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Profs study deadly germ seen in Iraq | Yale Daily News. (n.d.). Yale
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Profs study deadly germ seen in Iraq | Yale Daily News
April 11, 2026Quick comment request on AlphaGenome for IBM Think
February 11, 2026Brodsky, S. (2026, February 10). AI models at IBM and DeepMind are pushing DNA toward a GPT era. IBM Think.
https://www.ibm.com/think/news/ibm-deepmind-ai-models-pushing-dna-toward-gpt-era
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“What I found most novel about AlphaGenome was its multimodal nature,” Mark Gerstein, the Albert L Williams Professor of Biomedical Informatics at Yale University, and who was not involved in the research, told IBM Think in an interview. “The fact that it is trained on data from many different genomic modalities—for instance, RNA-seq, ATAC-seq and Hi-C—and predicts effects across these modalities is particularly notable.”
Gerstein said AlphaGenome stands out because it tries to predict multiple genomic signals simultaneously and treats them as connected rather than independent. Changes in chromatin state upstream, for example, can shape gene expression downstream—and models have long recognized those links. What’s new, in his view, is the scale at which AlphaGenome tries to fold those relationships directly into
sequence-to-function prediction.
He also highlighted how much DNA the model can “see” in one pass. The window, he said, is unusually large, on the order of a megabase. It’s a span big enough to capture regulatory effects that can sit far from the genes they influence.
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Gerstein’s reaction comes with an asterisk: he called the results promising, but stressed that performance on curated benchmarks doesn’t always translate to messy real-world biology.
AlphaGenome, as he sees it, is powerful at describing what a single change might do within a genome model. But real genomes do not change one letter at a time. They come as whole, inherited packages, full of variants that shape one another’s effects. “In terms of limitations, one major issue is that the model predicts the effect of only a single variant and does not take into account the full genetic background of an individual’s personal genome,” he said. “Background genetics can substantially influence the impact of a particular variant,
particularly by strongly affecting how a gene is expressed in response to a mutation.”
He thinks the next step is imaginable, even if it is harder: a future version of this kind of work could move beyond scoring a single mutation in isolation and instead operate directly on personal genomes. “One could imagine extending AlphaGenome by building large models that operate directly on personal genomes,” he said.
Medicine demands forms of evidence that many model developers simply do not have access to, Gerstein noted.
“With respect to translation into clinical practice, the main requirement is the accumulation of many use cases in which the effects of particular mutations are documented, followed by downstream validation showing that the predictions are accurate and clinically useful,” Gerstein said. “There is no substitute in the medical world for experimental data and actual clinical validation, and this will be necessary before outputs from tools like this are accepted.” ….
He also stressed what AlphaGenome does not claim to do: “It is important to remember that this tool provides the molecular
consequences of specific mutations, not downstream phenotypic or disease-level effects,” he said. “As a result, additional work would be required to bridge that gap.”
“With respect to translation into clinical practice, the main requirement is the accumulation of many use cases in which the effects of particular mutations are documented, followed by downstream validation showing that the predictions are accurate and clinically useful,” Gerstein said. “In the medical world, there is no substitute for experimental data and actual clinical validation.”
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With AlphaGenome, Researchers Are Using A.I. to Decode the Human Blueprint – The New York Times
January 28, 2026https://www.nytimes.com/2026/01/28/science/alphagenome-ai-deepmind-genetics.html
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But Dr. Koo and other outside experts cautioned that it represented just one step on a long road ahead. “This is not AlphaFold, and it’s not going to win the Nobel Prize,” said Mark Gerstein, a computational biologist at Yale.
AlphaGenome will be useful. Dr. Gerstein said that he would probably add it to his toolbox for exploring DNA, and others expect to follow suit. But not all scientists trust A.I. programs like AlphaGenome to help them understand the genome.
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In 2021, Dr. Avsec and his colleagues unveiled a preliminary A.I. called Enformer, which they have since expanded into AlphaGenome. They trained the program on an even greater expanse of biological data. “It’s really an industrial scale,” Dr. Gerstein said
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YSM Researchers Recognized with Yale Faculty Innovation Awards | Yale School of Medicine
December 23, 2025From Lab to Launch: Eleven Faculty Innovators Recognized for Turning Research into Real-World Impact | Yale Ventures
November 23, 2025QT:{{”
The 2025 Yale Faculty Innovation Awards honor academic founders whose startups—rooted in Yale research—are advancing breakthroughs in health, sustainability, and engineering.
Date: 11/20/2025
Yale University has recognized eleven faculty innovators with the 2025 Yale Faculty Innovation Awards for translating breakthrough research into ventures that address some of the world’s most pressing challenges. These academic founders are transforming discoveries made in Yale labs into technologies that improve human health, advance sustainability, and shape the future of science and society.
Spanning AI-powered drug discovery and precision health, novel therapeutics for neurodegenerative and autoimmune diseases, advanced materials and microLED engineering, and next-generation tools for molecular imaging and genomic privacy, this year’s awardees exemplify how Yale research drives real-world impact.
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Advancing DNA analysis and privacy protection with cutting-edge data-science approaches.
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Efficient Privacy-Preserving Training Of Quantum Neural Networks Utilizes Mixed States For Data Ensembles
September 21, 2025Qt on lab analyses from She Has Her Mother’s Laugh
August 2, 2025Quote from Pg. 192 of the hardcover version of
She Has Her Mother’s Laugh: The Powers, Perversions, and Potential of Heredity by Carl Zimmer:
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It took a couple of weeks for Mark Gerstein to work over my genome. He and his students wanted to analyze the short fragments of DNA with their own software and create their own map. Once they had pinned down the location of the vast majority of Illumina’s fragments, they could then determine which variants I carried. ….
The Nigerian and the Chinese had a similar number of single-nucleotide polymorphisms. But those variants did not distinguish the three of us in any clear way. Sushant Kumar, a postdoctoral researcher in Gerstein’s lab, made me a Venn diagram to drive the point home. … “}}
Yale’s Colton Center for Autoimmunity Announces 2025 Awardees Advancing Innovation in Autoimmune Disease Research | Yale Ventures
July 30, 2025Yale’s Colton Center for Autoimmunity Announces 2025 Awardees Advancing Innovation in Autoimmune Disease Research | Yale Ventures
https://ventures.yale.edu/news/yales-colton-center-autoimmunity-announces-2025-awardees-advancing-innovation-autoimmune
Eight Translational Biotech Projects Selected for 2025 Blavatnik Accelerator Awards | Yale Ventures
July 12, 2025QT:{{”
Mark Gerstein, PhD, Albert L Williams Professor of Biomedical Informatics and Professor of Molecular Biophysics & Biochemistry, of Computer Science, and of Statistics & Data Science
Project: WearGenix: Linking Wearables to Brain Health
Modality: Digital Platform | Therapeutic Area: Neuropsychiatry
Dr. Gerstein’s platform combines smartwatch data with genomic analysis to identify biomarkers for mental health and neurodegenerative diseases. The technology has been tested for ADHD and is expanding to other neurodegenerative conditions.
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Wearables may offer clues to psychiatric diagnoses | Findings | Yale Alumni Magazine
March 1, 2025https://www.yalealumnimagazine.com/articles/6008-wearables-may-offer-clues-to-psychiatric-diagnoses
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A study by a Yale-led research team has shown that more accurate diagnosis of attention-deficit/hyperactivity disorder (ADHD) and other mental health conditions may be on the horizon, thanks to wearable sensors such as smartwatches. The study was published in the journal Cell.
ADHD, characterized by difficulty focusing, restlessness, and impulsive behavior, is the most commonly diagnosed behavioral disorder in children; it can lead to disruptions in learning and daily functioning. Early, accurate diagnosis and intervention can be critical in minimizing its impact. The problem, notes co–senior author Mark Gerstein, the Albert L. Williams Professor of Biomedical Informatics, is that “ADHD has traditionally been diagnosed
symptomatically, and there is an element of subjectivity to
categorizing human behavior. We wanted to see if wearable devices could increase diagnostic precision.”
The study analyzed clinical, wearable, and genetic data from 11,878 US adolescents (ages 9–14) recruited by the NIH Adolescent Brain Cognitive Development Consortium project. Information collected included measurements of heart rate, calorie expenditure, activity intensity, steps, and sleep intensity.
The researchers determined that correctly processed smartwatch data could be used as a “digital phenotype.” (Phenotype is the observable expression—physical characteristics, behaviors—of someone’s genotype, which is each person’s unique DNA sequence and their environment.) “We found,” says Gerstein, “that from the sensor readings we could quite accurately determine if someone has ADHD.”
The researchers used the data to train AI models to identify two psychiatric disorders. They further determined which measurements were most useful in characterizing them. Heart rate was the most important predictor for ADHD, while sleep quality and stage were more useful for identifying anxiety. Based on the patterns across the wearable features, the researchers were able to pinpoint genes and genetic variants associated with ADHD.
Gerstein notes the significance of making the connection to genotype. “We found that the smartwatch measurements can better relate disorders to genetics than just correlating them directly with clinical diagnoses,” he says. “Finding more genetic variants and genes related to the disorders could uncover molecular mechanisms and give us pathways to new treatments.”
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