Data in Biotech

About Data in Biotech

Data in Biotech is a fortnightly podcast exploring how companies leverage data to drive innovation in life sciences. Every two weeks, Ross Katz, Principal and Data Science Lead at CorrDyn, sits down with an expert from the world of biotechnology to understand how they use data science to solve technical challenges, streamline operations, and further innovation in their business. You can learn more about CorrDyn - an enterprise data specialist that enables excellent companies to make smarter strategic decisions - at www.corrdyn.com (https://www.corrdyn.com)

Publisher

CorrDyn

Category

science · technology

Language

en

Explicit

No

First episode

25 Sept 2023

Latest episode

30 Sept 2026

Latest episodes

1 of 79 episodes have a transcript.

1. 

   30 Sept 2026

   How Standardized Cell Labeling Could Fix Biotech's Data Problem

   Flow cytometry can find a cell population in seconds, but naming it consistently across labs is a problem still unsolved. If you've ever tried to compare cytometry data across studies, CROs, or even two scientists in the same lab, you know the frustration: everyone gates their own way, and the same cell population ends up with three different definitions. That inconsistency is quietly limiting what AI can do with biotech data. Ryan Brinkman is VP Research Director of Flow Cytometry Bioinformatics at Dotmatics and Founding Director of SOULCAP, the Standard Ontology for Unambiguous Labeling in Cytometry and Phenotyping, built after years as an academic developing automated gating tools that kept running into the same naming problem. He's joined by Brian Wile, who is the General Manager of Flow Cytometry at KCAS Bio, a CRO that feels the cost of inconsistent labeling in client projects every day. You'll get a clear picture of how flow cytometry data moves from raw signal to labeled cell population, why that last step has resisted automation, and what a shared standard could unlock for machine learning models trained on this data. Ryan and Brian break down the gap between automated gating and consistent labeling, and why agreement, not just data volume, is what AI in biotech actually needs. This episode covers the mechanics of flow cytometry, an EVE Online citizen science project that trained a gating algorithm on hundreds of millions of human-labeled plots, and why cell population names like "Treg" or "natural killer cell" don't map to one agreed set of markers. Key Takeaways Gating automation solved half the problem: computers can now draw boundaries around cell populations, but nothing proves which name belongs on the result. A citizen science project turned hundreds of thousands of EVE Online players into an unlikely training set, collecting roughly half a billion labeled plots from people with zero biology background. The same cell type, like a regulatory T cell, gets defined by entirely different marker combinations depending on the lab or CRO, and those datasets can't simply be merged later. SOULCAP is running a Delphi-style consensus process among scientists worldwide to tie every cell population name to a specific, reproducible set of markers and experiments. Chapter Markers 00:00 Why naming cells is harder than naming genes 02:13 What flow cytometry measures, from cell to signal 06:21 The scale and complexity of high-dimensional cytometry data 09:20 How raw data becomes a gated cell population 12:02 Why gating has stayed manual for decades 16:19 Clusters of differentiation and the marker explosion 18:11 Training an algorithm with EVE Online players 25:31 Evaluating accuracy without a gold standard 31:34 Why solving gating doesn't solve labeling 36:32 What genomics got right that cytometry hasn't 38:09 Two definitions of a regulatory T cell, one label 44:28 The cost of inconsistent labeling for CROs and pharma 47:43 Introducing SoCAP and the Delphi consensus process 57:18 What automated labeling could make possible 65:38 How to get involved in SOULCAP Useful Links & Resources Ryan Brinkman on LinkedIn: https://www.linkedin.com/in/ryan-brinkman-9bb1103/ Brian Wile on LinkedIn: https://www.linkedin.com/in/brianwile/ KCAS Bio: https://www.kcasbio.com CorrDyn: https://www.corrdyn.com Connect With the Show Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ CorrDyn LinkedIn: https://www.linkedin.com/company/corrdyn/ Where do you land on the naming problem? Have you had to merge cytometry datasets that turned out to use different definitions for the same cell type? Tell us about it in the comments. Visit corrdyn.com to learn how CorrDyn can help your organization extract value from data. #DataInBiotech #FlowCytometry #BiotechDataScience #Bioinformatics #LifeSciences

2. 

   17 Sept 2026

   DrugBank CEO: Why Your AI Model Is Only Giving You Half the Answer

   Ask a general AI model how many approved drugs hit a target, and it might tell you three when the real answer is six, sounding just as confident either way. If your team is grounding drug discovery decisions in AI output with no way to trace where the answer came from, you're one regulator's question away from a very expensive problem. Lisa Downey is CEO of DrugBank, a structured biomedical intelligence platform cited in more than 60,000 papers and used by nine of the top 20 global pharma companies. She previously built Clarivate's genomic and rare disease data business from the ground up and held leadership roles at GlobalData, giving her almost 20 years across healthcare and life sciences data. Lisa breaks down why the bottleneck in AI-driven drug discovery has shifted from data scarcity to trustworthy grounding, and what that means for teams making target identification and go/no-go calls. You'll hear how DrugBank's knowledge graph separates causation from correlation, why reproducibility matters more than speed, and what questions to ask before building a reference data layer in-house. This episode covers deterministic versus probabilistic data, human-in-the-loop versus human-over-the-loop curation, and how biopharma teams connect grounding layers to their AI agents through MCP. It's built for data and analytics leaders, R&D teams, and anyone deciding whether to build or buy their biomedical data infrastructure. Key Takeaways - A general model asked how many approved drugs hit PD-L1 will answer with total confidence, and total inaccuracy, missing half the real number without any signal that it's wrong. - Anthropic's own benchmarks found frontier models pulling public genomic data got it right as little as 17% of the time, until a deterministic tool pushed accuracy past 99%. - DrugBank moved from human-in-the-loop curation to human-over-the-loop oversight once its data was connected enough that one expert validating one relationship could cascade trust across dozens of related facts. - Before building or buying a reference data layer, Lisa lays out four questions that separate real infrastructure from marketing, starting with whether every fact traces back to a source and a date. Chapter Markers 00:00 Why data scarcity isn't the real bottleneck anymore 01:22 What drew Lisa to DrugBank's mission 03:04 What DrugBank is and who relies on it 05:03 The grounding layer: completeness and reproducibility 07:28 Anthropic's benchmark on data infrastructure 09:21 The high-stakes decisions DrugBank data informs 12:23 Where lost cycle time actually comes from 14:13 DrugBank versus homegrown knowledge graphs 19:43 Human-in-the-loop versus human-over-the-loop curation 24:12 How DrugBank checks its own data quality 25:37 Deterministic versus probabilistic data explained 28:52 The J&J case: separating causation from correlation 33:06 Connecting DrugBank to your AI stack via MCP 37:28 Four questions to ask before you build or buy 42:15 Where DrugBank fits, and where it doesn't 44:44 AI as an amplifier of both good and bad decisions Useful Links & Resources - Connect with Lisa Downey on LinkedIn (https://www.linkedin.com/in/lisaldowney/) Connect With the Show - Ross Katz on LinkedIn (https://www.linkedin.com/in/b-ross-katz/) - CorrDyn on LinkedIn (https://www.linkedin.com/company/corrdyn/) Have you run into an AI model giving you a confident, wrong answer in your own R&D work? Tell us about it in the comments, we're always looking for real examples for future episodes. Visit corrdyn.com to learn how CorrDyn can help your organization extract value from data. #DataInBiotech #BiotechAI #DrugDiscovery #DataScience #LifeSciences

3. 

   2 Sept 2026

   How to Identify the Blind Spots in Your Biotech's Genomic Data Before They Cost You a Drug Target

   Most drug discovery genomic data comes from a thin slice of the world, and that bias follows every decision downstream. Your team can run a Mendelian randomization study on 35,000 patients and still walk away with a single signal that doesn't even apply to the population you care about. If your phenotype definitions are fuzzy, more data won't save you. Erika Kvikstad is a computational biologist who led precision medicine for cardiovascular disease at Bristol-Myers Squibb, working on therapies including Camzyos for hypertrophic cardiomyopathy. She now works independently on genomic data equity, focused on how reference populations shape everything from target discovery to clinical trial recruitment. You'll get a practical look at how to evaluate real-world data vendors, why heart failure is nearly impossible to define cleanly from billing codes, and where statistical power breaks down even with tens of thousands of patients. Erika also explains how her team used AI to reconstruct missing imaging data and validate cardiomyopathy diagnoses at scale. This episode covers GWAS studies, Mendelian randomization, UK Biobank, proteome-wide analysis, and the practical gap between biobank-scale data and disease-specific cohorts. It's built for data and analytics leaders working in life sciences who need to understand where genomic bias enters their pipeline, not just that it exists. Clarification Around 57:58–58:24, in discussing the proteome-wide Mendelian randomization study, Erika moved quickly between two related findings. BTN3A2 was identified as a candidate associated with ischemic stroke and potential immune-modulatory biology. Separately, single-cell expression data helped contextualize other candidate signals, including some with enriched expression in cardiomyocyte populations. Cardiomyocyte-enriched expression was not a specific finding for BTN3A2. Chapter Markers 00:00 Whose genome are we designing drugs for 01:34 Erika's path from academic genomics to BMS 03:48 Building the precision medicine strategy at BMS 06:37 Ross shares his own HCM diagnosis 07:09 Why heart failure resists clean definition 11:11 How medication use reclassifies patients 14:35 Imaging as a biomarker, and its data gaps 20:23 Data infrastructure gaps across regions 22:44 What to look for when evaluating a data vendor 27:35 Consortia and biobanked specimens for rare mutations 29:52 Cardiovascular data infrastructure versus oncology 32:29 Where statistical power breaks down 37:07 UK Biobank's strengths and its limits 40:01 Bridging broad biobanks with disease-specific cohorts 44:32 How reference population bias propagates downstream 48:53 Where genomic bias hits hardest in the pipeline 53:18 Inside a proteome-wide Mendelian randomization study 59:42 Choosing the right computational tool for the question 1:06:38 Building globally representative genomic infrastructure 1:08:04 Ross's takeaways on bias and statistical power Useful Links & Resources - Erika on LinkedIn: https://www.linkedin.com/in/erikakvikstad - UK Biobank: https://www.ukbiobank.ac.uk - Alliance for Genomic Discovery: https://alliancegenomicdiscovery.org - SHaRe Registry (DCM Foundation): https://dcmfoundation.org Connect With the Show - Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ - (Ross Katz on X: https://x.com/brosskatz - CorrDyn LinkedIn: https://www.linkedin.com/company/corrdyn/ Have you run into genomic reference bias in your own work? Tell us what it looked like and how your team caught it. Visit corrdyn.com to learn how CorrDyn can help your organization extract value from data. Subscribe to Data in Biotech so you don't miss the next conversation.

Inside a recent episode

Decoding the Dark Proteome: Biological Insights for Pharma with Dr. Jonathan Usuka from Sapient

Published 2 Apr 2025 · Transcript excerpt

[…] So we serve pharma companies, biotech researchers and academic researchers of disease by helping them to understand what is going on in their samples, to see and interpret all of these different molecules and then apply that to either finding new drug targets, to find new biomarkers that show that those drugs are actually working well in a therapeutic way that won't invoke a safety issue with the patient and then ultimately to stratify patients so that those patients that will benefit from therapy are on therapy and those patients that would have an adverse event or a safety issue would not be on therapy. […]

Who has been a guest on Data in Biotech

Names identified in recent episode analyses. Showing up to five guests.

• Anil Kane
• John Liberty
• Hugo Shi
• Ilya Burkov
• Robin Rohm

Reach and audience

Public platform figures. Ratings count people who left a rating, not total listeners.

Apple Podcasts (US)

5.0 / 5

10 ratings

Spotify

4.8 / 5

19 ratings

Podcast Authority Score: 41 / 100

A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.

Quality

49

Social presence

0

YouTube

0

Engagement

82

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Guest appearances

Does not typically book guests

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