Most human diseases can be traced to malfunctioning parts of a cell -- a tumor is able to grow because a gene wasn't accurately translated into a particular protein or a metabolic disease arises because mitochondria aren't firing properly, for example. But to understand what parts of a cell can go wrong in a disease, scientists first need to have a complete list of parts. By combining microscopy, biochemistry techniques and artificial intelligence, researchers at University of California San Diego School of Medicine and collaborators have taken what they think may turn out to be a significant leap forward in the understanding of human cells. The technique, known as Multi-Scale Integrated Cell (MuSIC), is described November 24, 2021 in Nature. "If you imagine a cell, you probably picture the colorful diagram in your cell biology textbook, with mitochondria, endoplasmic reticulum and nucleus. But is that the whole story? Definitely not," said Trey Ideker, PhD, professor at UC San Diego School of Medicine and Moores Cancer Center.

Summary: New artificial intelligence technology reveals previously unknown cell components. The findings may shed new light on human development and diseases. Most human diseases can be traced to malfunctioning parts of a cell -- a tumor is able to grow because a gene wasn't accurately translated into a particular protein or a metabolic disease arises because mitochondria aren't firing properly, for example. But to understand what parts of a cell can go wrong in a disease, scientists first need to have a complete list of parts. By combining microscopy, biochemistry techniques and artificial intelligence, researchers at University of California San Diego School of Medicine and collaborators have taken what they think may turn out to be a significant leap forward in the understanding of human cells.

Only 4 percent of all cancer therapeutic drugs under development earn final approval by the U.S. Food and Drug Administration (FDA). "That's because right now we can't match the right combination of drugs to the right patients in a smart way," said Trey Ideker, Ph.D., professor at University of California San Diego School of Medicine and Moores Cancer Center. "And especially for cancer, where we can't always predict which drugs will work best given the unique, complex inner workings of a person's tumor cells." In a paper published October 20, 2020 in Cancer Cell, Ideker and Brent Kuenzi, Ph.D., and Jisoo Park, Ph.D., postdoctoral researchers in his lab, describe DrugCell, a new artificial intelligence (AI) system they created that not only matches tumors to the best drug combinations, but does so in a way that makes sense to humans. "Most AI systems are'black boxes'--they can be very predictive, but we don't actually know all that much about how they work," said Ideker, who is also co-director of the Cancer Cell Map Initiative and the National Resource for Network Biology.

University of California San Diego School of Medicine and Moores Cancer Center say they have created a new artificial intelligence (AI) system called DrugCell that reportedly matches tumors to the best drug combinations, but does so in way that clearly makes sense. "That's because right now we can't match the right combination of drugs to the right patients in a smart way," said Trey Ideker, PhD, professor at University of California San Diego School of Medicine and Moores Cancer Center. "And especially for cancer, where we can't always predict which drugs will work best given the unique, complex inner workings of a person's tumor cells." Currently, Only four percent of all cancer therapeutic drugs under development earn final approval by the FDA. In a paper "Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells" published in Cancer Cell, Ideker, Brent Kuenzi, PhD, and Jisoo Park, PhD, postdoctoral researchers in his lab, published a paper on their work.

A new artificial intelligence (AI) system called DrugCell, developed by researchers at University of California San Diego School of Medicine and Moores Cancer Center can reportedly match tumors to the best drug combinations, in a way that has not bee possible previously. "That's because right now we can't match the right combination of drugs to the right patients in a smart way," said Trey Ideker, PhD, professor at University of California San Diego School of Medicine and Moores Cancer Center. "And especially for cancer, where we can't always predict which drugs will work best given the unique, complex inner workings of a person's tumor cells." Currently, Only four percent of all cancer therapeutic drugs under development earn final approval by the FDA. In a paper "Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells" published in Cancer Cell, Ideker, Brent Kuenzi, PhD, and Jisoo Park, PhD, postdoctoral researchers in his lab, published a paper on their work.

The deep neural networks that power today's Artificial Intelligence (AI) systems work in mysterious ways. They're black boxes – a question goes in and an answer comes out the other side, and while we might not know exactly how a black box AI system works, importantly we know that it does work. Over the past year there have been a few attempts to try to read and analyse the minds of these black boxes, from companies like Nvidia who use visualisations, to MIT who tried to analyse the neural network's layers, to Columbia University who pitted AI's against each other. But, frankly, none of them even come close to the out the box thinking, if you'll excuse the pun, of this approach – using biology itself to crack the black box open. A new study that mapped a neural network to the biological components within a simple yeast cell allowed researchers to watch the AI system at work, and it also gave them insights into cell biology in the process, and the resulting technology could help in the quest for new cancer drugs and personalised treatments.

The deep neural networks that power today's artificial intelligence systems work in mysterious ways. They're black boxes: A question goes in ("Is this a photo of a cat?" "What's the best next move in this game of Go?" "Should this self-driving car accelerate at this yellow light?"), and an answer comes out the other side. We may not know exactly how a black box AI system works, but we know that it does work. But a new study that mapped a neural network to the components within a simple yeast cell allowed researchers to watch the AI system at work. And it gave them insights into cell biology in the process.

"It seems like every time you turn around, someone is talking about the importance of artificial intelligence and machine learning," said Trey Ideker, PhD, University of California San Diego School of Medicine and Moores Cancer Center professor. "But all of these systems are so-called'black boxes.' They can be very predictive, but we don't actually know all that much about how they work." Ideker gives an example: machine learning systems can analyze the online behaviors of millions of people to flag an individual as a potential "terrorist" or "suicide risk." "Yet we have no idea how the machine reached that conclusion," he said.

Scientists have created a virtual yeast cell model that can learn from real-world behaviors, a key step in utilizing artificial intelligence in healthcare to diagnose diseases. A team of researchers from the University of California San Diego has developed what they called a "visible" neural network that enabled them to build DCell--a machine learning model of a functioning brewer's yeast cell that is commonly used in basic research. Machine learning systems are built on a neural network that consist of layers of artificial neurons that are tied together by seemingly random connections between neurons. The systems "learn" by fine-tuning those connections. In DCell, the researchers amassed all knowledge of cell biology in one place and created a hierarchy of the cellular components.