Tyler O'Neal, Staff Editor HEALTH

Mizzou prof creates a new way to visualize mountains of biological data

Researchers led by the University of Missouri have created a new method for analyzing large amounts of biological data to help scientists draw faster conclusions for possible treatments.

Studying genetic material on a cellular level, such as single-cell RNA-sequencing, can provide scientists with a detailed, high-resolution view of biological processes at work. This level of detail helps scientists determine the health of tissues and organs, and better understand the development of diseases such as Alzheimer's that impacts millions of people. However, a lot of data is also generated and leads to the need for an efficient, easy-to-use way to analyze it.

Now, a team of engineers and scientists from the University of Missouri and the Ohio State University have created a new way to analyze data from single-cell RNA-sequencing by using a computer method called "machine learning." This method uses the power of high-performance computers to intelligently analyze large amounts of data and help scientists draw faster conclusions and move to the next stage of the research. Their methodology is detailed in a new paper published by an academic journal. This is a general example of a type of visual that a graph neural network can create with provided biological data.

"Single-cell genetic profiling is on the cutting edge of today's technological advances because it measures how many genes are present and how they are expressed from the level of an individual biological cell," said Dong Xu, a professor in the MU College of Engineering. "At a minimum, there could be tens of thousands of cells being analyzed in this manner, so there ends up being a huge amount of data collected. Currently, determining conclusions from this type of data can be challenging because a lot of data must be filtered through in order to find what researchers are looking for. So, we applied one of the newest machine-learning methods to tackle this problem -- a graph neural network."

After the supercomputer intelligently analyzes the data through a machine learning process, the graph neural network then takes the results and creates a visual representation of the data to help easily identify patterns. The graph is made up of dots -- each dot representative of a cell -- and similar types of cells are color-coded for easy recognition. Xu said precision medicine is a good example of how single-cell RNA-sequencing can be used.

"With this data, scientists can study the interactions between cells within the micro-environment of a cancerous tissue, or watch the T-cells, B-cells and immune cells all try to attack the cancerous cells," Xu said. "Therefore, in cases where a person has a strong immune system, and cancer hasn't fully developed yet, we can learn how cancer can possibly be killed at an early stage, and we have our results sooner because of machine learning, which leads us to a viable treatment faster." Dong Xu

Xu believes this is a great example of how engineers and biologists can work together to study problems or issues in biology. He hopes this method can be used by biologists as a new tool to help solve complex biological questions, such as a possible treatment for Alzheimer's disease.

Michigan team's predictive modeling provides a more accurate picture of how coughs disperse viruses

Tyler O'Neal, Staff Editor HEALTH

Scientists don't fully understand exactly how our coughs disperse virus particles into the air, but a new project led by the University of Michigan aims to provide a more accurate assessment of the dangers of close-proximity coughs and potentially lead to better barriers that can mitigate the hazards.

Previous research typically treats coughing as a single burst of air from the lungs, but that's not really how coughs work. More often, they're two or more pulses that expel droplets outward, creating swirling, turbulent airflows.

In the age of COVID-19, it's more important than ever that we understand those "multi-pulse" coughing events. They are getting a closer look from researchers at U-M and Auburn University. In addition to potential ramifications for COVID-19, it also could inform future scenarios where pathogen transmission between humans presents a major health risk. 

"There are a lot of key open questions in this space," said Jesse Capecelatro, U-M assistant professor of mechanical engineering. "There is evidence to suggest particles originating deeper within the lung carry more virus, and we want to know how those particles get dispersed during a cough.

"Do multiple pulses give rise to multiple vortex rings as the virus-laden air is expelled? And if so, what effect does that have on how the virus particles travel through space? Those are key things to know if you're trying to understand transmission and how to protect against it." Visualizations showing a snapshot of (top to bottom) single-, two-, and three-pulse coughing events. Colors show "fluid vorticity," or rotation. Vortex structures can be seen to persist closer to the mouth for two- and three-pulse cases. These swirls can accelerate particles and send them further from their source than typical cough models would predict. This is especially important because air in these later pulses comes from deeper in the lungs and, in the case of COVID-19, would likely carry higher viral load.

Using custom cough simulators connected to mannequins, as well as lasers and human subjects, researchers at Auburn University will make physical measurements of the airflow and expelled particles during simulated coughing. The mannequins will be outfitted with face masks and face shields. They'll use laser sheet imaging to allow for tracking of all particles expelled during coughing as they pass through a laser plane. This process will enable them to quantify not only the number of particles but also to track where they go, how they move with the airflow, and how fast they travel.

With that data from the high-resolution simulations and physical experiments in hand, Capecelatro and his U-M team will utilize high-performance supercomputing to generate improved predictive modeling, which will provide a more accurate picture of exactly how coughs disperse viruses.

U-M and Auburn researchers see the work as basic science that has the potential to shape how governments and communities respond to future outbreaks.

"The methods developed will be used to study how droplet-laden coughs interact with barriers such as face masks and face shields. And we'll evaluate how effective they are at blocking these flows and containing outbreaks," said Vrishank Raghav, assistant professor of aerospace engineering at Auburn.

"The new knowledge will also lead to the development of improved tools that can rapidly assess the risk of spreading infectious disease and assist in the development of public policy, such as physical distancing guidelines or indoor occupancy limits."

Yuan Yao and Kalvin Monroe, members of the Capecelatro Research Group, are contributors to the project. Capecelatro's fluid dynamics work has been in high demand this year due to the COVID-19 pandemic. He has studied particle flows on U-M buses, in hospital rooms, and in dental clinics. That work has contributed to changes in practice and policy designed to keep people safe.

For this project, the National Science Foundation has awarded the two institutions $465,000 over the next three years.

UK researchers use machine learning to rank cancer drugs in order of efficacy

Tyler O'Neal, Staff Editor HEALTH

Researchers from Queen Mary University of London have developed a machine learning algorithm that ranks drugs based on their efficacy in reducing cancer cell growth

Researchers from the Queen Mary University of London have developed a machine learning algorithm that ranks drugs based on their efficacy in reducing cancer cell growth. The approach may have the potential to advance personalized therapies in the future by allowing oncologists to select the best drugs to treat individual cancer patients.

The method, named Drug Ranking Using Machine Learning (DRUML), was published today in an academic journal and is based on machine learning analysis of data derived from the study of proteins expressed in cancer cells. Having been trained on the responses of these cells to over 400 drugs, DRUML predicts the best drug to treat a given cancer model.

Speaking of the new method, Professor Pedro Cutillas from the Queen Mary University of London, who led the study, said: "DRUML predicted drug efficacy in several cancer models and from data obtained from different laboratories and in a clinical dataset. These are exciting results because previous machine learning methods have failed to accurately predict drug responses in verification datasets, and they demonstrate the robustness and wide applicability of our method."

The research was funded by The Alan Turing Institute, Medical Research Council, Barts Charity, and Cancer Research UK.

How does DRUML work?

The team used datasets derived from proteomics (the study of proteins within cells) and phosphoproteomics (the study of how these proteins are modified) analyses of 48 leukemia, esophagus, and liver cancer cell lines as the input for DRUML to build models that may be applied to leukemia and solid tumors.

By training the models using the responses of these cells to 412 cancer drugs listed in drug response repositories, DRUML was able to produce ordered lists based on the effectiveness of the drugs to reduce cancer cell growth. The team then verified the predictive accuracy of the models using data obtained from 12 other laboratories and a clinical dataset of 36 primary acute myeloid leukemia samples.

Importantly, as new drugs are developed moving forwards, DRUML could be retrained to capture all clinically relevant cancer drugs.

Machine learning and personalized medicine

Cancers of the same type exhibit great variation in their genetic makeup and characteristics from patient to patient. In the clinic, this variation translates to patients having different responses to therapy. To address this issue, the field of personalized medicine aims to combine genetic insights with other clinical and diagnostic information to identify patterns that can allow clinicians to predict patient responses to therapies and select the most effective interventions.

The application of artificial intelligence and machine learning to biomedicine promises to aid personalized medicine and transform how cancers are diagnosed and treated in the future. This study represents a significant advancement in artificial intelligence in biomedical research and demonstrates that machine learning using proteomics and phosphoproteomics data may be an effective way of selecting the best drug to treat different cancer models.