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machine learning

Brain Cell Analysis

Caption: While analyzing brain cells, a computer program “thinks” about which cellular structure to identify.
Credit: Steven Finkbeiner, University of California, San Francisco and the Gladstone Institutes

For centuries, scientists have trained themselves to look through microscopes and carefully study their structural and molecular features. But those long hours bent over a microscope poring over microscopic images could be less necessary in the years ahead. The job of analyzing cellular features could one day belong to specially trained computers.

In a new study published in the journal Cell, researchers trained computers by feeding them paired sets of fluorescently labeled and unlabeled images of brain tissue millions of times in a row [1]. This allowed the computers to discern patterns in the images, form rules, and apply them to viewing future images. Using this so-called deep learning approach, the researchers demonstrated that the computers not only learned to recognize individual cells, they also developed an almost superhuman ability to identify the cell type and whether a cell was alive or dead. Even more remarkable, the trained computers made all those calls without any need for harsh chemical labels, including fluorescent dyes or stains, which researchers normally require to study cells. In other words, the computers learned to “see” the invisible!

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Liquid Biopsy Schematic

Caption: Liquid biopsy. Tumor cells shed protein and DNA into bloodstream for laboratory analysis and early cancer detection.

Early detection usually offers the best chance to beat cancer. Unfortunately, many tumors aren’t caught until they’ve grown relatively large and spread to other parts of the body. That’s why researchers have worked so tirelessly to develop new and more effective ways of screening for cancer as early as possible. One innovative approach, called “liquid biopsy,” screens for specific molecules that tumors release into the bloodstream.

Recently, an NIH-funded research team reported some encouraging results using a “universal” liquid biopsy called CancerSEEK [1]. By analyzing samples of a person’s blood for eight proteins and segments of 16 genes, CancerSEEK was able to detect most cases of eight different kinds of cancer, including some highly lethal forms—such as pancreatic, ovarian, and liver—that currently lack screening tests.

In a study of 1,005 people known to have one of eight early-stage tumor types, CancerSEEK detected the cancer in blood about 70 percent of the time, which is among the best performances to date for a blood test. Importantly, when CancerSEEK was performed on 812 healthy people without cancer, the test rarely delivered a false-positive result. The test can also be run relatively cheaply, at an estimated cost of less than $500.

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Anshul Kundaje

Anshul Kundaje / Credit: Nalini Kartha

Science has always fascinated Anshul Kundaje, whether it was biology, physics, or chemistry. When he left his home country of India to pursue graduate studies in electrical engineering at Columbia University, New York, his plan was to focus on telecommunications and computer networks. But a course in computational genomics during his first semester showed him he could follow his interest in computing without giving up his love for biology.

Now an assistant professor of genetics and computer science at Stanford University, Palo Alto, CA, Kundaje has received a 2016 NIH Director’s New Innovator Award to explore not just how the human genome sequence encodes function, but also why it functions in the way that it does. Kundaje even envisions a time when it might be possible to use sophisticated computational approaches to predict the genomic basis of many human diseases.

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Meena Madhur

Meena Madhur / Credit: John Russell

If Meena Madhur is correct, people with hypertension will one day pay as much attention to their immune cell profiles as their blood pressure readings. A physician-researcher at Vanderbilt University School of Medicine, Nashville, Madhur is one of a growing number of scientists who thinks the immune system contributes to—or perhaps even triggers—hypertension, which increases the risk of stroke, heart disease, kidney disease, and other serious health problems.

About one of every three adult Americans currently have hypertension, yet a surprising number don’t know they have it and less than half have their high blood pressure under control—leading many health experts to refer to the condition as a “silent killer”[1,2]. For many folks, blood pressure control can be achieved through lifestyle changes, such as losing weight, exercising, limiting salt intake, and taking blood pressure medicines prescribed by their health-care provider. Unfortunately, such measures don’t work for everyone, and some people continue to suffer damage to their kidneys and blood vessels from poorly controlled hypertension.

Madhur wants to know whether the immune system might be playing a role, and whether this might hold some clues for developing new, more targeted ways of treating high blood pressure. To get such answers, this practicing cardiologist will use her 2016 NIH Director’s New Innovator Award to conduct sophisticated, single-cell analyses of the immune systems of people with and without hypertension. Her goal is to produce the most comprehensive catalog to date of which human immune cells might be involved in hypertension.

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Sleeping baby

Stockbyte

Research shows that the roots of autism spectrum disorder (ASD) generally start early—most likely in the womb. That’s one more reason, on top of a large number of epidemiological studies, why current claims about the role of vaccines in causing autism can’t be right. But how early is ASD detectable? It’s a critical question, since early intervention has been shown to help limit the effects of autism. The problem is there’s currently no reliable way to detect ASD until around 18–24 months, when the social deficits and repetitive behaviors associated with the condition begin to appear.

Several months ago, an NIH-funded team offered promising evidence that it may be possible to detect ASD in high-risk 1-year-olds by shifting attention from how kids act to how their brains have grown [1]. Now, new evidence from that same team suggests that neurological signs of ASD might be detectable even earlier.

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