Podcasts about Trey Ideker

As we enter our teenage years, many of us feel like life is just getting started. But for dogs, celebrating a ‘teen' birthday is a sign of old age, entering a phase when things start slowing down. Listener Susan was besotted with her beloved corgi Copper John and wants to know why our furry companions rarely live as long as us. We investigate what accounts for the huge differences in lifespans across animal species. From fish that live a few weeks, to sharks who can survive for 500 years, what are the factors that affect the ticking on our biological clocks? Central to this field is the idea of ‘live fast, die young', with some animals burning more quickly through their ‘life fuel'. But is this rate set in stone?Presenter Anand Jagatia find out how animals' growth, reproduction and anti-ageing methods contribute to the length of their survival. Dr Kevin Healy, a macroecologist at the University of Galway, discusses some of these theories, explaining how the dangers and luxuries faced by animals during their evolution shape their speed of life. One example of extreme slow living is the Greenland Shark. John Fleng Steffensen, Professor of Marine Biology at the University of Copenhagen, describes how he helped figure out how old they really are, and how their cold living quarters increase their lifespan. Alessandro Cellerino, physiologist at the Scuola Normale Superiore in Pisa, finds the key to the sharks' longevity in their DNA.Anand also goes on a hunt on the west coast of Ireland for a creature that lives fast but surprisingly, dies old. Noel Fahy, research student at the University of Galway, is his guide, while Dr Nicole Foley, Associate Research Scientist at Texas A&M University, reveals the life-extending secrets of this creature.And geneticist Trey Ideker, Professor at the University of California San Diego, busts the myth that one dog year is seven human years. But how much is this misconception off by?Presenter: Anand Jagatia Producer: Julia Ravey Content Editor: Cathy Edwards Studio Manager: Sarah Hockley Production Coordinator: Ishmael Soriano(Photo: Copper John the Welsh Pembrokeshire Corgi, by listener Susan)

The NIEHS Superfund Research Program (SRP) is hosting a Risk e-Learning webinar series focused on using artificial intelligence (AI) and machine learning to advance environmental health research. The series will feature SRP-funded researchers, collaborators, and other subject-matter experts who aim to better understand and address environmental health issues by applying AI and machine learning approaches to complex issues. Recent advances in AI and machine learning methods show promise to improve the accuracy and efficiency of environmental health research. Over the course of three sessions, presenters will discuss how they use AI and machine learning approaches to improve chemical analysis, characterize chemical risk, understand microbial ecosystems, develop technologies for contaminant removal, and more. In the third and final session, ML & AI Applications to Understand Omics, Metabolomics, & Immunotoxicity and Optimize Bioengineering Using Datasets, Models, and Mass Spectrometry, speakers will discuss how they apply machine learning and artificial intelligence tools to analyze mass spectrometry and microscopy data and optimize models for understanding metabolomics, metabolite pathways, and immunotoxicology To learn about and register for the other sessions in this webinar series, please see the SRP website. Grace Peng, Ph.D., is a co-coordinator of the National Institutes of Health (NIH) Common Fund's Bridge to Artificial Intelligence (Bridge2AI) program, bridging the gap between the biomedical, behavioral and bioethics research communities and the data science/AI communities through a consortium of diverse experts to set the stage for widespread adoption of AI/ML in medicine. Dr. Peng will give an overview of the Bridge2AI program and introduce one of their projects at the University of California San Diego — Trey Ideker, Ph.D. Dr. Ideker will discuss the cell maps for AI (CM4AI) functional genomics project, one of four major data generation projects under the Bridge2AI program. The goal of the project is to provide a comprehensive map of human cellular components through generation of major spatial proteomics datasets. John Efromson, M.S., will present on Ramona Optic, Inc.'s Multi-Camera Array Microscope [MCAM(TM)], which is used to automate imaging and computer vision analysis of zebrafish and greatly improves previous throughput and analysis capabilities. Multiple applications of machine learning will be discussed, including behavioral pose estimation and phenotyping, morphological analysis, and cell counting and fluorescence quantification, as well as how these distinct analyses can be used together for pharmacology, toxicology, and neuroscience research. Speakers:Grace C.Y. Peng, Ph.D., Division of Discovery Science and Technology (Bioengineering), National Institute of Biomedical Imaging and Bioengineering and Trey Ideker, Ph.D., University of California San DiegoJohn Efromson, M.S., Ramona OpticsForest White, Ph.D., Massachusetts Institute of Technology (MIT)Moderator: Hunter Moseley, Ph.D., University of Kentucky To view this archive online or download the slides associated with this seminar, please visit http://www.clu-in.org/conf/tio/SRP-ML-AI3_112224/

The NIEHS Superfund Research Program (SRP) is hosting a Risk e-Learning webinar series focused on using artificial intelligence (AI) and machine learning to advance environmental health research. The series will feature SRP-funded researchers, collaborators, and other subject-matter experts who aim to better understand and address environmental health issues by applying AI and machine learning approaches to complex issues. Recent advances in AI and machine learning methods show promise to improve the accuracy and efficiency of environmental health research. Over the course of three sessions, presenters will discuss how they use AI and machine learning approaches to improve chemical analysis, characterize chemical risk, understand microbial ecosystems, develop technologies for contaminant removal, and more. In the third and final session, ML & AI Applications to Understand Omics, Metabolomics, & Immunotoxicity and Optimize Bioengineering Using Datasets, Models, and Mass Spectrometry, speakers will discuss how they apply machine learning and artificial intelligence tools to analyze mass spectrometry and microscopy data and optimize models for understanding metabolomics, metabolite pathways, and immunotoxicology To learn about and register for the other sessions in this webinar series, please see the SRP website. Grace Peng, Ph.D., is a co-coordinator of the National Institutes of Health (NIH) Common Fund's Bridge to Artificial Intelligence (Bridge2AI) program, bridging the gap between the biomedical, behavioral and bioethics research communities and the data science/AI communities through a consortium of diverse experts to set the stage for widespread adoption of AI/ML in medicine. Dr. Peng will give an overview of the Bridge2AI program and introduce one of their projects at the University of California San Diego — Trey Ideker, Ph.D. Dr. Ideker will discuss the cell maps for AI (CM4AI) functional genomics project, one of four major data generation projects under the Bridge2AI program. The goal of the project is to provide a comprehensive map of human cellular components through generation of major spatial proteomics datasets. John Efromson, M.S., will present on Ramona Optic, Inc.'s Multi-Camera Array Microscope [MCAM(TM)], which is used to automate imaging and computer vision analysis of zebrafish and greatly improves previous throughput and analysis capabilities. Multiple applications of machine learning will be discussed, including behavioral pose estimation and phenotyping, morphological analysis, and cell counting and fluorescence quantification, as well as how these distinct analyses can be used together for pharmacology, toxicology, and neuroscience research. Speakers:Grace C.Y. Peng, Ph.D., Division of Discovery Science and Technology (Bioengineering), National Institute of Biomedical Imaging and Bioengineering and Trey Ideker, Ph.D., University of California San DiegoJohn Efromson, M.S., Ramona OpticsForest White, Ph.D., Massachusetts Institute of Technology (MIT)Moderator: Hunter Moseley, Ph.D., University of Kentucky To view this archive online or download the slides associated with this seminar, please visit http://www.clu-in.org/conf/tio/SRP-ML-AI3_112224/

A new research paper was published in Aging (Aging-US) Volume 14, Issue 24, entitled, “Epigenetic aging is associated with aberrant neural oscillatory dynamics serving visuospatial processing in people with HIV.” Despite effective antiretroviral therapy, cognitive impairment and other aging-related comorbidities are more prevalent in people with HIV (PWH) than in the general population. Previous research examining DNA methylation has shown PWH exhibit accelerated biological aging. However, it is unclear how accelerated biological aging may affect neural oscillatory activity in virally suppressed PWH, and more broadly how such aberrant neural activity may impact neuropsychological performance. Participants (n = 134) between the ages of 23 – 72 years underwent a neuropsychological assessment, a blood draw to determine biological age via DNA methylation, and a visuospatial processing task during magnetoencephalography (MEG). Researchers Mikki Schantell, Brittany K. Taylor, Rachel K. Spooner, Pamela E. May, Jennifer O'Neill, Brenda M. Morsey, Tina Wang, Trey Ideker, Sara H. Bares, Howard S. Fox, and Tony W. Wilson from the Boys Town National Research Hospital, University of Nebraska Medical Center, Creighton University, Heinrich-Heine University, and the University of California San Diego focused their analyses on the relationship between biological age and oscillatory theta (4-8 Hz) and alpha (10 - 16 Hz) activity among PWH (n=65) and seronegative controls (n = 69). “To our knowledge, no study to date has directly linked accelerated biological aging in PWH to the neuro-functional changes that occur in cognitively impaired PWH, which include deficits in visuospatial processing, attention, working memory, and motor function networks.” PWH had significantly elevated biological age when controlling for chronological age relative to controls. Biological age was differentially associated with theta oscillations in the left posterior cingulate cortex (PCC) and with alpha oscillations in the right medial prefrontal cortex (mPFC) among PWH and seronegative controls. Stronger alpha oscillations in the mPFC were associated with lower CD4 nadir and lower current CD4 counts, suggesting such responses were compensatory. Participants who were on combination antiretroviral therapy for longer had weaker theta oscillations in the PCC. Full press release - https://www.aging-us.com/news_room/Aging-Epigenetic-aging-associated-with-aberrant-neural-oscillatory-dynamics-serving-visuospatial-processing-in-people-with-HIV DOI: https://doi.org/10.18632/aging.204437 Corresponding Author: Tony W. Wilson - tony.wilson@boystown.org Keywords: HIV, epigenetics, biological age, visuospatial discrimination, oscillations About Aging-US: Launched in 2009, Aging (Aging-US) publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Visit our website at www.Aging-US.com​​ and connect with us: SoundCloud – https://soundcloud.com/Aging-Us Facebook – https://www.facebook.com/AgingUS/ Twitter – https://twitter.com/AgingJrnl Instagram – https://www.instagram.com/agingjrnl/ YouTube – https://www.youtube.com/agingus​ LinkedIn – https://www.linkedin.com/company/aging/ For media inquiries, contact media@impactjournals.com.

Scientific Sense ® by Gill Eapen: Prof Trey Ideker is Professor of Medicine, Bioengineering and Computer Science at the University of California, San Diego. He directs the National Resource for Network Biology, and the Cancer Cell Map and Psychiatric Cell Map Initiatives. A multi-scale map of cell structure fusing protein images and interactions. Nature. 2021 Nov 24. doi: 10.1038/s41586-021-04115 “We Might Not Know Half of What's in Our Cells, New AI Technique Reveals Interpretation of cancer mutations using a multiscale map of protein systems. Science. 2021 A protein network map of head and neck cancer reveals PIK3CA mutant drug sensitivity. A protein interaction landscape of breast cancer. Science. 2021 Oct;374(6563):eabf3066 “Studies Delve Deep into the Protein Machinery of Cancer Cells.” NCI (4 Nov 2021) “From COVID to cancer, gene-mapping tool could ‘revolutionize' treatment“. SF Chronicle (2 Oc “Moonshot Project Aims to Understand and Beat Cancer Using Protein Maps“. Singularity Hub (5 Oct 2021) “Looking Beyond DNA to See Cancer with New Clarity,” Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells. Cancer Cell (2020), https://doi.org/10.1016/j.ccell.2020.09.014. PMID: 33096023. [PDF] [PubMed] Related Press: UCSD Health, AZoLifeSciences, Med India, Health IT Analytics and ScienceDaily. Quantitative Translation of Dog-to-Human Aging by Conserved Remodeling of the DNA Methylome. Cell Systems. 2020 Aug 26;11(2):176-185.e6. doi: 10.1016/j.cels.2020.06.006. Epub 2020 Jul 2. PMID: 32619550 [PDF] [PubMed] *Cover Article Related Press: Here's a better way to convert dog years to human years, scientists say. Science Magazine (15 Nov 2019). See also: Scientific American, BBC, NPR, Washington Post, Discover Magazine, Smithsonian, New York Post, (and more) Identifying Epistasis in Cancer Genomes: A Delicate Affair. Cell. 2019 May 30;177(6):1375-1383. doi: 10.1016/j.cell.2019.05.005. Review. PMID: 31150618 [PDF] [PubMed] Using deep learning to model the hierarchical structure and function of a cell.* Nat Methods. 2018 Mar 5. doi: 10.1038/nmeth.4627. PMID: 29505029 [PDF] [PubMed] [Cover Art] *Cover article Please subscribe to this channel: https://www.youtube.com/c/ScientificSense?sub_confirmation=1 --- Send in a voice message: https://anchor.fm/scientificsense/message Support this podcast: https://anchor.fm/scientificsense/support

Consulta la tabla para saber la edad biológica de tu mejor amigo: https://www.facebook.com/112062497038990/posts/507128117532424/ Un equipo de investigadores de la Universidad de California San Diego, liderado por los genetistas Tina Wang y Trey Ideker, propuso en 2019 una nueva forma de calcular cómo envejecen los perros en comparación con los humanos. Para ello estudiaron un mecanismo epigenético llamado metilación del ADN. A medida que envejecemos, se agregan grupos metilo a las moléculas de ADN. Y hay genes que, al mutar, aceleran el proceso de envejecimiento. --- Send in a voice message: https://anchor.fm/xhk9/message

Kelly speaks to Trey Ideker, a professor at UC San Diego School of Medicine and Moores Cancer Center. 

Understanding cancer is like assembling IKEA furniture. Hear me out. Both start with individual pieces that make up the final product. For a cabinet, it's a list of labeled precut plywood. For cancer, it's a ledger of genes that—through the Human Genome Project and subsequent studies—we know are somehow involved in cells mutating, spreading, and eventually killing their host. Yet without instructions, pieces of wood can't be assembled into a cabinet. And without knowing how cancer-related genes piece together, we can't decipher how they synergize to create one of our fiercest medical foes. It's like we have the first page of an IKEA manual, said Dr. Trey Ideker at UC San Diego. But “how these genes and gene products, the proteins, are tied together is the rest of the manual—except there's about a million pages worth of it. You need to understand those pages if you're really going to understand disease.” Ideker's comment, made in 2017, was strikingly prescient. The underlying idea is seemingly simple, yet a wild shift from previous attempts at cancer research: rather than individual genes, let's turn the spotlight on how they fit together into networks to drive cancer. Together with Dr. Nevan Krogan at UC San Francisco, a team launched the Cancer Cell Map Initiative (CCMI), a moonshot that peeks into the molecular “phone lines” within cancer cells that guide their growth and spread. Snip them off, the theory goes, and it's possible to nip tumors in the bud. This week, three studies in Science led by Ideker and Krogan showcased the power of that radical change in perspective. At its heart is protein-protein interactions: that is, how the cell's molecular “phone lines” rewire and fit together as they turn to the cancerous dark side. One study mapped the landscape of protein networks to see how individual genes and their protein products coalesce to drive breast cancer. Another traced the intricate web of genetic connections that promote head and neck cancer. Tying everything together, the third study generated an atlas of protein networks involved in various types of cancer. By looking at connections, the map revealed new mutations that likely give cancer a boost, while also pointing out potential weaknesses ripe for target-and-destroy. For now, the studies aren't yet a comprehensive IKEA-like manual of how cancer components fit together. But they're the first victories in a sweeping framework for rethinking cancer. “For many cancers, there is an extensive catalog of genetic mutations, but a consolidated map that organizes these mutations into pathways that drive tumor growth is missing,” said Drs. Ran Cheng and Peter Jackson at Stanford University, who weren't involved in the studies. Knowing how those work “will simplify our search for effective cancer therapies.” Cellular Chatterbox Every cell is an intricate city, with energy, communications systems, and waste disposal needs. Their secret sauce for everything humming along nicely? Proteins. Proteins are indispensable workhorses with many tasks and even more identities. Some are builders, tirelessly laying down “railway” tracks to connect different parts of a cell; others are carriers, hauling cargo down those protein rails. Enzymes allow cells to generate energy and perform hundreds of other life-sustaining biochemical reactions. But perhaps the most enigmatic proteins are the messengers. These are often small in size, allowing them to zip around the cell and between different compartments. If a cell is a neighborhood, these proteins are mailmen, shuttling messages back and forth. Rather than dropping off mail, however, they deliver messages by physically tagging onto other protein. These “handshakes” are dubbed protein-protein interactions (PPIs), and are critical to a cell's function. PPIs are basically the cell's supply chain, communications cable, and energy economy rolled into one massive infrastructure. Destroying just one PPI can lead a thriving cell to die. PPIs ar...

text by Francesca Giuliani-Hoffman, CNN How do you compare a dog's age to that of a person? A popular method says you should multiply the dog's age by 7 to compute how old Fido is in "human years."But new research published Thursday in the Cell Systems journal debunks that method. And that's because the scientists behind a new study say dogs and humans don't age at the same rate.Researchers at the University of California San Diego School of Medicine have developed a new formula that takes into account that variance. Tracking molecular changes in the DNA of Labrador retrievers, and in particular "the changing patterns of methyl groups" in their genome, according to a release, the study shows how dogs age at a much faster rate than humans early in their lives, then slow down after reaching maturity."This makes sense when you think about it — after all, a nine-month-old dog can have puppies, so we already knew that the 1:7 ratio wasn't an accurate measure of age," lead author Trey Ideker is quoted as saying.Based on the study, a one-year-old dog compares to a 30-year-old human, a four-year-old dog to a 52-year-old human. The rate of aging decreases after dogs turn 7.The new formula "is the first that is transferable across species," and scientists plan to test their findings on other dog breeds to study the impact of longevity on their findings, according to a release.Researchers also believe that observing changes in the methylation patterns before and after the use of anti-aging products could help veterinarians make more informed decisions in terms of diagnostics and treatment.A graphic in the study makes the age comparisons intuitive and provides some helpful context for dog owners, including the scientists themselves."I have a six-year-old dog — she still runs with me, but I'm now realizing that she's not as 'young' as I thought she was," Ideker is quoted as saying.

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Trey Ideker, PhD argues for “visible” approaches that guide model structure with experimental biology. Series: "Exploring Ethics" [Show ID: 35459]

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Trey Ideker, PhD argues for “visible” approaches that guide model structure with experimental biology. Series: "Exploring Ethics" [Show ID: 35459]

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Trey Ideker, PhD argues for “visible” approaches that guide model structure with experimental biology. Series: "Exploring Ethics" [Show ID: 35459]

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Trey Ideker, PhD argues for “visible” approaches that guide model structure with experimental biology. Series: "Exploring Ethics" [Show ID: 35459]

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Trey Ideker, PhD argues for “visible” approaches that guide model structure with experimental biology. Series: "Exploring Ethics" [Show ID: 35459]

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Trey Ideker, PhD argues for “visible” approaches that guide model structure with experimental biology. Series: "Exploring Ethics" [Show ID: 35459]

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Trey Ideker, PhD argues for “visible” approaches that guide model structure with experimental biology. Series: "Exploring Ethics" [Show ID: 35459]

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Trey Ideker, PhD argues for “visible” approaches that guide model structure with experimental biology. Series: "Exploring Ethics" [Show ID: 35459]

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Trey Ideker, PhD argues for “visible” approaches that guide model structure with experimental biology. Series: "Exploring Ethics" [Show ID: 35459]

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Trey Ideker, PhD argues for “visible” approaches that guide model structure with experimental biology. Series: "Exploring Ethics" [Show ID: 35459]

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Trey Ideker, PhD argues for “visible” approaches that guide model structure with experimental biology. Series: "Exploring Ethics" [Show ID: 35459]

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Trey Ideker, PhD argues for “visible” approaches that guide model structure with experimental biology. Series: "Exploring Ethics" [Show ID: 35459]

Trey Ideker and Samson Fong teach a course at UC San Diego School of Medicine called Biological Networks and Biomedicine. It’s designed to introduce graduate students to the concept of network... A podcast about science and discovery at UC San Diego Health. In each episode, we bring you the story of one project, one discovery or one scientist.

In this edition, we learn how HIV is linked to premature aging, with Trey Ideker, Molecular Cell (00:00); how seeing and perceiving visual information isn’t actually the same thing, with Michael Cohen, Trends in Cognitive Sciences (7:48); how ancient trees need special conservation, with William Laurance, Trends in Ecology and Evolution (13:10); and how the salary gap persists between men and women (19:10). Plus much more!

In this month's Cell Podcast, we learn how llamas have helped the study of G protein-coupled receptors, with Brian Kobilka (0:00) (Trends in Pharmacological Sciences), how to teach an old genetic analysis test a cool new trick, with Trey Ideker (10:50) (Cell Reports). Plus, sample a selection of the hottest new papers from Cell Press (17:00).

Trey Ideker (http://www.idekerlab.ucsd.edu) discusses methods for visualizing high-throughput protein-protein and protein-DNA interaction data, which often produce the infamous 'hairballs'. He presents several compelling examples, using the widely-used Cytoscape tool he founded, that demonstrate how network comparison and other methods focusing on biological or bio-medically relevant questions can identify sub-networks that are easily manageable. This talk was presented at VIZBI 2011, an international conference series on visualizing biological data (http://www.vizbi.org) funded by NIH & EMBO.