Dr. Nathan Hotaling and Team!

Senior Data Scientist - National Center for Advancing Translational Science -

National Institutes of Health

Senior VP of Data Science - Axle Informatics

2023-12-05

Needs for Diverse Translational 

Biomedical Interoperable 

Computational Tools
The Team!

Andrew

Samee

Sunny

Nick

Kevin Andre Jesse

Arjun Kristen Lisa Steve Taylor Brandon Nazanin

Jake Phil

Antoine Brian Chris Tim

Bengt Camilo

Najib Nina

Hamdah

Aaron Josue Konstantin Nikita Rachel Simo Swazoo Vlad Xi

Aditi Akshat John Noelia

Kevin Mounika

Nathan
Foundational Problem

How can a scientist harness 

petabytes of data to derive 

quantitative measurements and 

gain meaningful insights?
Polus Platform - Open Source Adobe Creative 

Cloud... for Science

Constant Processing Time For Increasing Data Sizes

• Web-based solution to access cloud/server/ HPC computational and storage resources

Collaborative, Visual, and Interactive

• Web based UI; Simple point-click interface

Traceable and Reproducible Science

• Environment, packages, functions, etc. all recorded automatically; automated hyperlinked 

provenance to raw data

Analytical

• Validated measurements/algorithms; Supports latest in machine learning, deep learning, and 

statistics

Flexible and Modular 

• Open source Plugins/Applications/Codebase; Interoperable container-based. Shared 

Metadata, Storage, and AuthN/AuthZ

Secure

• Private research, patient data/PII, and IP is securely and safely stored 
Diverse Biomedical Data & Tooling

Began with Imaging

• Microscopy - light, fluorescent, electron, pathology, high content 

screening 

• Clinical - Fundus images (RGB, fluorescent), X-Rays, CTs, MRIs, 

OCTs, etc.

Expanded Out! 

• Cheminformatics, molecular modeling, real world evidence 

(electronic health records, insurance/payer claims, mortality, etc.) 

On the Horizon

• Omics - genomics, transcriptomics, proteomics, metabolomics, 

etc.
Diverse Tool Needs

Language Support

• Python, Java, R, C/C++, and Rust

Diverse Libraries 

• Traditional, Tensorflow, Pytorch, Jax, etc.

• Diverse driver requirements i.e. CUDA 

Diverse Hardware

• CPU - x86 and ARM

• AVX/AVX2

• GPU - Nvidia

• Historical code written across multiple 

Nvidia compute capabilities

• RAM - 8GB → ∞
Diverse Users Needs

Programmers

• SDKs and libraries

Technical

• Command line tools & terminal

• Direct edit YAML/JSON

Advanced Domain Experts

• Complex GUIs

• Configure/optimize hyperparameters

• Some "Macro" scripting

Domain Experts

• Simple GUIs

• Single button if possible!
Diverse Data Sizes and Scaling Needs

Dataset Scales Processed

• < 1GB - Text and image data

• > 100s of TBs - Imaging

Scaling Paradigms

• Split on data

• Wells of plates in HCS

• Molecules binding to proteins

• Scale on computation

• Molecular modeling

• Feature extraction

• EHR/Claims extraction, transformation, 

loading, filtering, modeling
Interoperable Analysis Tools

100+ Scalable Interoperable Tools!

● Background correction, Table Merge, etc.

Deep Learning Training/Inference Models

● Point/click interface for training & inference

● Over 400+ classification networks 

● Over 4200+ segmentation networks (Video→)

Custom Deep Learning Models 

● Image segmentation models - MESMER, CellPose, 

Spline/Star-Dist, etc.

● LLMs - Llama 2s, Mistral, Falcon, etc.

● Chemi-informatic - MolGAN, diffDock, etc.

● MM - OnionNet-pose, AlphaFold-2, BioExcel/BioBB, etc.

Trained/Implemented

● 4000+ image segmentation models on diverse images
Questions?