into a new home (pictured above) on West 54th St. and 11th Ave. in Manhattan, where it’ll have over 40,000 square feet of space—more than double what it currently has.

Perhaps the biggest item on the NYSCF’s agenda is finding ways to commercialize its work, something it hasn’t done as of yet. That’s where the incubator comes in.

Dr. Jessica Mega, a star cardiologist at Harvard Medical School, swapped coasts early this year to become medical director of Google Life Sciences — the Silicon Valley giant’s new foray into health and medicine. … Mega recently sat down with STAT at Google’s offices to talk about the baseline study.

A few days ago there was a minor Twitterstorm over a particular paper that claimed to have found an imaging biomarker that was predictive of some aspect of outcome in adults with autism. The details actually don’t matter that much and I don’t intend to pick on that study in particular, or even link to it, as it’s no worse than many that get published. What it prompted, though, was more interesting – a debate on research practices in the field of cognitive neuroscience and neuroimaging, particularly relating to the size of studies required to address some research questions and the scale of research operation they might entail.

What kicked off the debate was a question of how likely the result they found was to be “real”; i.e., to represent a robust finding that would replicate across future studies and generalise to other samples of autistic patients. I made a fairly uncompromising prediction that it would not replicate, which was based on the fact that the finding derived from: a small sample (n=31, in this case, but split into two), an exploratory study (i.e., not aimed at or constrained by any specific hypothesis, so that group differences in pretty much any imaging parameter would do) and lack of a replication sample (to test directly, with exactly the same methodology, whether the findings from the study were robust, prior to bothering anyone else with them).

… “We’re just at the very, very beginning of a computational era — computation will touch every aspect of our lives,” says Cynthia Dwork, a cryptographer and distinguished scientist at Microsoft Research.

Dwork is among a bevy of Microsoft researchers and engineers whose work — more than 20 accepted papers — will be presented next week at the 2015 Conference and Workshop on Neural Information Processing Systems (NIPS). It is the premiere conference on machine learning, but the pervasive nature of machine learning has seen it nearly double in size, growing to more than 4,000 attendees compared to 2,500 last year.

This study takes a stratified random sample of articles published in 2014 from the top 10 journals in the disciplines of biology, chemistry, mathematics, and physics, as ranked by impact factor. Sampled articles were examined for their reporting of original data or reuse of prior data, and were coded for whether the data was publicly shared or otherwise made available to readers. Other characteristics such as the sharing of software code used for analysis and use of data citation and DOIs for data were examined. The study finds that data sharing practices are still relatively rare in these disciplines’ top journals, but that the disciplines have markedly different practices. Biology top journals share original data at the highest rate, and physics top journals share at the lowest rate. Overall, the study finds that within the top journals, only 13% of articles with original data published in 2014 make the data available to others.

… Innovation is usually a lurching journey of discovery and problem-solving. As a result, it’s an iterative, often slow-moving process that requires patience and discipline. Hackathons, with their feverish pace, scant parameters, and winner-take-all culture, don’t just sidestep this process, they discourage it. And while that’s a reason for their appeal, there’s very little evidence of hackathons that lead directly to major market successes.