{"podcast":{"title":"Latent Space: The AI Engineer Podcast","slug":"latent-space-ai-engineer","podcast_index_feed_id":6058902,"rss_url":"https://api.substack.com/feed/podcast/1084089.rss","website_url":"https://www.latent.space/podcast","image_url":"https://substackcdn.com/feed/podcast/1084089/ca7468da5614a246d2906ee8926f6de7.jpg","author":"Latent.Space","episode_count":204,"summary":"The AI Engineer newsletter + Top technical AI podcast. How leading labs build Agents, Models, Infra, & AI for Science. See https://latent.space/about for highlights from Greg Brockman, Andrej Karpathy, George Hotz, Simon Willison, Soumith Chintala et al!","last_synced_at":null,"page_url":"https://stenobird.com/podcast/latent-space-ai-engineer"},"episode":{"title":"🔬 Training Transformers to solve 95% failure rate of Cancer Trials — Ron Alfa & Daniel Bear, Noetik","slug":"training-transformers-to-solve-95-failure-rate-of-cancer-trials-ron-alfa-daniel-bear-noetik","published_at":"2026-04-20T16:17:17+00:00","page_url":"https://stenobird.com/podcast/latent-space-ai-engineer/training-transformers-to-solve-95-failure-rate-of-cancer-trials-ron-alfa-daniel-bear-noetik","show_page_url":"https://stenobird.com/podcast/latent-space-ai-engineer","url":"https://www.latent.space/p/noetik","audio_url":"https://api.substack.com/feed/podcast/194810752/1b92bce4d49858354007a47c48e4e6d4.mp3","summary":"The 95% failure rate in cancer clinical trials is often a patient selection problem rather than a drug discovery failure. Noetik uses large-scale spatial transcriptomics and transformer models to predict how specific tumors will respond to existing treatments.","meta_description":"Learn how Noetik uses autoregressive transformers and spatial transcriptomics to solve the patient-matching bottleneck in cancer clinical trials.","key_points":["Main idea: Clinical trial failures are driven by poor patient-to-treatment matching, not necessarily poor pharmacology","Practical takeaway: Using H&E assays to predict 19,000-gene spatial maps can identify responders without expensive whole-plex sequencing","Technical insight: Scaling models on massive, multi-modal datasets (H&E, protein, and spatial transcriptomics) is essential for capturing non-linear biological patterns","Failure mode: Relying solely on cell-line-based drug discovery fails to account for the complex, multicellular architecture of human tumors","Strategic shift: The industry is moving from drug-discovery-only models toward software licensing platforms that improve trial success rates"],"chapters":[{"start_ms":60000,"title":"The Thesis: Solving the Matching Problem","summary":"Introduction to Noetik's approach to reducing the 95% failure rate in cancer trials through better patient selection."},{"start_ms":430000,"title":"The Gap in Cell Line Models","summary":"Why traditional cell line studies fail to translate to human clinical outcomes due to lack of mutation complexity."},{"start_ms":820000,"title":"Scaling Biological Transformers","summary":"The necessity of massive, multi-modal datasets to achieve the scaling laws seen in natural language processing."},{"start_ms":1205000,"title":"Decoding Spatial Transcriptomics","summary":"Defining spatial transcriptomics and how it provides the richest possible map of tumor biology."},{"start_ms":1575000,"title":"Computer Vision for Biology","summary":"Treating high-dimensional gene expression as a massive computer vision problem with thousands of channels."},{"start_ms":1970000,"title":"Simulating Genetic Perturbations","summary":"Using 'world models' to simulate the effects of knocking down specific genes within a tumor environment."},{"start_ms":2360000,"title":"The Advantage of Paired Data","summary":"How Noetik's unique access to paired H&E and spatial data provides a competitive edge in model training."}],"topics":["Spatial Transcriptomics","Transformer Models","Cancer Clinical Trials","Drug Development","Bioinformatics","Machine Learning","Precision Medicine","Biotech Software"],"duration_seconds":5121,"processing_state":"processed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/latent-space-ai-engineer/episodes/training-transformers-to-solve-95-failure-rate-of-cancer-trials-ron-alfa-daniel-bear-noetik/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/latent-space-ai-engineer/training-transformers-to-solve-95-failure-rate-of-cancer-trials-ron-alfa-daniel-bear-noetik.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}