Dan Shiffman
Redwood City, California, United States
813 followers
500 connections
Patents
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On the role of market economics in ranking search results
Issued US 8005824
View Dan’s full profile
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Brandon Wang
The life science panelists at Asian American Pioneer Medal Symposium are inspiring the audience with great insights 1. Randy Schekman: study of how the 23k gene inside a cell to communicate across the cells leads to manufacturing of insulin that impact millions of lives. Relying on large gene data available to innovate new algorithm for AI. 2. Helen Blau: Discovery of drugs to stimulate the stem cell leads to muscle Rebuild. Using AI to predict how different protein combining. 3. Ziyang Zhang: how training in chemistry allows him to study life science from new angle, and then new discovery. 4. Liquin Luo: recent Computation progress allows more hypothesis to be studies than experimental method alone. Computation neuroscience is hot! 5. J. Jean Cui: Traditional clinical trial is no longer always working, major huddle is the precise modeling, thus, new approaches are needed to speedup in validating the targets, and bring lab to in-person use much faster. #AAPM #LifeScience #DrugDiscovery #Tech4Innovators
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Anita Modi
Just wrapped up the AI x BIO 2024 summit at the NYSE, hosted by Decoding Bio -- I was so impressed by the insightful discussions between founders, investors, and industry experts. The event highlighted the transformative power of AI in life sciences that we are seeing every day, and the exciting opportunities ahead! A notable takeaway was the prediction from an industry leader that AI will become so integral to life sciences that standalone conferences like this may no longer be needed in the future. At Peer AI, we share this vision and are committed to driving innovation in this space. I am grateful to be part of this community, working together to shape the future of life sciences! #AIinLifeSciences #LifeScienceInnovation #DigitalTransformation #AIxBIO #PeerAI #FutureOfHealthcare #TechnologyConvergence
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Sophia Lugo
I’m thrilled to announce the launch of Radar Therapeutics, dedicated to making programmable, safe, "smart" #mRNA-based therapies that only get activated in the right cells, at the right time. Working with Katherine Jones, Sarah Chesemore, and others at the Bill & Melinda Gates Foundation, we witnessed how Moderna and BioNTech SE/Pfizer catapulted mRNA therapies onto the world stage with unprecedented speed, saving millions of lives. At Radar, we want to bring this promise beyond vaccines into the most complex therapeutic compartments by solving one of the biggest problems for these therapies: making sure these mRNAs only reprogram the right cells in the body, avoiding potentially toxic off-target effects. We are committed to making therapies with the highest bar of safety, and widely accessible to patients that need them. I was incredibly lucky to meet my co-founder Eerik Kaseniit (thank you Anna-Julia Storch), while at Stanford University Graduate School of Business. Eerik was obsessed with the problem of precisely targeted, timely and controlled regulation of mRNA translation. Alongside Stanford Prof Xiaojing Gao and MIT Prof Jim Collins, we decided to direct those efforts towards saving lives by starting Radar Therapeutics. We attracted the brilliant minds Svetlana Lucas, David Schaffer, and Eric Klein along the way. We are grateful to NFX, Eli Lilly and Company, Biovision Ventures, and KdT Ventures for taking the lead on this round, alongside BEVC, Pear VC, ARTIS Ventures, Ten VC, Elementum Ventures, Emcure Pharmaceuticals Limited, SALT, Conscience VC, and more. Here’s to a future where we can program therapies to rewrite any disease with utmost safety and accuracy. I’ll be at #BIO2024 for anyone looking to connect! Sharing some information about the company here: Endpoints News - https://lnkd.in/g4MQT245 Firstword Pharma - https://lnkd.in/gq3heWsE Genetic Engineering News - https://lnkd.in/gNPDfHEs Chemical & Engineering News - https://lnkd.in/gUx8ijmY Longevity Technology - https://lnkd.in/gRwdtiKx PBR- https://lnkd.in/gvUG_c5M Businesswire - https://lnkd.in/gcv88QAM Alix Marduel Vasudev Bailey Asset Abdualiyev Shan-Lyn Ma Wesley Barrow Pioneer Fund Jackie Benson Shannon Dahl Jackie Papkoff, Ph.D. Nat Kolber Jon Levin Ilya Strebulaev StartX Ron Boger Howard Rosen
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Eric Langlois
I am excited to announce that one of Access Discovery Bio’s first clients is Cache DNA based out of San Carlos, CA. Cache has an exciting new technology that allows for the “Caching” of DNA and RNA, allowing them to be stored at ambient room temperature. Caching can be done on a small scale with various tubes or can be completely integrated into existing automated technologies that handle plates for working at scale. This is a profound development in my eyes after decades of both working in biorepositories and shipping frozen samples all over the world. I have personally witnessed sample evaporation and loss despite long term cold storage. I’ve seen the dramatic rise in costs not only to create and maintain freezer farms but also to safely ship frozen samples which can often dip into the thousands for a single shipment. I have personally been on call and had to travel into a lab at 2am to recover samples in a failed freezer or during a power outage. I have also worked in the lab having to use various techniques to properly freeze or thaw samples to work with them. For many reasons, I see this as a transformational technology. Who doesn’t like to save massive amounts of money? Everyone does of course and this comes with the added benefits of reducing carbon footprints and helping the environment. Michael Becich and his team at Cache are a great group of scientists who have created something truly beneficial to basic science. Cache is presently offering free demo kits to select customers while supplies last. Given my network is made up almost entirely of people using or storing samples for research, please help me in giving this great start up a signal boost and please reach out directly to me with interest in either demo kits or more information. #biobanking #DNA #RNA #technology #biospecimens https://lnkd.in/eK9q9JQk
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Ben Field
Trying to build AI into your products but worried about potential failure cases? There's a wide gap between a cool demo and something that's production ready. You can bridge that gap with a good data flywheel and solid eval sets. LLM applications need continuous evaluation, monitoring, and improvement to maintain performance over time. Here's a streetfighting cheatsheet for LLM evaluation: 1. Define clear, task-specific success metrics by examining actual LLM outputs, not just theorizing about potential failures. 2. Implement code-based, LLM-based and human judged eval metrics. The order of value is generally human > LLM > code. This also maps to how expensive the evals are to run. 3. Use binary (True/False) metrics when possible for easier alignment and consistency. 4. Validate inputs as well as outputs. 5. Keep metric implementations aligned by regularly sampling and labeling production data. 6. Use dynamic few-shot examples in prompts, retrieved based on input similarity and weighted by recency. 7. Manually iterate on prompts or pipelines based on metric scores and patterns in low-performing instances. 8. Bootstrap improvement by fixing low-scoring outputs and using them as few-shot examples. 9. Break down "good" outputs into multiple dimensions for more accurate labeling and targeted improvements. 10. Consider uncertainty quantification for LLMs to enhance alignment and prioritize data for human review. 11. If your LLM system has many points of call, graph it out. Whiteboarding your system and the potential failure points is very helpful. 12. Log all your data. Use tools like langfuse to make sure no production interaction goes to waste.
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Janis Naeve
Having spent the majority of my career in the pursuit of drug discovery (largely as a VC), I enjoyed reading "How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons" https://lnkd.in/d8ge3Cx5 by Boston Consulting Group. In a nutshell: it about doubles the success rate in Phase 1 but effect not seen in Phase 2 and beyond). Before we get too ahead of ourselves, Derek Lowe digs through the Supplementary materials and says ‘not-so-fast. Maybe so but the N value is low and these are mostly known targets and pathways’. Derek Lowe's In the Pipeline -https://lnkd.in/dxFgiZhr Being a Libra, I certainly strive to hold both conclusions in a balanced fashion. Fortunately a great friend and colleague, Philip Tagari, recently was interviewed by Vijay Pande, PhD on the a16z podcast “Raising Health: The Power of Drug Discovery”. What better way to put AI into perspective than when a great drug developer and visionary computational biologist share their perspectives. I’ve captured below the points that resonated with me and why I am excited about the new period of drug discovery we are entering. Drug Discovery is now at the point of transition from ‘finding to designing’. Enough knowledge has been amassed across modalities, molecules, genetics, human disease data, etc. With machine learning (ML) we can think much more about the biology necessary for impact and what the therapeutic needs to be to interact, and how do you design the approach. Machine Learning (ML) is not only an enabler of new things but a massive accelerator. ML is not applied just here and there but incorporated throughout the process pervading how you think about everything. The problem with pharma is that their systems aren’t native to ML. For companies like insitro, it is built from ground up so the infrastructure and architecture are different–a virtuous cycle, reuse models and infrastructure generating the flywheel effect. Then it becomes how do you make decisions at the speed this all is happening. Drug Discovery predictions next 5-10 years: 1) Fundamental overcapacity—going to be consolidation. Can’t have 35 companies working on the same targets 2) Ruthless drive for efficiency. Have to include advanced ML because of the enormous amount of waste in biomedical research which raises costs, reduces accessibility and has environmental impact. Personalized medicine will become achievable. Using all the tools of compute and automation. This will not only include therapeutics but can be applied through care delivery. ML can remove the ‘trial-and-error’ approach to care delivery. The Next-Generation of Drug Discovery companies will be ‘bilingual’. As Vijay points out ‘it is the people challenges that become more significant than the technologies’. Melding of tech and life sciences will require speaking both languages. Learning the language and trying to be fluent will be enormously enabling.
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Rishab Shah
Imagine transforming clinical studies from regulatory checkboxes to powerful sales engines. May 2nd is your opportunity to learn from a master. Meet 𝐁𝐞𝐧 𝐑𝐨𝐬𝐧𝐞𝐫, 𝐌𝐃, 𝐏𝐡𝐃 - a titan in digital health, lead clinical advisor at Manos Health and researcher and hospitalist at UCSF. Dr. Rosner's will be leading our 𝐒𝐚𝐥𝐞𝐬 𝐅𝐨𝐜𝐮𝐬𝐞𝐝 𝐂𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 workshop. But this isn't just another academic exercise. It's a deep dive into leveraging validation studies for market dominance. Why settle for traditional when you can: 🚀 Propel your clinical trials from costly obligations to potent revenue streams. 🧭 Navigate the complex studies decisions with ease, selecting only the most impactful and cost-effective. ✍🏻 Seamlessly translate hard data into compelling marketing narratives that captivate your target customers. This workshop is designed for the forward-thinking Founders, Health-Tech Executives, and Product Leaders. As a bonus, we’ll also be sharing the internal tools we use with clients: 🧬 Study Type Diagnostic (Determine the best and most cost-effective trial for your stage) 📈 Marketing Dissemination Templates (How to share your study results across socials and other marketing) ✅ Trial Readiness Checklist (What you need to get together to be ready to start with trials) Spaces are limited. Get your spot here
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