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.

Why treating the cell, not the protein, could turn chronic disease treatment into something closer to a cure.

You've built single-cell pipelines that spit out clusters, p-values and target lists, but nothing that survives contact with the clinic. What if the clustering method itself is quietly leading you astray?

Adam Freund is Founder and CEO of Arda Therapeutics, a biotech using single-cell sequencing to find the pathogenic cells driving chronic disease. He spent seven years as a Principal Investigator at Calico Life Sciences, building a research lab on the biology of ageing and helping grow the company from 15 to more than 200 people, and holds a PhD in Molecular and Cell Biology from UC Berkeley.

You'll get a working model for how Arda's discovery engine turns single-cell and spatial transcriptomic data into causal cell targets. Adam explains why a common statistical shortcut in single-cell analysis produces disease signals that don't hold up and how cell depletion could replace daily dosing with a handful of treatments that reset the immune system.

Ross and Adam cover how Arda finds pathogenic cell populations across hundreds of donors, why chi-squared tests on cell clusters can substitute cell count for donor count without anyone noticing, and how B-cell depletion therapies proved that removing a cell can beat blocking its pathway. This one is for data science leaders and computational biologists building single-cell pipelines, not listeners after a general intro to drug discovery.

Key Takeaways

- Chi-squared tests on cell clusters draw their statistical power from the number of cells, not the number of donors, so a single oversampled patient can produce the same p-value as a hundred-donor study.

- Rituximab clears 100% of B cells from circulation yet does nothing for lupus because the disease-driving cells live in tissue, not blood, a lesson now shaping where Arda tests its own molecules.

- Neighborhood analysis scores each cell by the donor identity of its nearest neighbours rather than forcing cells into predefined clusters, producing a continuous disease-enrichment map with no cluster boundaries.

- When depleted cells regrow, they often come back without the trait that made them harmful in the first place, which means a handful of doses can hold a chronic disease in remission for months.

Chapter Markers

00:00 Why cell depletion beats pathway blocking

01:05 Welcome Adam Freund to the show

01:30 From Calico Life Sciences to founding Arda

03:29 Why blocking one pathway rarely works

05:32 B-cell depletion as the proof of concept

08:14 Building a modular library of depletion tools

10:46 Single-cell sequencing removes the need for a hypothesis

11:43 Why clustering is a dial, not ground truth

15:24 The chi-squared trap in single-cell analysis

20:40 Neighbourhood analysis and donor-weighted scoring

23:44 Moving from enrichment to causality

26:32 Inside Arda's lead fibrosis program

30:33 Why solid tissue testing beats blood samples

34:25 Simulating depletion in spatial transcriptomic data

38:49 The case for intermittent dosing over daily pills

43:58 The data infrastructure behind Arda's platform

48:46 Where spatial and protein data are heading

Useful Links & Resources

- Adam Freund on LinkedIn: https://www.linkedin.com/in/adam-freund-0657654

- CorrDyn: https://corrdyn.com

Connect With the Show

- Host Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/

- Host Ross Katz on X: https://x.com/brosskatz

- CorrDyn on LinkedIn: https://www.linkedin.com/company/corrdyn/

If your team runs single-cell pipelines, how do you currently decide on the number of clusters, and have you ever checked whether your significance scales with donor count rather than cell count? Tell us in the comments; we're building a running list of data QA checks for biotech data science teams.

Visit corrdyn.com to learn how CorrDyn can help your organization extract value from data.

Everyone in biotech agrees AI needs more data. Almost no one is willing to pay for it.

If you're trying to build or buy a biotech AI model, you've hit the same wall: predictive performance depends on data your budget doesn't cover, and nobody in the field seems willing to close that gap.

John Androsavich runs Ginkgo Datapoints, the bio AI data arm of Ginkgo Bioworks. He trained as an RNA scientist, spent years on the pharma side deciding which technologies were worth buying, and now sells the raw biological data everyone claims to want.

Ross and John get into why biotech spends a fraction of what tech spends on data, how automation dropped ADME testing to $199 a compound, and what that unlocks for drug discovery pipelines and data science in biotech more broadly. You'll hear why single-cell foundation models don't scale the way the field expected, and how GPT-5 designed its own lab experiments inside an autonomous facility.

This one's for data and analytics leaders in biotech who need a clearer read on where to spend on data generation, and where the field is still guessing. It's less useful if you're after a general AI overview with no biotech specifics.

Key Takeaways

- One Meta investment in a data-labelling vendor outweighs a full year of AI drug discovery venture funding combined, and dwarfs the entire single-cell data market. Biotech's data spend looks nothing like tech's.

- Ginkgo's ADME-1 offering runs at roughly a tenth of standard pricing, which is changing when and how much companies test. Teams are now running full tier-one panels earlier instead of triaging molecules before they've generated the negative data models need.

- A recent Microsoft Research paper found single-cell foundation model learning saturates at 200,000 to 2 million cells, out of a possible 20 million. Volume alone isn't the lever people assumed it was.

- GPT-5 wrote its own experimental protocols for optimising cell-free protein expression, ran them through Ginkgo's autonomous Nebula lab, and hit the lowest price-per-titer ever recorded in the field.

Chapter Markers

00:00 Introducing John Androsavich and Ginkgo Datapoints

01:12 Why Ginkgo launched a bio AI data business

05:03 Which companies benefit most from Datapoints

06:31 The paradox: everyone wants data, no one pays

09:00 How automation drives ADME-1's $199 price point

12:59 Testing the Jevons paradox in biotech data buying

16:05 Do we actually know biotech AI's scaling laws?

20:54 Why foundation model builders resist more data

24:59 What an empirical bake-off for bio AI could look like

29:32 The case against sitting on the sidelines

33:26 Inside the Virtual Cell Pharmacology Initiative

41:57 Where VCP fits among other virtual cell projects

44:50 The Antibody Developability Consortium with Apheris

53:57 Autonomous labs and GPT-5 designing its own experiments

59:38 Advice for mid-stage biotech data strategy

01:01:31 Final thoughts on where bio AI investment is heading

Useful Links & Resources

- Ginkgo Bioworks: [ginkgobioworks.com](https://www.ginkgobioworks.com)

- Related episode: Apheris CEO Robin Rohm on federated co-folding (Data in Biotech)

- Related episode: Eliza Appel on Lilly's TuneLab and federated learning (Data in Biotech)

- CorrDyn: [corrdyn.com](https://www.corrdyn.com)

Connect With the Show

- Host LinkedIn (Ross Katz): [linkedin.com/in/b-ross-katz](https://www.linkedin.com/in/b-ross-katz/)

- Host X: [x.com/brosskatz](https://x.com/brosskatz)

- CorrDyn LinkedIn: [linkedin.com/company/corrdyn](https://www.linkedin.com/company/corrdyn/)

Where does your organisation sit on the data investment paralysis John describes? Are you waiting for someone else to prove the scaling laws first, or are you buying the data now? Drop your take in the comments.

Visit corrdyn.com to learn how CorrDyn can help your organisation extract value from data.

#DataInBiotech #BiotechAI #DrugDiscovery #DataScience #GinkgoBioworks

In this episode of Data in Biotech, host Ross Katz sits down with Woody Sherman, Founder and Chief Innovation Officer at PsiThera, for a conversation on why AI can transform drug discovery's paperwork and code while barely touching the hardest part of the problem: the molecules themselves. Woody's career runs through physical chemistry at MIT; over a decade at Schrödinger building tools the industry still relies on; founding Silicon Therapeutics (where his team took a small molecule STING agonist from concept to clinic in roughly three years); scaling that platform after Roivant's acquisition; and now leading PsiThera's effort to build oral small molecules for immunology targets that today are only reachable with injectable biologics.

The conversation digs into why large language models excel at automation, coding, and regulatory writing but hit a wall when the task is predicting how a molecule behaves, what "physical AI" actually means as a category distinct from both LLMs and traditional physics-based simulation, and why representing molecules as quantum mechanical objects rather than text strings or 2D graphs changes what's predictable. 

Woody also walks through the STING program in detail, why the field's excitement over fast co-folding models like Boltz needs a strong dose of skepticism, and what it takes to build a database and team culture where chemists, biologists, and data scientists can actually understand each other.

What you'll learn in this episode:

>> Why the contradiction of "AI is transforming drug discovery" and "drugs still take a decade and billions of dollars" can both be true at once.

>> How Silicon Therapeutics engineered a small molecule STING agonist to dimerize itself through a quantum mechanical interaction that had never been designed for before.

>> What "physical AI" means as a new category built on embeddings from orbital-level, quantum mechanical representations of molecules, rather than language tokens or force-field simulations.

>> Why molecular representation is the whole game: the limitations of SMILES strings and 2D graphs versus true 3D, quantum mechanical embeddings like PsiThera's Psiformer model

>> Why a widely publicized claim of near-FEP-quality binding affinity at 1,000x the speed didn't hold up under scrutiny.

>> How PsiThera captures not just simulation and wet lab data but human chemist judgment and reasoning as structured data, and why building a shared vocabulary across computational and experimental teams is as important as any model.

Meet our guest:

Woody Sherman, PhD, is Founder and Chief Innovation Officer at PsiThera, a biotechnology company designing oral small molecule drugs for immunology and inflammatory diseases, starting with the TNF superfamily. His career spans physical chemistry research at MIT, more than a decade at Schrödinger developing computational drug discovery tools, founding Silicon Therapeutics (acquired by Roivant), and leading the platform's evolution through PsiThera today. He has published more than 100 peer-reviewed papers spanning molecular dynamics, quantum mechanics, free energy simulations, and machine learning for drug design.

• Connect with Woody Sherman on LinkedIn: https://www.linkedin.com/in/woodysherman/

About the host:

Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation.

• Connect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/

Sponsored by…

This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. https://www.linkedin.com/company/corrdyn/

In this episode of Data in Biotech, host Ross Katz sits down with Paul Finn, Chief Scientific Officer at Oxford Drug Design, for a conversation on what it actually takes to find a drug molecule that works not just on paper but also in the lab, in the cell, and, ultimately, in the clinic. Paul brings four decades of experience across what became GSK, Pfizer, and a series of Oxford-area spinouts and has shepherded a compound all the way to a marketed drug. That perspective gives him a particular kind of skepticism toward AI results that look too good to be true because he's done the work of checking whether they are.

The conversation moves through synthesizability as a first-class constraint, why chemistry has proven so much harder for AI than biology, how 3D molecular representation gets closer to the physics that actually matters, and what rigorous multi-parameter optimization looks like when you're trying to kill cancer cells and drug-resistant bacteria at the same time.

What you'll learn in this episode:

>> Why synthesizability is chronically underestimated and why changing a single atom in a structure can take a molecule from trivially easy to make to practically impossible

>> How Oxford Drug Design constrains the generative search to reaction schemes and purchasable building blocks, and why that chemical space is still so vast that novelty is not meaningfully sacrificed

>> Why most generative AI models learn from a 2D string representation of a molecule; two steps removed from the 3D physics that govern how a drug actually binds to its target

>> How Bayesian optimization over reagent space, rather than molecular space, allows an active learning loop to focus on the structural patterns associated with activity

>> Why benchmarking complex models against simple ones is the discipline that exposes false correlations and why Paul and his co-authors were able to recover the Halicin result using methods decades older than deep learning

>> What a pharma company should actually ask an AI drug discovery vendor before buying what they're selling

Meet our guest:

Paul Finn is Chief Scientific Officer at Oxford Drug Design, a computational drug discovery company with roots in Oxford's chemistry department. His career spans over 40 years of computational drug discovery, from early structure-activity modeling in the 1980s through to modern generative AI methods, with deep experience at what became GSK and Pfizer before moving into the Oxford spinout ecosystem. At Oxford Drug Design, Paul leads internal programs in oncology and antibacterial resistance, combining novel computational methods with a rigorous, synthesizability-first approach to multi-parameter optimization.

• Connect with Paul Finn on LinkedIn: https://uk.linkedin.com/in/paul-finn-2250616

About the host:

Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation.

• Connect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/

Connect with us:

• Follow the podcast for more insightful discussions on the latest in biotech and data science.
• Subscribe and leave a review if you enjoyed this episode!

Sponsored by…

This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. https://www.linkedin.com/company/corrdyn/

In this episode of Data in Biotech, host Ross Katz sits down with Arvind Rao, Professor of Computational Medicine and Bioinformatics at the University of Michigan, for a discussion on the gap between what biomedical AI can do and what it can reliably be trusted to do in clinical practice. Arvind's research sits at the intersection of computational oncology and AI governance and his lab works across H&E histopathology, multiplex immunofluorescence, spatial transcriptomics, and single-cell RNA sequencing, not just to build predictive models, but to understand the full lifecycle from data to model to inference, and to ask where that lifecycle can be trusted and where it can't. 

The conversation moves through two of his recent papers on SPIFEE, a graph-based framework that replaces scalar interaction scores in the tumor microenvironment with spatially resolved functional representations, and a multimodal framework that traces a path from stained tissue slides to nominated drug targets via morphological pattern discovery and spatial transcriptomic mapping. 

What you’ll learn in this episode: 

>> Why the field's central failure is not algorithmic but translational and the gap between a model that performs well on a benchmark and one that can be consistently trusted in a high-stakes clinical setting 

>> How SPIFEE replaces the conventional scalar edge representation of cell-cell interactions in the tumor microenvironment with spatially resolved functional edges

>> How Arvind's multimodal framework moves from H&E pathology slides labeled with clinical outcomes, through morphological pattern discovery via multiple instance learning, to spatial transcriptomic mapping, to the nomination of molecular mechanisms and actionable drug targets

>> Why Goodhart's Law applies directly to foundation model evaluation in biology 

>> What the AI literacy gap costs when it goes unaddressed in healthcare and pharma organizations 

Meet our guest:

Arvind Rao is a Professor of Computational Medicine and Bioinformatics, with a joint appointment in Radiation Oncology, at the University of Michigan. His research focuses on establishing trust in biomedical AI predictions across the full data-to-decision pipeline, integrating H&E histopathology, spatial transcriptomics, multiplex immunofluorescence, and single-cell RNA sequencing to build models that are predictive, interpretable, and biologically credible. Alongside his research, Arvind develops AI literacy programs for healthcare and pharma professionals, helping clinical and procurement teams evaluate and govern AI systems with the rigor those decisions demand.

• Connect with Arvind Rao on LinkedIn: https://www.linkedin.com/in/arvind-rao-3301301ba/

About the host:

Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation.

• Connect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/

Connect with us:

• Follow the podcast for more insightful discussions on the latest in biotech and data science.
• Subscribe and leave a review if you enjoyed this episode!

Sponsored by…

This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. https://www.linkedin.com/company/corrdyn/

In this episode of Data in Biotech, host Ross Katz sits down with Sadegh Salehi, Director of Research and Principal Scientist at Overjet, to explore what rigorous model evaluation actually looks like when the stakes are clinical. 

Overjet builds FDA-cleared vision models that detect and quantify dental disease across billions of X-ray images from thousands of practices - a data problem with a staggering number of dimensions. Thirty-two teeth per adult patient, each with different morphology. Multiple image types capturing different anatomy. Fifteen to twenty sensor manufacturers producing perceptually distinct images, each with different contrast, resolution, and noise characteristics. And disease severity distributions ranging from barely visible early-stage decay to obvious pathology. 

Sadegh walks through what it takes to evaluate models responsibly across all of those dimensions and discusses why aggregate metrics like F1 score can mask catastrophic failures on specific subgroups, how models find and exploit shortcuts in training data, and why the same flawed sampling that creates gaps in your training set also creates them in your test set. 

He also traces Overjet's architectural evolution from over twenty narrow task-specific models to a single foundation model they call Unity, explains how treatment plan procedure codes provide a noisy but real production feedback signal, and describes how Overjet became one of the first companies to secure the FDA's Predetermined Change Control Plan (a framework that allows model updates without filing a new clearance each time.)

What you’ll learn in this episode: 

>> Why aggregate evaluation metrics are insufficient for high-stakes medical AI 

>> How models exploit shortcuts in training data: if all images from a rare sensor in the training set happen to be healthy, the model doesn't learn to read that sensor, it learns that the sensor means healthy, bypassing the visual task entirely and producing systematic false negatives in production

>> How Overjet evolved from over twenty narrow, sensor-specific and indication-specific models into a single foundation model called Unity, using noisy labels generated by the small models as the training signal for a much larger backbone, then building independent prediction heads for each clinical indication on top of it

>> Why the decision to keep prediction heads architecturally independent from one another was driven as much by FDA regulatory strategy as by modeling considerations

>> How Overjet uses dental treatment plan procedure codes as a production monitoring signal

Meet our guest:

Sadegh Salehi is Director of Research and Principal Scientist at Overjet, where he leads the team responsible for building, evaluating, and deploying FDA-cleared vision models for dental disease detection and quantification. 

• Connect with Sadegh Salehi on LinkedIn: https://www.linkedin.com/in/sadegh-salehi/

About the host:

Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation.

• Connect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/

Connect with us:

• Follow the podcast for more insightful discussions on the latest in biotech and data science.
• Subscribe and leave a review if you enjoyed this episode!

Sponsored by…

This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. https://www.linkedin.com/company/corrdyn/

In this episode of Data in Biotech, host Ross Katz sits down with Jesse Johnson, founder of Merelogic, a software consulting firm specializing in data infrastructure for biotech organizations. 

Jesse brings a rare perspective to the conversation: having built data systems at Google where engineers control the data collection function end to end, before moving into biotech, where the biology does what it wants and bench scientists, not engineers, generate the data. 

The result is a grounded, pragmatic take on one of the most consequential and underappreciated questions in life sciences right now: as bio foundation models fundamentally change the value equation for experimental data, are biotech labs structured to capture that value? 

Jesse argues the answer is usually no and that the fix is less technical than most assume. It doesn't require a production-grade data pipeline or a cloud architecture. It requires lightweight, human-readable standard operating procedures, clear expectations between computational and wet lab teams, and a data strategy designed not just for the questions you're asking today, but for the ones you don't yet know you'll need to ask.

What you’ll learn in this episode: 

>> Why the transition from tech to biotech requires a fundamental reset of assumptions about data infrastructure and why the biggest difference isn't technical, it's organizational.

>> How bio foundation models have flipped the value equation for experimental data by reducing the cost of organizing it while dramatically increasing the potential return

>> How the strategic value of proprietary data is evolving in the biotech ecosystem, from Tahoe Therapeutics building an acquirable single-cell dataset to Eli Lilly's Lowe lab using data as currency for partnerships 

>> Why electronic lab notebooks aren't going anywhere and how the real question facing biotech software teams isn't whether to use an ELN, but how to balance schema rigidity against the flexibility required for the long tail of one-off exploratory assays that no automation pipeline will ever fully capture

Meet our guest:

Jesse Johnson is the founder of Merelogic, a software consulting firm that works with biotech and biopharma organizations on data infrastructure and data operations strategy. Jesse writes regularly about data strategy for biotech on his Substack, covering topics from bio foundation model adoption to the evolving role of electronic lab notebooks in an AI-augmented research environment.

• Connect with Jesse Johnson on LinkedIn: https://www.linkedin.com/in/jesse-johnson-biotech/ 
• Follow Merelogic on Linkedin: https://www.linkedin.com/company/merelogic/

About the host:

Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation.

• Connect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/

Connect with us:

• Follow the podcast for more insightful discussions on the latest in biotech and data science.
• Subscribe and leave a review if you enjoyed this episode!

Sponsored by…

This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn.

This week, we're delighted to be joined by Markus Gershater, Chief Scientific Officer and CoFounder of Synthace - a digital experiment platform built for high-performance life science R&D teams to help them run more powerful experiments and accelerate scientific progress. 

Host Ross Katz speaks with Markus on what’s broken about:

• What’s broken about how laboratories across the globe run biological experiments at scale
• The opportunity that exists for researchers in striving to achieve experimental design automation processes
• Why, as an industry, we must move towards implementing infrastructure that enables multifactorial experiments versus one-factor experiments
• And the role of AI in making sense of complex systems. 

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If you’re a biotech company struggling to transform your business with data, CorrDyn can help.

Whether you need to supplement existing technology teams with specialist expertise or launch a data program that lays the groundwork for future internal hires, you can partner with Corrdyn to unlock the potential of your business data - today. 

Visit connect.corrdyn.com/biotech to learn more. 

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Data in Biotech is a fortnightly podcast exploring how companies leverage data to drive innovation in life sciences. 

This week, we're delighted to be joined by Michelle Wiest, Director of IVD Biostatistics at Freenome - a high-growth biotech company that creates tools to help prevent, detect, and treat disease.

Host Ross Katz speaks with Michelle on the use of biostatistics in the field of diagnostics, what biases can corrupt diagnostic tests and how to catch them early, the different types of data sets that are being used to develop diagnostic models and how to prepare to present data to regulatory bodies such as the FDA.

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If you’re a biotech company struggling to transform your business with data, CorrDyn can help.

Whether you need to supplement existing technology teams with specialist expertise or launch a data program that lays the groundwork for future internal hires, you can partner with Corrdyn to unlock the potential of your business data - today. 

Visit connect.corrdyn.com/biotech to learn more. 

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Data in Biotech is a fortnightly podcast exploring how companies leverage data to drive innovation in life sciences.