Summer has finally arrived, and it's once again time to shine some light on NIH's rich history. Over the past couple months, NIH has celebrated several important anniversaries, including the 20th birthday of NIH's Vaccine Research Center and the 70th anniversary of the NIH Record newsletter. Read on to learn more about these milestones and other fun facts and intriguing objects from NIH's past!
While the Human Genome Project accomplished a remarkable feat in sequencing all the genes in the human genome, technological limitations still left significant swaths of our genetic blueprints unexplored. Recent advances in DNA sequencing are starting to fill in those gaps, but these new technologies require new computational tools to make sense of the data they generate. That’s where computer scientists like the IRP’s Adam Phillippy, Ph.D., come in.
Virtually all parents would agree that having kids is a massive undertaking, and not just after they’re born. Many couples struggle to conceive, and each year thousands of American women experience complications when giving birth. With the help of the NIH’s state-of-the-art supercomputer, Biowulf, IRP senior investigator Rajeshwari Sundaram, Ph.D., develops and refines statistical tools that can guide prospective parents and their doctors through these challenges.
Science is a process of trial and error. Most successful research publications are preceded by at least a few false starts and perhaps weeks or even months of tinkering to get experiments to work. For IRP senior investigator Carson Chow, Ph.D., this process of testing and throwing out one potential solution after another is an essential part of his research, so much so that he may go through thousands of iterations before arriving at one that works. However, rather than test each approach himself, he leverages the IRP’s considerable computing power to considerably accelerate the process of sorting the wheat from the chaff.
The human genome comprises roughly three billion base pairs and around 20,000 protein-coding genes, according to recent estimates. That’s a lot of information crammed into the tiny nucleus of a cell, and it doesn’t even include the many genes that do not produce a protein or the fact that most genes come in multiple flavors that vary in different individuals. Add to that the phenomenon of an identical gene being either more or less active in two different people and you can quickly end up with genomic datasets that would overload nearly any computer. Fortunately for IRP senior investigator Daniel Levy, M.D., the NIH IRP has one of the few computer systems in the world that can handle this mountain of information.
For most of their history, computers have been limited to mindlessly executing the instructions their programmers give them. However, recent advances have given rise to the intertwined fields of artificial intelligence (AI) and machine learning, which focus on the creation of computer programs that can operate independently and even teach themselves to perform specific, specialized tasks. In 2013, the online PubMed database listed only 200 research publications related to ‘deep learning,’ a new type of machine learning that has shown success for particularly difficult tasks like object and speech recognition. Just four years later, in 2017, that number exceeded 1,100.
Access to robust computing resources provides a critical foundation for advancing the wide variety of biomedical research taking place within the NIH’s Intramural Research Program (IRP). Whether performing molecular modeling simulations, generating whole-genome sequencing data, deducing the structures of biomolecules, or advancing drug discovery efforts, our ability to analyze large-scale biological and biomedical data strongly depends on our ability to employ computationally intensive approaches that produce interpretable results and advance translational efforts aimed at improving human health.
For over a decade, my family shared our home with a short, fat beagle named Kayla Sue. She had big floppy ears, a tail as straight as an exclamation point, and a coat of fur that was a patchwork of white, brown, and black splotches. Her love of chasing small animals was matched only by her enthusiasm for eating, napping, and belly rubs. One of my best friends growing up, on the other hand, had a mean-spirited Dachshund named Rocky who would not let anyone outside his family touch his long, brown, sausage-shaped body. Meanwhile, one of my brother’s close childhood friends had two humongous, overly-friendly, black-and-brown German shepherds that would immediately bowl you over when you walked through the front door.
It doesn’t take a particularly sharp observer to notice that, despite being the same species, the more than 300 breeds of dog have remarkably different physical and behavioral traits. But what remains less clear even today are the specific biological roots that produce these widely varying attributes. And, perhaps more importantly, scientists seek to understand how learning about that immense diversity might help us improve the health of our canine companions – and ourselves.
If you were going to train an artificial intelligence (AI) system to understand and accurately diagnose medical images, what kind of information do you think would be most effective: lots of general image data, or small amounts of specific data?
