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Beyond Correlation: Apotek’s Causal Path Platform for Smarter Target Identification in Drug Discovery
Author: Chin-Lin Chen, Ph.D / Data Scientist at Vizuro Key Takeaway Drug discovery has entered the era of big data. Every year, thousands of disease and normal samples are profiled — yet one critical bottleneck remains: identifying clinically meaningful target–marker gene pairs for new drug development . The core value of Apotek Path is for this purpose: Identify disease-relevant targets → Causal modeling reveals mechanistic gene interactions tailored to your disease contex
Chin-Lin Chen
Sep 265 min read
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From Object Recognition to Physical AI: When SDG Meets the Physical World
The future of Physical AI isn't just about better technology—it's about smarter SDG implementation. Organizations that understand how to match SDG strategies with their specific Physical AI needs will gain significant competitive advantages.
ginochang
Sep 196 min read
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From Correlation to Causation: Why Pharma Is Embracing Causal AI
Why causal AI is becoming pharma’s next strategic differentiator — from target validation to clinical trials and real-world evidence. Visualizing the Ladder of Causation in Pharma: from seeing correlations, to doing interventions, to imagining counterfactuals — the foundation of causal AI in drug discovery. Introduction: The Limits of Correlation in Drug Discovery Drug discovery today generates unprecedented volumes of data. Omics, imaging, electronic health records, and real
Esther Chen
Sep 76 min read
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Choosing the Right Scale and Tasks: Making SDG Work for Your AI Projects
How Corvus's synthetic data generation platform addresses the critical challenge of matching model capabilities with data requirements...
Ting-Yuan Wang
Sep 46 min read
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Why Ad Platform Reports Don’t Tell the Full ROI Story
Ad platform reports from Google, Meta, and others usually over-credit the last click and don’t reveal the true drivers of ROI....
Po-Han Huang
Sep 13 min read
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