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Artwork for Data in Biotech

Data in Biotech

CorrDyn
Biotechnology
Clinical Trials
Machine Learning
Drug Discovery
Drug Development
Data Science
Artificial Intelligence
Real World Data
Precision Medicine
Bioanalysis
Generative AI
Pharmaceutical Industry
Bioprocessing
Mass Spectrometry
AI In Drug Discovery
AI Workloads
Data Integration
Data Fragmentation
Life Sciences
Gene Editing

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

PublishesTwice monthlyEpisodes77Founded3 years ago
Number of ListenersCategories
Life SciencesScienceTechnology

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Artwork for Data in Biotech

Latest Episodes

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 ... more

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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. W... more

YouTube

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 th... more

YouTube

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 o... more

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Recent Guests

Erika Kvikstad
Computational biologist focused on health equity and genomic data; former leader in precision medicine for cardiovascular disease at Bristol-Myers Squibb
Bristol-Myers Squibb, Independent researcher
Episode: How to Identify the Blind Spots in Your Biotech's Genomic Data Before They Cost You a Drug Target
Adam Freund
Founder and CEO of Arda Therapeutics
Arda Therapeutics
Episode: How to Turn Single-Cell Data Into a New Class of Cell-Depleting Therapies
John Androsavich
CEO, Ginkgo Datapoints
Ginkgo Datapoints
Episode: Why Biotech Talks About AI But Won't Pay for the Data It Needs
Woody Sherman
A computational chemist with experience at Schrödinger, Silicon Therapeutics, Roivant, and PsiThera
PsiThera
Episode: Beyond Language: Why Drug Discovery Needs Physical AI, Not Just Large Language Models
Paul Finn
Chief Scientific Officer at Oxford Drug Design
Oxford Drug Design
Episode: Synthesizable by Design: Rethinking AI's Role in Small Molecule Drug Discovery
Arvind Rao
Professor of computational medicine and bioinformatics at the University of Michigan
University of Michigan
Episode: From Tissue to Mechanism to Decision: Building AI for Computational Oncology
Sadegh Salehi
Director of Research and Principal Scientist at Overjet
Overjet
Episode: Cavities in the Data: Building FDA-Cleared AI for Dental Imaging with Overjet
Jesse Johnson
Founder of Merelogic
Merelogic
Episode: Data as a Moat: Why Biotech's Most Valuable Asset is Buried in a Hard Drive
Michelle Wiest
Director of IVD Biostatistics at Freenome
Freenome
Episode: Data Science and Diagnostic Models - the What, Why and How with Michelle Wiest

Host

Ross Katz
Principal and Data Science Lead at CorrDyn; host of Data in Biotech

Reviews

4.9 out of 5 stars from 32 ratings
  • A data podcast for biotech

    Data can be messy and it is great to learn from the guests how they use data in biotech for drug development. Ross the host is very knowledgeable himself in data and the conversations are very insightful!

    Apple Podcasts
    5
    Crazyhottommy
    United States3 years ago
  • Great, very accessible podcast

    I’m far from an expert in advanced data and analytics but loved this podcast! Learned a lot and found it to be accessible even to people like me who like data but aren’t deeply technical in the methods of data science. Well done, looking forward to future episodes.

    Apple Podcasts
    5
    Asgrow1333
    United States3 years ago
  • Data in Biotech

    The podcast clearly delineated the myriad of applications for data collection and analysis in biotech. The information was clearly understandable for even a nonprofessional in the field.

    Apple Podcasts
    5
    Lapidii
    United States3 years ago

Listeners Say

Key themes from listener reviews, highlighting what works and what could be improved about the show.

Very accessible even to non-experts in data science; learns a lot.
Data can be messy and it is great to learn from the guests how they use data in biotech for drug development.
Clearly delineates applications for data in biotech and is understandable to nonprofessionals

Chart Rankings

How this podcast ranks in the Apple Podcasts, Spotify and YouTube charts.

Talking Points

Recent interactions between the hosts and their guests.

How to Turn Single-Cell Data Into a New Class of Cell-Depleting Therapies
Q: What does the data infrastructure and discovery platform look like to support this kind of work, and how do you ensure the data from thousands of donors across multiple technologies speaks the same language?
We built a single structured system with rich metadata and a controlled vocabulary so that data objects are discoverable and AI-friendly. We emphasize cross-study correlation, a custom integration approach that preserves cell-type signals while harmonizing datasets, and an emphasis on enabling researchers to query data without always waiting for bioinformatics. This foundation supports rapid iteration from discovery to preclinical validation and, eventually, to the clinic.
How to Turn Single-Cell Data Into a New Class of Cell-Depleting Therapies
Q: Can you talk a little bit about how you think about that problem and why this cell depletion approach is the right therapeutic approach for certain disease types?
The fundamental idea is that cells are the functional units driving disease, so removing the pathogenic cells can be more effective than inhibiting individual pathways. In B cells this has already shown clinical traction; expanding beyond B cells to other immune cells and fibroblasts is feasible by identifying surface markers and leveraging oncology modalities like ADCs, CAR-Ts, and T-cell engagers, guided by a discovery engine that moves from hypothesis-free data to targeted hypotheses and clinically actionable candidates.
Synthesizable by Design: Rethinking AI's Role in Small Molecule Drug Discovery
Q: What methodological unlocks do you see for advancing AI in small-molecule discovery?
Key unlocks include better data preparation and benchmarking against simple baselines, improving chemistry representations (including charge states and tautomerism), and integrating physics-based constraints with learned models to avoid over-reliance on data-hungry methods while preserving predictive power.
Synthesizable by Design: Rethinking AI's Role in Small Molecule Drug Discovery
Q: How does the optimization process balance multiple properties like potency, solubility, and metabolic stability?
The optimization uses a tailored scoring function for each project, weighting properties according to what matters most at that moment, and frequently revisits experimental validation to ensure the model guidance matches real-world performance, since properties often pull in different directions.
Synthesizable by Design: Rethinking AI's Role in Small Molecule Drug Discovery
Q: Can you unpack a little bit about why synthesizability is such a hard constraint and how you think about it in the optimization process?
Synthesizability is hard because generative AI operates on abstract representations of chemical space; making predictions about activity alone ignores the practicalities of making the molecule. By grounding the search in known, reliable reaction schemes and reagents, we constrain the space to what chemists can actually synthesize, which improves the practicality and success rate of downstream testing.

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Frequently Asked Questions About Data in Biotech

What is Data in Biotech about and what kind of topics does it cover?

Data in Biotech explores how data science is applied to advance life sciences, featuring interviews with senior practitioners, researchers, and industry technologists who build data infrastructures, enable AI-driven biotech work, and solve real-world research and manufacturing challenges. Episodes consistently center on data strategy, model development for biotech, regulatory considerations, and practical deployment—often highlighting data collaboration, data quality, governance, and how foundational data assets unlock faster discovery and better decisions. A notable strength is the breadth of guests—from computational chemistry to computational oncology, imaging AI, and platform-level data architectures—giving listeners a cross-cutting vie... more

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1. The a16z Show
2. BioCentury This Week
3. Dwarkesh Podcast
4. Google DeepMind: The Podcast
5. Biotech Hangout

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Data in Biotech launched 3 years ago and published 77 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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What guests have appeared on Data in Biotech?

Recent guests on Data in Biotech include:

1. Erika Kvikstad
2. Adam Freund
3. John Androsavich
4. Woody Sherman
5. Paul Finn
6. Arvind Rao
7. Sadegh Salehi
8. Jesse Johnson

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