Healthcare analytics, AI solutions for biological big data, providing an AI platform for the biotech, life sciences, medical and pharmaceutical industries, as well as for related technological approaches, i.e., curation and text analysis with machine learning and other activities related to AI applications to these industries.
In Memoriam: Nobel Laureate J. Michael Bishop for discovery of Retroviral Oncogenes
Reporter: Stephen J. Williams, Ph.D.
Among the many cancer researchers who have passed this year (listed in a post here), Dr. Bishop, who won the Nobel Prize with Harold Varmus for their discovery of retroviral oncogenes deserves special mention. Since Peyton Raus noticed that an infective agent could transfer oncogenic stimulus for formation of new sarcomas in chickens injected with a fractioned tumor lysate, the search for the molecular basis a viral elements could induce tumorigenesis had remained elusive for most of the 20th century. From medieval times cancer had been thought to have some infective component (and people used to think that tumors themselves were able to infect others). The identification that a virus could induce tumors in animals was a revolutionary discovery and many investigators had tried to uncover human viruses which could induce tumors as well for a large part of the 20th century.
Drs. Bishop and Varmuses work to determine the human homologue to the chicken virus led to a revolutionary discovery of how growth factor signaling and their proliferative effects could be explained on the molecular level and how such proteins, when mutated, could lead to abnormal proliferation and growth.
J. Michael Bishop, MD, a Fellow of the AACR Academy who shared the Nobel Prize for Physiology or Medicine in 1989 with Harold E. Varmus, MD, for their discovery of the cellular origin of retroviral oncogenes, died March 20, 2026, at the age of 90.
Bishop served as chancellor of the University of California, San Francisco, from 1998 until he retired in 2009. He presided over a major expansion of UCSF, a university that is devoted entirely to biomedical sciences.
Born in York, Pennsylvania, in 1936, Bishop grew up in a rural area and attended elementary school in a two-room schoolhouse. He received a bachelor’s degree in chemistry from Gettysburg College in 1958 and a medical degree from Harvard Medical School in 1962.
After a residency at Massachusetts General Hospital and research at the National Institute of Allergy and Infectious Diseases in Bethesda, Maryland, and the Heinrich Pette Institute in Hamburg, Germany, he joined the faculty of UCSF as assistant professor of microbiology and immunology in 1968. He became a full professor in 1972, and in 1981 he was named director of the university’s George F. Hooper Research Foundation.
In 1970, Varmus joined Bishop’s lab as a postdoctoral fellow. They began studying the Rous sarcoma virus to test the theory that healthy body cells contain dormant viral oncogenes that cause cancer when triggered. Bishop and Varmus discovered that the viral genes that cause cancer in humans and animals do not originate within viruses, as previously thought, but instead begin as normal genes within healthy cells that act to control cellular growth and division. This finding indicated that these benign genes (called proto-oncogenes) can be picked up by certain viruses and then transformed into oncogenes.
This discovery that cancer could be caused by the malfunction of normal genes revolutionized the understanding of tumorigenesis and greatly expanded the scope of associated scientific areas such as cancer detection as well as drug discovery and development.
In addition to receiving the Nobel Prize, Bishop’s honors included election to the National Academy of Sciences in 1980 and as an Honorary Fellow of the American Association for the Advancement of Science in 1987, and as a Foreign Member of the Royal Society. He received the Albert Lasker Award for Basic Medical Research in 1982, the Gairdner Foundation International Award and the Alfred P. Sloan, Jr., Prize from the General Motors Cancer Research Foundation in 1984, and the Dickson Prize in Medicine in 1986. He was presented with the 2003 National Medal of Science by President George W. Bush. He was a member of the inaugural class of Fellows of the AACR Academy in 2013.
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class X: +300 Audio Podcasts Library: Interviews with Scientific Leaders
Curator: Aviva Lev-Ari, PhD, RN
We had researched the topic of AI Initiatives in Big Pharma in the following article:
Authentic Relevance of LPBI Group’s Portfolio of IP as Proprietary Training Data Corpus for AI Initiatives at Big Pharma
We are publishing a Series of Five articles that demonstrate the Authentic Relevance of Five of the Ten Digital IP Asset Classes in LPBI Group’s Portfolio of IP for AI Initiatives at Big Pharma.
For the Ten IP Asset Classes in LPBI Group’s Portfolio, See
This Corpus comprises of Live Repository of Domain Knowledge Expert-Written Clinical Interpretationsof Scientific Findings codified in the following five Digital IP ASSETS CLASSES:
• IP Asset Class V: 7,500 Biological Images in our Digital Art Media Gallery, as prior art. The Media Gallery resides in WordPress.com Cloud of LPBI Group’s Web site
BECAUSE THE ABOVE ASSETS ARE DIGITAL ASSETS they are ready for use as Proprietary TRAINING DATA and INFERENCE for AI Foundation Models in HealthCare.
Expert‑curated healthcare corpus mapped to a living ontology, already packaged for immediate model ingestion and suitable for safe pre-training, evals, fine‑tuning and inference. If healthcare domain data is on your roadmap, this is a rare, defensible asset.
The article TITLE of each of the five Digital IP Asset Classes matched to AI Initiatives in Big Pharma, an article per IP Asset Class are:
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class I: PharmaceuticalIntelligence.com Journal, 2.5MM Views, 6,250 Scientific articles and Live Ontology
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class II: 48 e-Books: English Edition & Spanish Edition. 152,000 pages downloaded under pay-per-view
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class III: 100 e-Proceedings and 50 Tweet Collections of Top Biotech and Medical Global Conferences, 2013-2025
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class V: 7,500 Biological Images in LPBI Group’s Digital Art Media Gallery, as prior art
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class X: +300 Audio Podcasts Library: Interviews with Scientific Leaders
LPBI Group’s IP Asset Class X: A Library of Podcasts are a “live repository” primed for Big Pharma AI, fueling from R&D reviews to global equity. Technical Implications: Enables auditory-multimodal models for diagnostics/education. Business Implications: Accelerates $500M ROI; licensing for partnerships. Unique Insight: As unscripted leader interviews, they provide a “verbal moat” in AI—completing series’ holistic pharma data ecosystem.Promotional with links to podcast library/IP portfolio. Synthesizes series by emphasizing auditory human-AI synergy.
In the series of five articles, as above, we are presenting the key AI Initiatives in Big Pharma as it was created by our prompt to @Grok on 11/18/2025:
Generative AI tools that save scientists up to 16,000 hours annually in literature searches and data analysis.
Drug Discovery and Development Acceleration Pfizer uses AI, supercomputing, and ML to streamline R&D timelines
Clinical Trials and Regulatory Efficiency AI:
-Predictive Regulatory Tools
-Decentralize Trials
-inventory management
Disease Detection and Diagnostics:
– ATTR-CM Initiative
– Rare diseases
Generative AI and Operational Tools:
– Charlie Platform
– Scientific Data Cloud AWS powered ML on centralized data
– Amazon’s SageMaker /Bedrock for Manufacturing efficiency
– Global Health Grants:
Pfizer Foundation’s AI Learning Lab for equitable access to care and tools for community care
Partnerships and Education
– Collaborations: IMI Big Picture for 3M – sample disease database
– AI in Pharma AIPM Symposium: Drug discovery and Precision Medicine
– Webinars of AI for biomedical data integration
– Webinar on AI in Manufacturing
Strategic Focus:
– $500M R&D reinvestment by 2026 targets AI for Productivity
– Part of $7.7B cost savings
– Ethical AI, diverse DBs
– Global biotech advances: China’s AI in CRISPR
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class X: +300 Audio Podcasts Library: Interviews with Scientific Leaders
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class X: +300 Audio Podcasts Library: Interviews with Scientific Leaders
Overview: Final (fifth) in LPBI Group’s five-article series on AI-ready digital IP assets for pharma. This installment highlights IP Asset Class X—+300 audio podcasts of interviews with scientific leaders—as a proprietary, expert-curated auditory corpus for training and inference in healthcare AI models. Using a November 18, 2025, Grok prompt on Pfizer’s AI efforts, it maps the library to pharma applications, emphasizing audio ingestion for breakthroughs review, education, and platform integration. Unlike visual/text prior classes, this focuses on verbal expert insights for multimodal/hybrid AI, positioning them as a “rare, defensible” resource for ethical, diverse foundation models.Main Thesis and Key Arguments
Core Idea: LPBI’s +300 podcasts capture unscripted scientific discourse from leaders, forming a live repository of domain knowledge ideal for AI ingestion—enhancing Big Pharma’s shift from generic to human-curated models for R&D acceleration and equitable care.
Value Proposition: Part of ten IP classes (five AI-ready: I, II, III, V, X); podcasts equivalent to $50MM value in series benchmarks, with living ontology for semantic mapping. Unique for hybrid uses (e.g., education starters) and safe pre-training/fine-tuning, contrasting open-source data with proprietary, ethical inputs.
Broader Context: Caps series by adding auditory depth to text/visual assets; supports Pfizer’s $500M AI reinvestment via productivity gains (e.g., 16,000 hours saved).
AI Initiatives in Big Pharma (Focus on Pfizer) Reuses Grok prompt highlights, presented in an integrated mapping table (verbatim):
AI Initiative at Big Pharma i.e., Pfizer
Description
Generative AI tools
Save scientists up to 16,000 hours annually in literature searches and data analysis.
Drug Discovery and Development Acceleration
Pfizer uses AI, supercomputing, and ML to streamline R&D timelines.
Charlie Platform; Scientific Data Cloud AWS powered ML on centralized data; Amazon’s SageMaker/Bedrock for Manufacturing efficiency; Global Health Grants: Pfizer Foundation’s AI Learning Lab for equitable access to care and tools for community care.
Partnerships and Education
Collaborations: IMI Big Picture for 3M-sample disease database; AI in Pharma AIPM Symposium: Drug discovery and Precision Medicine; Webinars of AI for biomedical data integration; Webinar on AI in Manufacturing.
Strategic Focus
$500M R&D reinvestment by 2026 targets AI for Productivity; Part of $7.7B cost savings; Ethical AI, diverse DBs; Global biotech advances: China’s AI in CRISPR.
Mapping to LPBI’s Proprietary DataCore alignment table (verbatim extraction, linking Pfizer initiatives to Class X podcasts):
AI Initiative at Big Pharma i.e., Pfizer
Library of Audio and Video Podcasts N = +300
Generative AI tools (16,000 hours saved)
(No specific mapping provided.)
Drug Discovery and Development Acceleration
Review ALL SCIENTIFIC BREAKTHROUGHS.
Clinical Trials and Regulatory Efficiency
(No specific mapping provided.)
Disease Detection and Diagnostics (ATTR-CM, rare diseases)
(No specific mapping provided.)
Generative AI and Operational Tools (Charlie, AWS, etc.)
Ingest to Charlie Platform all +300 Podcasts.
Partnerships and Education (IMI, AIPM, webinars)
Use Podcast for Education; Use Podcast as Hybrid: Start presentation with a Podcast continue with a life interview.
Strategic Focus ($500M reinvestment, ethics)
(No specific mapping provided.)
Methodologies and Frameworks
AI Training Pipeline: Ingest audio into Charlie/AWS for pre-training (expert discourse corpus), fine-tuning (e.g., breakthrough reviews), and inference (e.g., educational hybrids). Use ontology for thematic mapping; Grok prompt for sourcing initiatives.
Productivity Model: Boosts Pfizer’s $7.7B savings via auditory data for diverse, ethical AI (e.g., Learning Lab grants); hybrid formats for webinars/symposia.
Insights: Quote from Dr. Stephen J. Williams, PhD: Assets as “codified digital treasures” for safe healthcare AI. Predicts revolution in verbal inference for precision medicine.
Conclusions and Implications
LPBI Group’s IP Asset Class X:
A Library of Podcasts are a “live repository” primed for Big Pharma AI, fueling from R&D reviews to global equity. Technical Implications: Enables auditory-multimodal models for diagnostics/education. Business Implications: Accelerates $500M ROI; licensing for partnerships. Unique Insight: As unscripted leader interviews, they provide a “verbal moat” in AI—completing series’ holistic pharma data ecosystem.Promotional with links to podcast library/IP portfolio. Synthesizes series by emphasizing auditory human-AI synergy.
@Grok was prompted to summarize the Table of Contents of the e-Book that present the Library of Podcast INDEX.
Overview of IP Asset Class X: +300 Audio Podcasts – Interviews with Scientific LeadersThe BioMed Audio Podcast Library from LPBI Group is a valuable intellectual property asset in the biomedical field, featuring over 301 audio podcasts created from interviews, curations, and discussions with prominent scientific leaders. This collection highlights breakthroughs in areas like genomics, cancer research, immunology, precision medicine, and public health policy. Curated by experts such as Aviva Lev-Ari, PhD, RN (197 podcasts), Larry H. Bernstein, MD, FCAP (48 podcasts), and Stephen J. Williams, PhD (38 podcasts), the library is organized into 21 thematic chapters for easy navigation.Key features include:
Search and Accessibility: Filter by chapter, curator, subject, or podcast ID. Each entry includes text-to-speech conversion and NLP-generated WordClouds for topic visualization.
Content Focus: Emphasizes Nobel laureates, key opinion leaders, and innovators discussing technologies like CRISPR-Cas9, mRNA vaccines, immunotherapy, and biotechnology ventures.
Format and Updates: Derived from articles on real-time events (e.g., COVID-19 impacts, award announcements). The library continues to expand, with no direct audio embeds—access via linked articles for full transcripts and playback.
Themes Covered: Public health policy, cardiovascular science, neuroscience, academic institutions, and more, with a strong emphasis on translational research and personalized medicine.
This asset represents a rich repository for researchers, students, and professionals seeking insights from leaders like Francis Collins, Jennifer Doudna, and Siddhartha Mukherjee.Selected Highlights by ChapterBelow are curated examples from key chapters, showcasing interviews with scientific leaders. For the full library (301+ entries), visit the source page.
Chapter 1: Public Health
Podcast ID
Curator
Title
Scientific Leader(s)
Brief Description
Link
17
Aviva Lev-Ari
LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2
Leaders in genome sequencing
Explores genetic mutations’ role in personalized cancer therapies.
This selection captures the library’s depth, blending historical perspectives (e.g., Watson) with cutting-edge topics (e.g., CRISPR, immunotherapy). For deeper dives, use the site’s search tools to explore chapters like Cardiovascular Science (36 podcasts) or Immunology.
In Memoriam: Nobel Laureate James D. Watson, Ph.D. (1928-2025)
Curator: Stephen J. Williams, Ph.D.
On Thursday November 6, 2025, Nobel Laureate Dr. James D. Watson passed away after a reported brief illness. Although well known for his discovery of the DNA double helix with Francis Crick, Maurice Wilkens using the crystallographic data of Rosalind Franklin, Dr. Watson had contributed other seminal findings to the fields of biology and cancer, as well as his mentoring of young scientists. Therefore it is only fitting to curate some of the commentary on his life and passing in the words of the institutions and the renowned scientists he had mentored.
The world of science bids farewell to one of its most brilliant and controversial figures, Dr. James Dewey Watson, who passed away on 6th November 2025 at the age of 97. Best known as one of the co-discoverers of the double-helix structure of DNA, Watson’s name became synonymous with a new era in genetics and molecular biology. His life, filled with intellectual daring, unyielding curiosity, and deep contributions to science and education, forever altered humanity’s understanding of the genetic code that defines life itself.
James Watson and Francis Crick with model of DNA double helix. The model was based on data from Rosalind Franklin and x ray diffraction analysis of Maurice Wilkins.
From Cold Spring Harbor Laboratory, where Dr. Watson spent most of his scientific career:
Jim Watson made many contributions to science, education, public service, and especially Cold Spring Harbor Laboratory (CSHL).
As a scientist, his and Francis Crick’s determination of the structure of DNA, based on data from Rosalind Franklin, Maurice Wilkins and their colleagues at King’s College London, was a pivotal moment in the life sciences. Watson, along with Crick and Wilkins were awarded the 1962 Nobel Prize in Physiology or Medicine. Watson also received the Presidential Medal of Freedom from President Gerald Ford and the National Medal of Science from President Bill Clinton, among many other awards and prizes. While at Cambridge, Watson also carried out pioneering research on the structure of small viruses. At Harvard, Watson’s laboratory demonstrated the existence of mRNA, in parallel with a group at Cambridge, UK, led by Sydney Brenner. His laboratory also discovered important bacterial proteins that control gene expression and contributed to understanding how mRNA is translated into proteins.
As an author, Watson wrote two books at Harvard that were and remain best sellers. The textbook Molecular Biology of the Gene, published in 1965 (7th edition, 2020), changed the nature of science textbooks, and its style was widely emulated. The Double Helix (1968) was a sensation at the time of publication. Watson’s account of the events that resulted in the elucidation of the structure of DNA remains controversial, but still widely read.
As a public servant, Watson successfully guided the first years of the Human Genome Project, persuading scientists to take part and politicians to provide funding. He created the Ethical, Legal and Social Issues (ELSI) program because of his concerns about misuse of the fruits of the project.
Watson’s association with Cold Spring Harbor Laboratory began in 1947 when he came as a graduate student with his supervisor, Salvador Luria. Luria, with Max Delbruck, was teaching the legendary Phage Course. Watson returned repeatedly to CSHL, most notably in 1953 when he gave the first public presentation of the DNA double helix at that year’s annual Symposium. He became a CSHL trustee in 1965.
CSHL was created in 1964 by the merger of two institutes that existed in Cold Spring Harbor since 1890 and 1902, respectively. In 1968, Watson became the second director when he was 40 years old. John Cairns, the first director, had begun to revive the institute but it was still not far short of being destitute when Watson took charge. He immediately showed his great skills in choosing important topics for research, selecting scientists and raising funds.
Also in 1968, Watson married Elizabeth (Liz) Lewis, and they have lived on the CSHL campus their entire lives together. Jim and Liz have two sons, Rufus and Duncan. As with the former Directors, they fostered close relationships with the local Cold Spring Harbor community.
In 1969, Watson focused research at CSHL on cancer, specifically on DNA viruses that cause cancer. The study of these viruses resulted in many fundamental discoveries of important biological processes, including the Nobel prize-winning discovery of RNA splicing. Watson was the first Director of CSHL’s National Cancer Institute-designated Cancer Center, which remains today.
Watson was passionate about science education and promoting research through meetings and courses. Meetings began at CSHL in 1933 with the Symposium series, and the modern advanced courses started with the Phage course in 1945. Watson greatly expanded both programs, making CSHL the leading venue for learning the latest research in the life sciences. Publishing also increased, notably of laboratory manuals, epitomized by Molecular Cloning, and several journals began, led by Genes & Development and later Genome Research. He encouraged the creation of the DNA Learning Center, unique in providing hands-on genetic education for high-school students. There are now DNA Learning Centers throughout the world.
Through a substantial gift to CSHL in 1973 by Charles Robertson, Watson started the Banbury Center on the Robertsons’ 54-acre estate in nearby Lloyd Harbor. Today, this center functions as an important “think tank” for advancing research and policies on many issues related to life and medical sciences.
From the American Association for Cancer Research (AACR) and contributions to cancer research
James D. Watson, PhD
Cold Spring Harbor Laboratory
Cold Spring Harbor, New York
Class of 2013
A renowned molecular biologist, teacher, and author, Dr. Watson is best known as the co-discoverer of the double-helix structure of DNA, for which he won the 1962 Nobel Prize in Physiology or Medicine. First announced in early April 1953 by the director of the Cavendish Laboratory in Cambridge, the discovery went largely unnoticed until a paper reporting it appeared in the April 25, 1953, issue of Nature. Prominent biologists later described the finding as the most important scientific discovery of the 20th century.
Dr. Watson headed the Human Genome Project at the National Institutes of Health from 1990 to 1992. In 2007, he became the second person to publish his personal fully sequenced genome online. Ahead of his time as usual, he said he did so to “encourage the development of an era of personalized medicine”, in which information contained in our genomes can be used to identify and prevent disease and to create individualized medical therapies. – He has written several highly regarded molecular biology textbooks and in 1968 published a personal account in The Double Helix, which became one of Modern Library ‘s 100 Best Nonfiction Books.
Career Highlights
2001 Benjamin Franklin Medal for Distinguished Achievement in the Sciences
2000 The Liberty Medal, National Constitution Center
1999 Honorary Member, AACR
1997 National Medal of Science, National Science Foundation
1994-2004 President, Cold Spring Harbor Laboratory
1993 Copley Medal of the Royal Society of London
1988-1992 Director, Human Genome Project, NIH
1971 John J. Carty Award in Molecular Biology, National Academy of Sciences
1975 Elected Fellow, American Academy of Arts and Sciences
2002 Gairdner Foundation International Award
1962 Nobel Prize in Physiology or Medicine
1960 Albert Lasker Award for Basic Medical Research
1959 Eli Lilly Award in Biological Chemistry
1959 John Collins Warren Prize, Massachusetts General Hospital
1950 PhD, Indiana University, Bloomington
In the Words of Colleagues who Worked With Dr. James Watson
Philip Sharp
Molecular biologist Phillip Allen Sharp received the 1993 Nobel Prize in physiology or medicine for his discovery of splicing of introns and exons or “split genes.” He found that these genes are the most common type of gene structure in higher organisms, including humans. He shared the prize with Richard John Roberts, who discovered split genes independently of Sharp. The discovery of split genes has been of fundamental importance to basic research in biology as well as medical research on the development of cancer and other diseases. The discovery of split genes led to the prediction of the genetic process of splicing.
Here is a great interview with Nobel Laureate Dr. Philip Sharp and working with Jim Watson at Cold Spring Harbor Labs
Watch Video
These are the parts of the transcript he talk about working with Jim Watson. Note he also seeked out David Baltimore to do a postdoctoral fellowship at MIT on viruses.
Transcript:
Sharp: So I also wanted to begin to work with human cells. And I wanted to work with viruses that infected human cells, because, again, I could isolate their DNA. And I could understand that DNA. And I got that experience from working with Jerry Vinograd at Caltech, who was also a professor there. And I collaborated with him and Norman once while I was there. So I wanted to learn virology. And I contacted three labs to do a second postdoc for a period of time. Dave Baltimore, who was here at MIT, Howard Temin up at Wisconsin, and Jim Watson at Cold Spring Harbor. And Jim invited me to come to Cold Spring Harbor. I moved there to start working with animal viruses. He had just come down from Harvard to take over Cold Spring Harbor and was expanding the tumor virus program there.So I joined that program and started to work with mammalian cells and DNA tumor viruses that cause tumors in animals. But to me they were a tool as well to begin to look at gene structure and function in the human cells.
INTERVIEWER: So as a humanist, for lack of a better word, you were interested on some level in the potential for the curative powers of biology by studying viruses; but as a chemist you saw viruses as this platform, a window, into the structure of DNA.
SHARP: That’s right, and the structure of cells. How the complex human cell worked. Because in the early 1970s, we really didn’t have the tools to begin to understand the biology, molecular biology, or cell biology of human cells. It was really a totally unexplored at the level of a gene and how it functioned. And I saw this as a chemist as a tool that I could move into that question. And I knew that question was central to human biology. I mean, you can’t understand the biology of an organism without understanding the gene. So it seemed pretty apparent to me. It’s sort of written on the wall, understand what the gene is. And so I, you know, had multiple reasons to begin these studies. Some was, you know, how cancer developed. Others were fundamental. What was a gene.
INTERVIEWER: Most people who’ve understood James Watson by reputation at the time that you went to study with him viewed him as a towering pillar of science who had answered an enormously important question in biology for all time. But when you went to study with him, you were, in fact, seeing it from the other side, that, in fact, Watson’s work was just the beginning of an extremely long journey that we’re still on. How did he understand that we were at the beginning of something, versus how you understood it. And how did that work in your relationship?
SHARP: Jim at that stage, you know, he had done so much. He had discovered the structure of DNA. He’d built the Department of Molecular Biology and Biochemistry at Harvard, the most outstanding department in the country focused on that. Written his text book, The Molecular Biology of the Gene, which was the introduction to students of this fascinating field. And took over Cold Spring Harbor and resurrected from a lab that was not going to survive much longer. He constructed, he understood that DNA was a critical tool in understanding complex biology. And that this subject would lead to increasing insights. He obviously had a much greater vision of all the relationships of, you know, different parts of biology to these questions than I did. And he gathered around him very bright, energetic, interesting people. And he’s sort of chit chatted at the top, left him alone. And when he found something that was interesting that happened in that mix, he would sort of pluck it out and say, “nice work”, you know. “Write that up. Tell other people about that.” And so he played that sort of, you know, very senior mentor and creator of a community. And in that community, I found some really wonderful people, very talented people. Joe Sambrook who I collaborated with. And Ulf Pettersson and Mike Botchan and a whole host of others who are now all leaders around the world. So it was just a very stimulating environment.
INTERVIEWER: Again, this sense of a team of people working at the top of their game, focused in any way they can, using all the disciplines of knowledge at their disposal on the problems that excite them.
SHARP: That’s true, and a team in which there are different disciplines. Jim understood this, that he needed someone with more physical chemistry; and he needed someone with chemistry. And he needed a biologist. And he needed this biochemist. And he sort of, you know, mixed people that would complement one another. And I was the individual who came in with a broad interest in biology, new and physical chemistry, new electron microscopy. And there was a lot of people in the environment that were virologists and cell biologists who needed this sort of tools to do their science. So we complemented each other and stimulated each other.
Sir Richard John Roberts, Ph.D.
Sir Richard John Roberts was co-awarded with Philip Sharp the 1993 Nobel Prize in Physiology or Medicine for their discovery of RNA splicing. They both worked at Cold Spring Harbor Laboratories. Dr. Roberts also discovered numerous restriction enzymes which he used to develop DNA sequencing of complex genomes. He also co-founded New England BioLabs. Below is an interesting interview of his quick hiring interview with Jim Watson and his time at Cold Spring Harbor Labs.
Other Notable Scientists Who Have been Mentored and interacted with Dr. Watson
Antonio Giordano, M.D., Ph,D.
Dr. Giordano is the President and Founder of the Sbarro Health Research Organization and Professor in Biology at Temple University and ‘chiara fama’ Professor of Anatomic Pathology in the Department of Medical Biotechnology at the University of Siena, in Siena, Italy. He discovered the tumor suppressor RBL2/p130 and showed its alteration in multiple tumor types, showing the first molecular evidence that causually linked proliferation and cancer. In addition he has discovered cyclin dependent kinases CDK9 and CDK10, as well as other regulators and development of new classes of inhibitors of the cell cycle.
Dr. Antonio Giordano with his mentor and colleague Dr. James Watson. Dr. James Dewey Watson discovered the structure of the DNA molecule with Francis Crick and Maurice Wilkens, whom he also received the Nobel Prize for. On the left is a signed copy to Dr. Giordano of Watson’s book the Double Helix.
Other articles of relevance on James Watson and the DNA Helix on this Open Access Journal include:
David Baltimore, PhD, FAACR, a Fellow of the AACR Academy and a towering figure in modern biology whose insights reshaped cancer research and biomedical science, died on September 6, 2025, at the age of 87.
Baltimore’s career was defined by transformative discoveries. In 1975, he was awarded the Nobel Prize in Physiology or Medicine, alongside Renato Dulbecco and Howard Temin, for elucidating how tumor viruses interact with the genetic material of the cell. His discovery of reverse transcriptase overturned one of the central dogmas of molecular biology by showing that genetic information could flow from RNA back to DNA. This single revelation opened countless new frontiers in virology, immunology, oncology, and genetics, laying the foundation for decades of scientific advances influencing the fundamental understanding of retroviruses such as HIV, and driving the development of modern gene therapies and mRNA-based technologies.
Following his groundbreaking work in virology, Baltimore expanded his focus to the immune system, pioneering research on how mammalian immunity can be harnessed to combat cancer. His quintessential vision and curiosity fueled entire fields of inquiry, and his scholarship bridged basic science with clinical potential.
Born in New York City in 1938, Baltimore earned his undergraduate degree from Swarthmore College and a doctorate from Rockefeller University in 1964. His early independent research at the Massachusetts Institute of Technology (MIT) and the Salk Institute quickly established him as one of the most original scientific thinkers of his generation. At just 30 years old, he became an associate professor at MIT, where he would spend much of his career shaping both science and the careers of a plethora of researchers who would subsequently establish themselves as leaders in the global cancer research community.
Baltimore served in distinguished leadership roles throughout his storied career, including as president of Rockefeller University and later of the California Institute of Technology (Caltech), where he guided the institution through a decade of growth and scientific excellence. At Caltech, he held the Robert Andrews Millikan Professorship of Biology, and later the Judge Shirley Hufstedler Professorship of Biology, titles that underscored his standing as both a scientist and mentor with an enduring legacy.
Beyond the laboratory and university walls, Baltimore’s voice carried weight in national and international science policy forums. He was a leading advocate for federal investment in AIDS research, co-chaired the National Academy of Sciences Committee on a National Strategy for AIDS in 1986, and led the NIH AIDS Vaccine Research Committee a decade later. He also played an active role in shaping consensus guidelines on genetic engineering, thereby ensuring that scientific innovation proceeded with ethical responsibility.
Throughout his lifetime, Baltimore received innumerable honors, including election to the National Academy of Sciences, the Institute of Medicine, and the American Academy of Arts and Sciences. He was recognized with the National Medal of Science, the AMA Scientific Achievement Award, and the Lasker-Koshland Special Achievement Award in Medical Science. He also served as president of the American Association for the Advancement of Science and was elected to the inaugural class of Fellows of the AACR Academy in 2013.
Perhaps as significant as his discoveries, was Baltimore’s role as a mentor. He trained and inspired generations of scientists who themselves went on to make landmark contributions in cancer biology, immunology, and virology. Many of his mentees later achieved the highest levels of recognition in the field, including election as Fellows of the AACR Academy. His intellectual generosity and willingness to champion young investigators created a legacy of discovery that continues to reverberate to this day and will help to advance future researchers in the years to come.
David Baltimore’s life was one of restless inquiry, bold imagination, and unwavering dedication to science. His revolutionary discoveries continue to transform cancer medicine and deepen our understanding of life itself. The cancer research community—and indeed, all of biomedical science—mourns the loss of one of its most visionary and impactful leaders.
Dr. El-Diery welcomes all to this joint symposium with Advancing Precision Medicine and the Win Consortium, which he is currently the head of. More in the WIN Consortium: (Worldwide Innovation Network Consortium in Precision Oncology). The WIN Network is involved in setting up internationalclinical tumor board collaboration
WIN was formed on the premise that we can accomplish more together than each organization can achieve working alone. We aim to improve cancer patients’ survival and quality of life. View WIN’s history and unique attributes:
WIN members collaboratively design and carry out global studies designed to achieve breakthroughs for patients worldwide. Our distinguished Scientific Advisory Board oversees WIN studies. Current trials include:
They guide WIN’s strategic, operational, and scientific direction.
OrganizationThe WIN Consortium is organized in the following groups who collectively work together to achieve WIN’s common goals
Nigel Russell, Founder and CEO, Advancing Precision Medicine
Christopher P. Molineaux, President & Chief Executive Officer, Life Science Pennsylvania
Life Sciences Pennsylvania (LSPA) is the statewide trade association for the commonwealth’s life sciences industry. Founded in 1989, LSPA works to ensure Pennsylvania has a business and public policy climate that makes the commonwealth the most attractive location to open and operate a life sciences company. Our membership is comprised of organizations statewide, representing the entire ecosystem of the life sciences: research institutions, biotechnology, medical device, diagnostic, pharmaceutical, and investment entities, along with service providers who support the industry. Together, we unify Pennsylvania’s innovators to make the Commonwealth a global life sciences leader.
As president & CEO of Life Sciences Pennsylvania, Christopher Molineaux serves as the chief advocate and spokesman for the life sciences industry that calls Pennsylvania home. Molineaux oversees the strategic direction for the association, assuring Life Sciences Pennsylvania continues to be the catalyst that makes Pennsylvania the top location for life sciences companies.
Molineaux brings to Life Sciences Pennsylvania more than 25 years of experience in the bio-pharmaceutical and health care industries, with front-line experience in developing and executing strategies to navigate a shifting economic and political environment.
9:00-9:40
Keynote Lecture – WIN Consortium
Targeting the Achilles’ Heel of Cancer: Synthetic Lethality and Hypoxia in Precision Oncology
William Kaelin was born in New York City. He studied chemistry and mathematics at Duke University in Durham, North Carolina, and received his doctor of medicine degree there in 1982. He then did his residency at Johns Hopkins University in Baltimore, Maryland. In 2002 he became a professor at Harvard Medical School in Cambridge, Massachusetts.
Work
Animals need oxygen for the conversion of food into useful energy. The importance of oxygen has been understood for centuries, but how cells adapt to changes in levels of oxygen has long been unknown. William Kaelin, Peter Ratcliffe, and Gregg Semenza discovered how cells can sense and adapt to changing oxygen availability. During the 1990s they identified a molecular machinery that regulates the activity of genes in response to varying levels of oxygen. The discoveries may lead to new treatments of anemia, cancer and many other diseases.
TRACK 1 204BC
WIN SYMPOSIUM
MULTI-OMICS
9:40 – 10:40
SESSION 1
From Base Pairs To Better Care:
AI and Omics in Precision Oncology
9:40-10:00
Multi-Omic Profiling and Clinical Decision Support in Precision Oncology
David Spetzler, PhD, MBA, MS, President, Caris Life Sciences
10:00-10:20
Integrating Omics and AI for Next-Gen Precision Oncology
Keith T. Flaherty, MD, FAACR, Director of Clinical Research,Massachusetts General Cancer Center; Professor of Medicine, Harvard Medical School; President-Elect: 2025-2026, American Association for Cancer Research (AACR)
10:20-10:40
Real-World Data and AI in Precision Oncology: Making Data Work for Patients – Q&A
MODERATOR: Jeff Elton, PhD, Vice Chairman, Founding CEO
ConcertAI
PANELISTS: David Spetzler, PhD, MBA, MS, President, Caris Life Sciences
Keith T. Flaherty, MD, FAACR, Director of Clinical Research,Massachusetts General Cancer Center; Professor of Medicine, Harvard Medical School; President-Elect: 2025-2026, American Association for Cancer Research (AACR)
Daryl Pritchard, PhD, Interim President, Personalized Medicine Coalition
Keith T. Flaherty, MD, FAACR, Director of Clinical Research,Massachusetts General Cancer Center; Professor of Medicine, Harvard Medical School; President-Elect: 2025-2026, American Association for Cancer Research (AACR)
SESSION 3
The Shifting Landscape:
Tumor Plasticity and Resistance
12:00-12:20
Mathematical and Evolutionary Modeling in Precision Radiation Oncology
Jacob Scott, MD, DPhil, Professor and Staff Physician-Scientist, CWRU School of Medicine and Cleveland Clinic
12:20-12:40
Plasticity and Persistence: The Role of EMT in Cancer Progression and Therapy Resistance
Sendurai A. Mani, PhD, Professor of Pathology and Laboratory Medicine, Brown University; Associate Director of Translational Oncology, Brown University Legorreta Cancer Center
12:40-1:00
Targeting Molecularly Defined Subsets: Challenges in Translational Oncology
Benedito A. Carneiro, MD, MS, Director, Clinical Research Director, Cancer Drug Development; Associate Director, Division of Hematology/Oncology
Legorreta Cancer Center, Brown University Health
Nobel Prize in Chemistry 2024 to David Baker, Demis Hassabis and John M. Jumper
Reporter: Aviva Lev-Ari, PhD, RN
UPDATED on 10/22/2024
ProteinMPNN, which is now available free on the open-source software repository GitHub, will give researchers the tools to make unlimited new designs. “The challenge, of course … is what are you going to design?” Baker says.
In a second Nobel win for AI, the Royal Swedish Academy of Sciences has awarded half the 2024 prize in chemistry to Demis Hassabis, the cofounder and CEO of Google DeepMind, and John M. Jumper, a director at the same company, for their work on using artificial intelligence to predict the structures of proteins. The other half goes to David Baker, a professor of biochemistry at the University of Washington, for his work on computational protein design. The winners will share a prize pot of 11 million Swedish kronor ($1 million).
The potential impact of this research is enormous. Proteins are fundamental to life, but understanding what they do involves figuring out their structure—a very hard puzzle that once took months or years to crack for each type of protein. By cutting down the time it takes to predict a protein’s structure, computational tools such as those developed by this year’s award winners are helping scientists gain a greater understanding of how proteins work and opening up new avenues of research and drug development. The technology could unlock more efficient vaccines, speed up research on cures for cancer, or lead to completely new materials.
Hassabis and Jumper created AlphaFold, which in 2020 solved a problem scientists have been wrestling with for decades: predicting the three-dimensional structure of a protein from a sequence of amino acids. The AI tool has since been used to predict the shapes of all proteins known to science.
“I’ve dedicated my career to advancing AI because of its unparalleled potential to improve the lives of billions of people,” said Demis Hassabis. “AlphaFold has already been used by more than two million researchers to advance critical work, from enzyme design to drug discovery. I hope we’ll look back on AlphaFold as the first proof point of AI’s incredible potential to accelerate scientific discovery,” he added.
Baker has created several AI tools for designing and predicting the structure of proteins, such as a family of programs called Rosetta. In 2022, his lab created an open-source AI tool called ProteinMPNN that could help researchers discover previously unknown proteins and design entirely new ones. It helps researchers who have an exact protein structure in mind find amino acid sequences that fold into that shape.
Most recently, in late September, Baker’s lab announced it had developed custom molecules that allow scientists to precisely target and eliminate proteins associated with diseases in living cells.
“[Proteins] evolved over the course of evolution to solve the problems that organisms faced during evolution. But we face new problems today, like covid. If we could design proteins that were as good at solving new problems as the ones that evolved during evolution are at solving old problems, it would be really, really powerful,” Baker told MIT Technology Review in 2022.
born 1962 in Seattle, WA, USA. PhD 1989 from University of California, Berkeley, CA, USA. Professor at University of Washington, Seattle, WA, USA and Investigator, Howard Hughes Medical Institute, USA.
University of Washington, Seattle, WA, USA
Howard Hughes Medical Institute, USA
Demis Hassabis “for protein structure prediction”
born 1976 in London, UK. PhD 2009 from University College London, UK. CEO of Google DeepMind, London, UK.
Google DeepMind, London, UK
John M. Jumper “for protein structure prediction”
born 1985 in Little Rock, AR, USA. PhD 2017 from University of Chicago, IL, USA. Senior Research Scientist at Google DeepMind, London, UK.
Google DeepMind, London, UK
The Nobel Prize in Chemistry 2024 is about proteins, life’s ingenious chemical tools. David Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins. Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures. These discoveries hold enormous potential.
“One of the discoveries being recognised this year concerns the construction of spectacular proteins. The other is about fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences. Both of these discoveries open up vast possibilities,” says Heiner Linke, Chair of the Nobel Committee for Chemistry.
Proteins generally consist of 20 different amino acids, which can be described as life’s building blocks. In 2003, David Baker succeeded in using these blocks to design a new protein that was unlike any other protein. Since then, his research group has produced one imaginative protein creation after another, including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors.
The second discovery concerns the prediction of protein structures. In proteins, amino acids are linked together in long strings that fold up to make a three-dimensional structure, which is decisive for the protein’s function. Since the 1970s, researchers had tried to predict protein structures from amino acid sequences, but this was notoriously difficult. However, four years ago, there was a stunning breakthrough.
In 2020, Demis Hassabis and John Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.
Life could not exist without proteins. That we can now predict protein structures and design our own proteins confers the greatest benefit to humankind.
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This year’s Nobel Prize laureates in chemistry Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures.
In 2020, Hassabis and Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.
2024 Nobel Prize in Physiology or Medicine jointly to Victor Ambros and Gary Ruvkun for the discovery of microRNA and its role in post-transcriptional gene regulation
Reporter: Aviva Lev-Ari, PhD, RN
Updated 10/22/2024
The revolution in our understanding of transcriptional regulation and dark regions of the genome
The genome of higher eukaryotes are comprised of multiple exonic and intronic regions, with coding and noncoding DNA respectively. Much of the DNA sequence between exonic regions of genes, the sequences encoding the amino acids of a polypeptide, was considered either promoter regions regulating an exonic sequence or ‘junk DNA’, which had merely separated exons and their regulatory elements. It was not considered that this dark DNA or junk DNA was important in regulating transcription of genes. It was felt that most gene regulation occurred in promoter regions by response element factors which bound to specific sequences within these regions.
MicroRNA (miRNA), originally discovered in Caenorhabditis elegans, is found in most eukaryotes, including humans [1–3]. It is predicted that miRNA account for 1-5% of the human genome and regulate at least 30% of protein-coding genes [4–8]. To date, 940 distinct miRNAs molecules have been identified within the human genome [9–12] (http://microrna.sanger.ac.uk accessed July 20, 2010). Although little is currently known about the specific targets and biological functions of miRNA molecules thus far, it is evident that miRNA plays a crucial role in the regulation of gene expression controlling diverse cellular and metabolic pathways.
MiRNA are small, evolutionary conserved, single-stranded, non-coding RNA molecules that bind target mRNA to prevent protein production by one of two distinct mechanisms. Mature miRNA is generated through two-step cleavage of primary miRNA (pri-miRNA), which incorporates into the effector complex RNA-induced silencing complex (RISC). The miRNA functions as a guide by base-pairing with target mRNA to negatively regulate its expression. The level of complementarity between the guide and mRNA target determines which silencing mechanism will be employed; cleavage of target messenger RNA (mRNA) with subsequent degradation or translation inhibition
Fig. (1). MicroRNA maturation and function.
Figure. miRNA maturation and function. Source: Macfarlane LA, Murphy PR. MicroRNA: Biogenesis, Function and Role in Cancer. Curr Genomics. 2010 Nov;11(7):537-61. doi: 10.2174/138920210793175895.
The following is an interview in the journal Journal of Cellular Biology with Dr, Victor Ambros on his discovery of miRNA.
Source: Ambros V. Victor Ambros: the broad scope of microRNAs. Interview by Caitlin Sedwick. J Cell Biol. 2013 May 13;201(4):492-3. doi: 10.1083/jcb.2014pi. PMID: 23671307; PMCID: PMC3653358.
Once, we thought we understood all there was to know about how gene expression is regulated: A cell can tinker with the expression level of a given protein’s messenger RNA by modifying the activity, abundance, and type of transcription factors in the nucleus or with the RNA’s stability once it is made. But then came a surprising story about a short RNA in C. elegans called lin-4, which didn’t encode a protein but prevented expression of the protein encoded by another gene, lin-14, through antisense binding to lin-14 mRNA (1, 2). Today, we know that lin-4 was just the first example of a large number of small RNAs, called microRNAs, which regulate the expression of various other proteins in a similar way.
Victor Ambros, whose lab published that first story about lin-4, has been studying microRNAs (3, 4) and their regulation (5, 6) ever since, pushing forward our understanding of this powerful mechanism. We called him at his office at the University of Massachusetts Medical School to get some perspective on microRNAs and his career and to learn about some of the latest developments in his lab.
“That shared discovery is one of the most precious moments in my career.”
FROM FARM TO LAB TABLE
How did you end up doing a PhD with David Baltimore?
I was the first scientist in my family. My dad was an immigrant from Poland. He came to the States just after World War II and met my mom. They got married, moved to a farm in Vermont, and started farming. My siblings and I grew up amongst the cows and pigs and helped with the haying and cutting corn, stuff like that.
When I was about nine, I got interested in science, and after that I always wanted to be a scientist. I was an amateur astronomer; I built a telescope and started to imagine that I could actually do astronomy or physics as an occupation. But I quickly changed my mind when I reached college, in part because I realized that my math skills weren’t really up to the task of being a physicist and also because I discovered molecular biology and genetics and just fell in love with both subjects. David taught one of the advanced biology classes I took as an undergraduate at MIT, and that probably had some influence on my decision to work with him. After college, I worked as a technician in David’s lab for a year. I liked it a lot and stayed on in his lab when I entered graduate school at MIT. I was lucky because I had gotten a little bit of traction on a project and continued on that as a grad student, so I ended up finishing grad school fairly efficiently.
Had you any idea at the time what the nature of the lin-4 mutant was?
The assumption was that it was a protein product. I mean, nobody ever thought that there would be any other kind of regulator. There really wasn’t any reason to imagine that there were any other kinds of molecules necessary, other than proteins, to carry out everything that’s done in a cell—especially with regard to the regulation of gene expression. The complexity of gene regulation by proteins alone was so enormous that I never imagined—and nobody I knew imagined—that we needed to look for new kinds of regulatory molecules. The realization that lin-4 was antisense to the 3′-untranslated region of lin-14 was totally the result of communication between Gary and me. That shared discovery is one of the most precious moments in my career. But at the time I didn’t realize that this might be the first example of a general mechanism for regulating gene expression because I was prone to thinking that whatever I was studying in the worm was not generally applicable. It wasn’t until genome sequences were made available that the prevalence of this mechanism became clear.
THE RIGHT CONTEXT
You’ve moved to studying processes that modulate microRNA function…
One protein we’ve studied is called Nhl-2. It’s an example of an emerging class of proteins that can modulate, positively or negatively, the RNA-induced silencing complex (RISC) that inhibits mRNAs targeted by microRNAs. This class of genes may have either general effects on RISC activity or, in some cases, more specific effects. One area of interest in the lab right now is trying to understand the specific outcomes for the regulation of particular microRNAs. Do they always interact with all their targets, or is their activity on some targets promoted or inhibited at the expense of other targets? Can their interaction with certain targets be modified depending on context? We’re using genetic and genomic approaches to identify new modulatory cofactors.
Watch Video
Victor Ambros was born in 1953 in Hanover, New Hampshire, USA. He received his PhD from Massachusetts Institute of Technology (MIT), Cambridge, MA, in 1979 where he also did postdoctoral research 1979-1985. He became a Principal Investigator at Harvard University, Cambridge, MA in 1985. He was Professor at Dartmouth Medical School from 1992-2007 and he is now Silverman Professor of Natural Science at the University of Massachusetts Medical School, Worcester, MA.
Gary Ruvkun was born in Berkeley, California, USA in 1952. He received his PhD from Harvard University in 1982. He was a postdoctoral fellow at Massachusetts Institute of Technology (MIT), Cambridge, MA, 1982-1985. He became a Principal Investigator at Massachusetts General Hospital and Harvard Medical School in 1985, where he is now Professor of Genetics.
This year’s Nobel Prize honors two scientists for their discovery of a fundamental principle governing how gene activity is regulated.
The information stored within our chromosomes can be likened to an instruction manual for all cells in our body. Every cell contains the same chromosomes, so every cell contains exactly the same set of genes and exactly the same set of instructions. Yet, different cell types, such as muscle and nerve cells, have very distinct characteristics. How do these differences arise? The answer lies in gene regulation, which allows each cell to select only the relevant instructions. This ensures that only the correct set of genes is active in each cell type.
Victor Ambros and Gary Ruvkun were interested in how different cell types develop. They discovered microRNA, a new class of tiny RNA molecules that play a crucial role in gene regulation. Their groundbreaking discovery revealed a completely new principle of gene regulation that turned out to be essential for multicellular organisms, including humans. It is now known that the human genome codes for over one thousand microRNAs. Their surprising discovery revealed an entirely new dimension to gene regulation. MicroRNAs are proving to be fundamentally important for how organisms develop and function.
Ambros and Ruvkun were interested in genes that control the timing of activation of different genetic programs, ensuring that various cell types develop at the right time. They studied two mutant strains of worms, lin-4 and lin-14, that displayed defects in the timing of activation of genetic programs during development. The laureates wanted to identify the mutated genes and understand their function. Ambros had previously shown that the lin-4 gene appeared to be a negative regulator of the lin-14 gene. However, how the lin-14 activity was blocked was unknown. Ambros and Ruvkun were intrigued by these mutants and their potential relationship and set out to resolve these mysteries.
Ambros and Ruvkun performed further experiments showing that the lin-4 microRNA turns off lin-14 by binding to the complementary sequences in its mRNA, blocking the production of lin-14 protein. A new principle of gene regulation, mediated by a previously unknown type of RNA, microRNA, had been discovered! The results were published in 1993 in two articles in the journal Cell.
Ruvkun cloned let-7, a second gene encoding a microRNA. The gene is conserved in evolution, and it is now known that microRNA regulation is universal among multicellular organisms.
Andrew Z. Fire and Craig C. Mello, awarded the Nobel Prize in 2006, described RNA interference, where specific mRNA-molecules are inactivated by adding double-stranded RNA to cells.
Mutations in one of the proteins required for microRNA production result in the DICER1 syndrome, a rare but severe syndrome linked to cancer in various organs and tissues.
Matthew S. Smith is a Contributing Editor for IEEE Spectrum and the former Lead Reviews Editor at Digital Trends.
The Nobel Prize Committee for Physics caught the academic community off-guard by handing the 2024 award to John J. Hopfield and Geoffrey E. Hinton for their foundational work in neural networks.
The pair won the prize for their seminal papers, both published in the 1980s, that described rudimentary neural networks. Though much simpler than the networks used for modern generative AI like ChatGPT or Stable Diffusion, their ideas laid the foundations on which later research built.
Even Hopfield and Hinton didn’t believe they’d win, with the latter telling The Associated Press he was “flabbergasted.” After all, AI isn’t what comes to mind when most people think of physics. However, the committee took a broader view, in part because the researchers based their neural networks on “fundamental concepts and methods from physics.”
“Initially, I was surprised, given it’s the Nobel Prize in Physics, and their work was in AI and machine learning,” says Padhraic Smyth, a distinguished professor at the University of California, Irvine. “But thinking about it a bit more, it was clearer to me why [the Nobel Prize Committee] did this.” He added that physicists in statistical mechanics have “long thought” about systems that display emergent behavior.
Hopfield first explored these ideas in a 1982 paper on neural networks. He described a type of neural network, later called a Hopfield network, formed by a single layer of interconnected neurons. The paper, which was originally categorized under biophysics, said a neural network could retain “memories” from “any reasonably sized subpart.”
Hinton expanded on that work to conceptualize the Boltzmann machine, a more complex neural network described in a 1985 paper Hinton co-authored with David H. Ackley and Terrence J. Sejnowski. They introduced the concept of “hidden units,” additional layers of neurons which exist between the input and output layers of a neural network but don’t directly interact with either. This makes it possible to handle tasks that require a more generalized understanding, like classifying images.
So, what’s the connection to physics?
Hopfield’s paper references the concept of a “spin glass,” a material in which disordered magnetic particles lead to complex interactions. Hinton and his co-authors drew on statistical mechanics, a field of physics that uses statistics to describe the behavior of particles in a system. They even named their network in honor of Ludwig Boltzmann, the physicist whose work formed the foundation of statistical mechanics.
And the connection between neural networks and physics isn’t a one-way street. Machine learning was crucial to the discovery of the Higgs boson, where it sorted the data generated by billions of proton collisions. This year’s Nobel Prize for Chemistry further underscored machine learning’s importance in research, as the award went to a trio of scientists who built an AI model to predict the structures of proteins.
Smyth saw Hopfield’s efforts first-hand as a student at the California Institute of Technology. “Hopfield was able to bring together mathematicians, engineers, computer scientists, and physicists. He got them in the same room, got them excited about modeling the brain, doing pattern recognition and machine learning, unified by mathematical theories he brought in from physics.”
In 2012, Hinton co-founded a company called DNNResearch with two of his students; Ilya Sutskever, who later co-founded OpenAI, and Alex Krizhevsky. Together, the trio collaborated on AlexNet, a hugely influential neural network for computer vision. Hinton also taught at the University of Toronto, where he continued to champion machine learning.
Navdeep Jaitly, now a deep learning researcher at Apple, said Hinton inspired new generations of engineers and researchers. In Jaitly’s case, the influence was direct; Jaitly studied under Hinton at the University of Toronto.
“I came in with experience in statistical modeling,” says Jaitly, “but Hinton still managed to entirely change how I think about problem solving. In terms of his contributions to machine learning, his methods are central to almost everything we do.”
Born 1933 in Chicago, IL, USA. PhD 1958 from Cornell University, Ithaca, NY, USA. Professor at Princeton University, NJ, USA.
Geoffrey E. Hinton University of Toronto, Canada
Born 1947 in London, UK. PhD 1978 from The University of Edinburgh, UK. Professor at University of Toronto, Canada.
was announced on 10/8/2024 in Stockholm, Sweden.
“for foundational discoveries and inventions that enable machine learning with artificial neural networks”
They trained artificial neural networks using physics
This year’s two Nobel Laureates in Physics have used tools from physics to develop methods that are the foundation of today’s powerful machine learning. John Hopfield created an associative memory that can store and reconstruct images and other types of patterns in data. Geoffrey Hinton invented a method that can autonomously find properties in data, and so perform tasks such as identifying specific elements in pictures.
John Hopfield invented a network that uses a method for saving and recreating patterns. We can imagine the nodes as pixels. The Hopfield network utilises physics that describes a material’s characteristics due to its atomic spin – a property that makes each atom a tiny magnet. The network as a whole is described in a manner equivalent to the energy in the spin system found in physics, and is trained by finding values for the connections between the nodes so that the saved images have low energy. When the Hopfield network is fed a distorted or incomplete image, it methodically works through the nodes and updates their values so the network’s energy falls. The network thus works stepwise to find the saved image that is most like the imperfect one it was fed with.
Geoffrey Hinton used the Hopfield network as the foundation for a new network that uses a different method: the Boltzmann machine. This can learn to recognise characteristic elements in a given type of data. Hinton used tools from statistical physics, the science of systems built from many similar components. The machine is trained by feeding it examples that are very likely to arise when the machine is run. The Boltzmann machine can be used to classify images or create new examples of the type of pattern on which it was trained. Hinton has built upon this work, helping initiate the current explosive development of machine learning.
AACR 2023 Meeting Highlights: Reports from Plenary Sessions and Major Symposium Talks
Reporter: Stephen J. Williams, Ph.D.
Highlights from Sunday April 16,2023
Nobel Laureate will discuss her work investigating the glycobiology of cancer
Carolyn R. Bertozzi, PhD, shared the Nobel Prize in Chemistry in 2022 for her invention of bioorthogonal chemistry, which is a class of chemical reactions that are compatible with living systems. These chemistries allow researchers to explore molecular imaging and drug targeting without interfering with natural biological processes. Bertozzi’s AACR Award for Outstanding Achievement in Chemistry in Cancer Research, and her lecture, focus on the glycobiology of cancer.
Carolyn R. Bertozzi, PhD
“There is a family of receptors on immune cells that bind carbohydrates,” said Bertozzi, Baker Family Director of the Sarafan ChEM-H Institute and Anne T. and Robert M. Bass Professor of Chemistry at Stanford University. “Called the ‘sialic acid-binding immunoglobulin-like lectins’ — abbreviated Siglecs — these receptors bind carbohydrates that possess the sugar sialic acid. There are 14 Siglec family members in humans and they are found in various combinations on every type of immune cell — T cells, macrophages, neutrophils, NK cells, all of the immune cell types that are important in anti-cancer immunity. As tumors progress, they often overexpress sialoglycan ligands for Siglecs, which allows them to engage these receptors and suppress immune-cell reactivity. We have focused on developing immune therapies that disrupt Siglec-ligand interactions.”
Bertozzi will discuss this area of her research during her award lecture, Targeting the Glycocalyx for Cancer Immune Therapy, at 4:30 p.m. ET Sunday in Tangerine Ballroom 3-4 (WF3-4) at the convention center.
“The signaling biochemistry of the Siglec family of checkpoint receptors is similar to the signaling biochemistry that PD-1 participates in,” Bertozzi explained. “They are like PD-1 except that they bind sugars rather than proteins, and they are present on every type of immune cell, including activated T cells, but also myeloid-derived cell types.”
“Glycobiology is an important area to become more familiar with if you want to truly be able to move the needle,” she said. “The science we have uncovered has led to the identification of exciting new targets, which has enabled us to invent new therapeutic modalities.”
Familiar small molecules and antibodies are of marginal use in targeting sugars, Bertozzi explained. Because carbohydrates are different types of molecules than traditional cancer targets, they need nontraditional mechanisms of action.A new class of targeted enzymes can edit the cell surface glycocalyx (or sugar coating) and deprive cancers of their ability to engage Siglec receptors. Without the broad inhibitory activity of Siglecs, the immune system remains free to engage and, hopefully, destroy tumors. At least one investigative agent is in phase I human trials and is poised to move into phase II.
“Glycobiology might explain why so many patients don’t respond to anti-PD-1 and anti-PD-L1 antibodies,” Bertozzi said. “We think a large fraction of tumors suppress the immune response through Siglec engagement.”
Other Articles on Real Time Coverage of AACR Meetings on this Open Access Scientific Journal Include:
The Nobel Prize in Physiology or Medicine 2023, jointly to Katalin Karikó and Drew Weissman for their discoveries concerning nucleoside base modifications that enabled the development of effective mRNA vaccines against COVID-19
Reporter: Aviva Lev-Ari, PhD, RN
The breakthrough
Karikó and Weissman noticed that dendritic cells recognize in vitro transcribed mRNA as a foreign substance, which leads to their activation and the release of inflammatory signaling molecules. They wondered why the in vitro transcribed mRNA was recognized as foreign while mRNA from mammalian cells did not give rise to the same reaction. Karikó and Weissman realized that some critical properties must distinguish the different types of mRNA.
RNA contains four bases, abbreviated A, U, G, and C, corresponding to A, T, G, and C in DNA, the letters of the genetic code. Karikó and Weissman knew that bases in RNA from mammalian cells are frequently chemically modified, while in vitro transcribed mRNA is not. They wondered if the absence of altered bases in the in vitro transcribed RNA could explain the unwanted inflammatory reaction. To investigate this, they produced different variants of mRNA, each with unique chemical alterations in their bases, which they delivered to dendritic cells. The results were striking: The inflammatory response was almost abolished when base modifications were included in the mRNA. This was a paradigm change in our understanding of how cells recognize and respond to different forms of mRNA. Karikó and Weissman immediately understood that their discovery had profound significance for using mRNA as therapy. These seminal results were published in 2005, fifteen years before the COVID-19 pandemic.
This year’s Nobel Prize laureates in chemistry Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures.
In 2020, Hassabis and Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.
Read more about their story: https://bit.ly/4diKiJ2