Chris Tava

QTLmax 5.0 is acclaimed for its exceptional accuracy in genomic prediction. A trade-off for the excellence is a hefty computational cost, when handling large-scale datasets. We mitigate this challenge by leveraging GPU acceleration on the AMD's ROCm platform. If precise genomic prediction within a reasonable timeframe is important to your business, you can rest assured with QTLmax 5.0.

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Another advancement of AI programmable biology EDEN-AI (Environmentally-Derived Evolutionary Network), a family of generative foundation models designed to transform therapeutic design into a predictable engineering discipline by learning from billions of years of metagenomic evolution (metagenomic dark matter). https://lnkd.in/g-KEXJ5G

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Our recent peer-reviewed paper on JMIR AI: The Leading Journal for Real-World AI Applications in Medicine with Mount Sinai Health System evaluates the performance Tailored #NLP Pipelines for Radiology, Pathology, and Progress Notes in #ClinicalDecisionSupport: https://lnkd.in/gcmFpxEK #ai #research #healthcareai #healthai #datascience #textmining

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Dynamic Trust Calibration for AI-Assisted Clinical Decision Making: Trust in AI should be computed, not assumed. (https://lnkd.in/e_uiCSKh) Instead of treating trust as a subjective perception, we propose a dynamic trust calibration framework that combines: • AI confidence • Semantic alignment with clinical reasoning • Transparency signals These factors produce a composite trust score that determines when clinicians should accept or override AI recommendations. We also measure trust behaviorally using clinician override rates, showing how calibrated confidence and transparency can significantly improve adoption. The main insight: Trustworthy AI is not just about better models—it requires a trust-calibration layer between AI outputs and human decisions. Paper: https://lnkd.in/e_uiCSKh

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A clinical development team I was on call with realized their comparator arm had just become obsolete. Six months earlier, the protocol was sound. But oncology moves fast. A new readout shifted the standard of care overnight, and their trial design was suddenly a relic. This is the reality for teams running complex programs right now: ADC Therapeutics is managing combination trials across LOTIS-5 and LOTIS-7. Zymeworks Inc. reprioritized its pipeline, advancing ZW251 over ZW220. Revolution Medicines is scaling multiple Phase 3 programs simultaneously. IDEAYA Biosciences is balancing biomarker-driven eligibility with enrollment speed. ORIC Pharmaceuticals Inc. is transitioning dose optimization data into a global registrational trial. Their operational teams are keeping up. That deserves respect. But even the strongest teams hit the same structural wall. When a protocol requires an urgent amendment, propagating changes across the Investigator's Brochure, Informed Consent Forms, and downstream data collection requirements is still manual. That cross-document work eats days of bandwidth better spent on strategic execution. A clinically aware platform identifies downstream impacts, flags version control conflicts before they happen, and propagates the amendment across every required document in hours instead of weeks. Oncology is not going to slow down. The operational side has to keep up.

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These exits matter. Not because they make headlines, but because they reveal something deeper about direction and integrity inside organizations. Yann LeCun, Meta’s longtime Chief AI Scientist (Turing Award winner, and considered one of the founding fathers of modern deep learning) is reportedly leaving Meta to launch a new company focused on world models that learn from video and spatial data instead of text. Behind the scenes, this move follows Meta’s sweeping AI reorg, where LeCun began reporting to Alexandr Wang and FAIR (Meta’s AI research lab) saw over 600 roles cut. His departure is symbolic. In every sector, whether we’re talking about Big Tech, Medicaid transformation, or public-sector AI, these moments mark a fork in the road: Do we double down on scale, speed, and short-term returns? Or do we stay anchored to long-term scientific integrity, transparency, and human benefit? For those of us working at the intersection of AI and social systems — Medicaid, behavioral health, aging services — these leadership shifts aren’t just gossip. They’re reminders of what happens when values and vision diverge. MettaHealth Partners we believe integrity at scale isn’t just a nice-to-have. It’s the only way AI can serve the public good. — 💡 If you lead teams or design systems in public health or social services, this is the moment to ask: Are we aligning our AI direction with our mission — or just following the market? #AIethics #PublicSectorAI #MedicaidInnovation #Leadership #Integrity #DigitalTransformation https://lnkd.in/gpqyQxbZ

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The AI assistant is 20% model and 80% effort. MIT Sloan reports that the vast majority of agent deployment is "sociotechnical". The behind-the-scenes work of implementation and infrastructure. A smart model is a starting point, not an outcome. If you're going to deliver beyond the AI Vending Machine, you must curate the knowledge base, align the model with brand intent, and commit to the labor of testing. Done correctly, this effort helps manage costs and ensures the output serves stakeholders.

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🧬 Building the Virtual Cell starts with data. Today, we’re making the X-Atlas/Orion Perturb-seq dataset even more accessible — now live on Hugging Face 📊 One of the largest & highest-quality perturbation datasets ever released, it provides the foundation for training AI models that can simulate and reason about cellular behavior. 👉 Explore here: https://lnkd.in/gKiDmXkZ Fewer barriers. More models. A step closer to the virtual cell. 🌌 Shoutout to my amazing colleague and friend, Ci Chu ,who is leading the data generation efforts in Xaira Therapeutics #VirtualCell #FoundationModels #AIinBiology #HuggingFace

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