René Vidal, at the podium, introduces the event “ChatGPT turns one: How is generative AI reshaping science?” Bhuvnesh Jain, left at the table, moderated the discussion with Sudeep Bhatia, Konrad Kording, Andrew Zahrt, and Nick Pangakis.
As a neuroscientist surveying the landscape of generative AI—artificial intelligence capable of generating text, images, or other media—Konrad Kording cites two potential directions forward: One is the “weird future” of political use and manipulation, and the other is the “power tool direction,” where people use ChatGPT to get information as they would use a drill to build furniture.
“I’m not sure which of those two directions we’re going but I think a lot of the AI people are working to move us into the power tool direction,” says Kording, a Penn Integrates Knowledge (PIK) University professor with appointments in the Perelman School of Medicine and School of Engineering and Applied Science. Reflecting on how generative AI is shifting the paradigm of science as a discipline, Kording said he thinks “it will push science as a whole into a much more collaborative direction,” though he has concerns about ChatGPT’s blind spots.
Kording joined three University of Pennsylvania researchers from the chemistry, political science, and psychology departments sharing their perspectives in the recent panel “ChatGPT turns one: How is generative AI reshaping science?” PIK Professor René Vidal opened the event, which was hosted by the School of Arts & Sciences’ Data Driven Discovery Initiative (DDDI), and Bhuvnesh Jain, physics and astronomy professor and co-faculty director of DDDI, moderated the discussion.
“Generative AI is moving so rapidly that even if it’s a snapshot, it will be very interesting for all of us to get that snapshot from these wonderful experts,” Jain said. OpenAI launched ChatGPT, a large language model (LLM)-based chatbot, on Nov. 30, 2022, and it rapidly ascended to ubiquity in news reports, faculty discussions, and research papers. Colin Twomey, interim executive director of DDDI, told Penn Today that it’s an open question as to how it will change the landscape of scientific research, and the` idea of the event was to solicit colleagues’ opinions on interesting directions in their fields.
Konrad Paul Kording is Nathan Francis Mossell University Professor in Bioengineering and Computer and Information Science in Penn Engineering and in Neuroscience in the Perelman School of Medicine.
We hope you will join us for the 2023 Herman P. Schwan Distinguished Lecture by Dr. Dorin Comaniciu, hosted by the Department of Bioengineering.
Wednesday, December 13, 2023 1:00 PM ET Location: Wu & Chen Auditorium (Levine 101) The lecture and Q&A will be followed by a light reception in Levine Lobby.
Speaker:Dorin Comaniciu, Ph.D. Senior Vice President Artificial Intelligence and Digital Innovations Siemens Healthineers
About Dorin Comaniciu:
Dr. Comaniciu serves as Senior Vice President for Artificial Intelligence and Digital Innovation at Siemens Healthineers. His scientific contributions to machine intelligence and computational imaging have translated to multiple clinical products focused on improving the quality of care, specifically in the fields of diagnostic imaging, image-guided therapy, and precision medicine.
Comaniciu is a member of the National Academy of Medicine, the Romanian Academy, and a Top Innovator of Siemens. He is a Fellow of the IEEE, ACM, MICCAI Society, and AIMBE, and a recipient of the IEEE Longuet-Higgins Prize for fundamental contributions to computer vision. Recent recognition of his work includes an honorary doctorate from Friedrich-Alexander University of Erlangen-Nuremberg.
He has co-authored 550 granted patents and 350 peer-reviewed publications that have received 61,000 citations, with an h-index of 102, in the areas of machine intelligence, medical imaging, and precision medicine.
A graduate of University of Pennsylvania’s Wharton School, Comaniciu received a doctorate in electrical and computer engineering from Rutgers University and a doctorate in electronics and telecommunications from Polytechnic University of Bucharest.
He is an advocate for technological innovations that save and enhance lives, addressing critical issues in global health.
About the Schwan Lecture:
The Herman P. Schwan Distinguished Lecture is in honor of one of the founding members of the Department of Bioengineering, who emigrated from Germany after World War II and helped create the field of bioengineering in the US. It recognizes people with a similar transformative impact on the field of bioengineering.
Breaking the code of the immune system could provide a new fundamental way of understanding, treating, and preventing every type of disease. Penn Medicine is investing in key discoveries about immunity and immune system function, and building infrastructure, to make that bold idea a reality.
This grandfather lives with primary progressive multiple sclerosis (MS), an autoimmune disorder that he controls with a medicine that depletes his body of the type of immune cells that make antibodies. So while he has completed his COVID-19 vaccine course, his immune system function isn’t very strong—and the invitation has arrived at a time when COVID-19 is still spreading rapidly.
You can imagine the scene as an older gentleman lifts a thick, creamy envelope from his mailbox, seeing his own name written in richly scripted lettering. He beams with pride and gratitude at the sight of his granddaughter’s wedding invitation. Yet his next thought is a sober and serious one. Would he be taking his life in his hands by attending the ceremony?
“In the past, all we could do was [measure] the antibody response,” says Amit Bar-Or, the Melissa and Paul Anderson President’s Distinguished Professor in Neurology at the Perelman School of Medicine, and chief of the Multiple Sclerosis division. “If that person didn’t have a good antibody response, which is likely because of the treatment they’re on, we’d shrug our shoulders and say, ‘Maybe you shouldn’t go because we don’t know if you’re protected.’”
Today, though, Bar-Or can take a deeper dive into his patients’ individual immune systems to give them far more nuanced recommendations. A clinical test for immune cells produced in response to the COVID-19 vaccine or to the SARS-CoV-2 virus itself—not just antibodies—was one of the first applied clinical initiatives of a major new Immune Health® project at Penn Medicine. Doctors were able to order this test and receive actionable answers through the Penn Medicine electronic health record for patients like the grandfather with MS.
“With a simple test and an algorithm we can have a very different discussion,” Bar-Or says. A test result showing low T cells, for instance, would tell Bar-Or his patient may get a meaningful jolt in immunity from a vaccine booster, while low antibody levels would suggest passive antibody therapy is more helpful. Or, the test might show his body is already well primed to protect him, making it reasonably safe to attend the wedding.
This COVID-19 immunity test is only the beginning.
Physicians and scientists at Penn Medicine are imagining a future where patients can get a precise picture of their immune systems’ activity to guide treatment decisions. They are working to bring the idea of Immune Health to life as a new area of medicine. In labs, in complex data models, and in the clinic, they are beginning to make sense out of the depth and breadth of the immune system’s millions of as-yet-undeciphered signals to improve health and treat illnesses of all types.
Penn Medicine registered the trademark for the term “Immune Health” in recognition of the potential impact of this research area and its likelihood to draw non-academic partners as collaborators in its growth. Today, at the south end of Penn’s medical campus, seven stories of research space are being added atop an office building at 3600 Civic Center Blvd., including three floors dedicated to Immune Health, autoimmunity, and immunology research.
The concept behind the whole project, says E. John Wherry, director of Penn Medicine’s Institute for Immunology and Immune Health (I3H), “is to listen to the immune system, to profile the immune system, and use those individual patient immune fingerprints to diagnose and treat diseases as diverse as immune-related diseases, cancer, cardiovascular disease, Alzheimer’s, and many others.”
The challenge is vast. Each person’s immune system is far more complex than antibodies and T cells alone. The immune system is made of multiple interwoven layers of complex defenders—from our skin and mucous membranes to microscopic memory B cells that never forget a childhood infection—meant to fortify our bodies from germs and disease. It is a sophisticated system that learns and adapts over our lifetimes in numerous ways, and it also falters and fails in some ways we understand and others that remain mysterious. And each person’s intricate internal battlefield is in some way unique.
The immune system is not just a set of defensive barricades, either. It’s also a potential source of deep insight about a person’s physiological functioning and responses to medical treatments.
“The immune system is sensing and keeping track of basically all tissues and all cells in our body all the time,” Wherry says. “It is surveying the body trying to clean up any invaders and restore homeostasis by maintaining good health.”
“Our goal is to essentially break the code of the immune system,” says Jonathan Epstein, executive vice dean of the Perelman School of Medicine and chief scientific officer at Penn Medicine. “By doing so, we believe we will be able to determine your state of health and your response to therapies in essentially every human disease.”
In an era peppered by breathless discussions about artificial intelligence—pro and con—it makes sense to feel uncertain, or at least want to slow down and get a better grasp of where this is all headed. Trusting machines to do things typically reserved for humans is a little fantastical, historically reserved for science fiction rather than science.
Not so much for César de la Fuente, PhD, the Presidential Assistant Professor in Psychiatry, Microbiology, Chemical and Biomolecular Engineering, and Bioengineering in Penn’s Perelman School of Medicine and School of Engineering and Applied Science. Driven by his transdisciplinary background, de la Fuente leads the Machine Biology Group at Penn: aimed at harnessing machines to drive biological and medical advances.
“Biology is complexity, right? You need chemistry, you need mathematics, physics and computer science, and principles and concepts from all these different areas, to try to begin to understand the complexity of biology,” he said. “That’s how I became a scientist.”
Marrying artificial intelligence with advanced experimental methods, the Machine Biology Group has mined the ancient past for future medical breakthroughs, bringing extinct molecules back to life. (Image credit: Ella Marushchenko)
“We need to think big in antibiotics research,” says Cesar de la Fuente. “Over one million people die every year from drug-resistant infections, and this is predicted to reach 10 million by 2050. There hasn’t been a truly new class of antibiotics in decades, and there are so few of us tackling this issue that we need to be thinking about more than just new drugs. We need new frameworks.”
Marrying artificial intelligence with advanced experimental methods, the group has mined the ancient past for future medical breakthroughs. In a recent study published in CellHost and Microbe, the team has launched the field of “molecular de-extinction.”
Our genomes – our genetic material – and the genomes of our ancient ancestors, express proteins with natural antimicrobial properties. “Molecular de-extinction” hypothesizes that these molecules could be prime candidates for safe new drugs. Naturally produced and selected through evolution, these molecules offer promising advantages over molecular discovery using AI alone.
In this paper, the team explored the proteomic expressions of two extinct organisms –Neanderthals and Denisovans, archaic precursors to the human species – and found dozens of small protein sequences with antibiotic qualities. Their lab then worked to synthesize these molecules, bringing these long-since-vanished chemistries back to life.
“The computer gives us a sequence of amino acids,” says de la Fuente. “These are the building blocks of a peptide, a small protein. Then we can make these molecules using a method called ‘solid-phase chemical synthesis.’ We translate the recipe of amino acids into an actual molecule and then build it.”
The team next applied these molecules to pathogens in a dish and in mice to test the veracity and efficacy of their computational predictions.
“The ones that worked, worked quite well,” continues de la Fuente. “In two cases, the peptides were comparable – if not better – than the standard of care. The ones that didn’t work helped us learn what needed to be improved in our AI tools. We think this research opens the door to new ways of thinking about antibiotics and drug discovery, and this first step will allow scientists to explore it with increasing creativity and precision.”
Traumatic brain injury (TBI) has disabled 1 to 2% of the population, and one of their most common disabilities is problems with short-term memory. Electrical stimulation has emerged as a viable tool to improve brain function in people with other neurological disorders.
Now, a new study in the journal Brain Stimulation shows that targeted electrical stimulation in patients with traumatic brain injury led to an average 19% boost in recalling words.
Led by University of Pennsylvania psychology professor Michael Jacob Kahana, a team of neuroscientists studied TBI patients with implanted electrodes, analyzed neural data as patients studied words, and used a machine learning algorithm to predict momentary memory lapses. Other lead authors included Wesleyan University psychology professor Youssef Ezzyat and Penn research scientist Paul Wanda.
“The last decade has seen tremendous advances in the use of brain stimulation as a therapy for several neurological and psychiatric disorders including epilepsy, Parkinson’s disease, and depression,” Kahana says. “Memory loss, however, represents a huge burden on society. We lack effective therapies for the 27 million Americans suffering.”
Michael Kahana is the Edmund J. and Louise W. Kahn Term Professor of Psychology at the University of Pennsylvania. He is a member of the Penn Bioengineering Graduate Group.
Artificial intelligence is a new addition to the infectious disease researcher’s toolbox. Yet in merely half a decade, AI has accelerated progress on some of the most urgent issues in medical science and public health. Researchers in this field blend knowledge of life sciences with skill in computation, chemistry and design, satisfying decades-long appeals for interdisciplinary tactics to treat these disorders and stop their spread.
Diseases are “infectious” when they are caused by organisms, including parasites, viruses, bacteria and fungi. People and animals can contract infectious diseases from their environments or food, or through interactions with one another. Some, but not all, are contagious.
Infectious diseases are an intractable global challenge, posing problems that continue to grow in severity even as science has offered a steady pace of solutions. The world continues to become more interconnected, bringing people into new kinds and levels of relation, and the climate crisis is throwing environmental and ecological networks out of balance. Diseases that were once treatable by drugs have become resistant, and new drug discovery is more costly than ever. Uneven resource distribution means that certain parts of the world are perennial hotspots for diseases that others never fear.
In the paper, de la Fuente and co-authors assess the progress, limitations and promise of research in AI and infectious diseases in three major areas of inquiry: anti-infective drug discovery, infection biology, and diagnostics for infectious diseases.
In a recent CNN feature, César de la Fuente, Presidential Assistant Professor in Bioengineering, Psychiatry, Microbiology, and in Chemical and Biomolecular Engineering commented on a study about a new type of antibiotic that was discovered with artificial intelligence:
“I think AI, as we’ve seen, can be applied successfully in many domains, and I think drug discovery is sort of the next frontier.”
The de la Fuente lab uses machine learning and biology to help prevent, detect, and treat infectious diseases, and is pioneering the research and discovery of new antibiotics.
They will conduct research, pursue graduate degrees, or teach English in Belgium, Brazil, Colombia, Denmark, Ecuador, Estonia, France, Germany, Guatemala, India, Israel, Latvia, Mexico, Nepal, New Zealand, the West Bank-Palestine territories, South Korea, Spain, Switzerland, Taiwan, and Thailand.
The Fulbright Program is the United States government’s flagship international educational exchange program, awarding grants to fund as long as 12 months of international experience.
Among the Penn Fulbright grant recipients for 2023-24 is Ella Atsavapranee, from Cabin John, Maryland, who graduated in May with a bachelor’s degree in bioengineering from the School of Engineering and Applied Science and a minor in chemistry from the College. She was offered a Fulbright to conduct research at the École Polytechnique Fédérale de Lausanne in Switzerland.
At Penn, Atsavapranee worked with Michael Mitchell, J. Peter and Geri Skirkanich Assistant Professor in Bioengineering, engineering lipid nanoparticles to deliver proteases that inhibit cancer cell proliferation. She has also worked with Shan Wang, Leland T. Edwards Professor in the School of Engineering and Professor of Electrical Engineering at Stanford University, using bioinformatics to discover blood biomarkers for cancer detection. To achieve more equitable health care, she worked with Lisa Shieh, Clinical Professor in Medicine at the Stanford School of Medicine, to evaluate an AI model that predicts risk of hospital readmission and study how room placement affects patient experience.
Outside of research, Atsavapranee spread awareness of ethical issues in health care and technology as editor-in-chief of the Penn Bioethics Journaland a teaching assistant for Engineering Ethics (EAS 2030). She was also a Research Peer Advisor for the Penn Center for Undergraduate Research & Fellowships (CURF), a student ambassador for the Office of Admissions, and a volunteer for Service Link, Puentes de Salud, and the Hospital of the University of Pennsylvania. She plans to pursue a career as a physician-scientist to develop and translate technologies that are more affordable and accessible to underserved populations.
Read the full list of Penn Fulbright grant recipients for 2023-24 in Penn Today.
nucleus and membrane of pathogen micro organisms in blue background
Up to 50 percent of cancer-signaling proteins once believed to be immune to drug treatments due to a lack of targetable protein regions may actually be treatable, according to a new study from the Perelman School of Medicine at the University of Pennsylvania. The findings, published this month in Nature Communications, suggest there may be new opportunities to treat cancer with new or existing drugs.
Researchers, clinicians, and pharmacologists looking to identify new ways to treat medical conditions—from cancer to autoimmune diseases—often focus on protein pockets, areas within protein structures to which certain proteins or molecules can bind. While some pockets are easily identifiable within a protein structure, others are not. Those hidden pockets, referred to as cryptic pockets, can provide new opportunities for drugs to bind to. The more pockets scientists and clinicians have to target with drugs, the more opportunities they have to control disease.
The research team identified new pockets using a Penn-designed neural network, called PocketMiner, which is artificial intelligence that predicts where cryptic pockets are likely to form from a single protein structure and learns from itself. Using PocketMiner—which was trained on simulations run on the world’s largest super computer—researchers simulated single protein structures and successfully predicted the locations of cryptic pockets in 35 cancer-related protein structures in thousands of areas of the body. These once-hidden targets, now identified, open up new approaches for potentially treating existing cancer.
What’s more, while successfully predicting the cryptic pockets, the method scientists used in this study was much faster than previous simulation or machine-learning methods. The network allows researchers to nearly instantaneously decide if a protein is likely to have cryptic pockets before investing in more expensive simulations or experiments to pursue a predicted pocket further.
“More than half of human proteins are considered undruggable due to an apparent lack of binding proteins in the snapshots we have,” said Gregory R. Bowman, PhD, a professor of Biochemistry and Biophysics and Bioengineering at Penn and the lead author of the study. “This PocketMiner research and other research like it not only predict druggable pockets in critical protein structures related to cancer but suggest most human proteins likely have druggable pockets, too. It’s a finding that offers hope to those with currently untreatable diseases.”
