The Technology Foundation Behind SpaceIQ™
PredxBio’s technology is built on a unified decision-intelligence foundation designed to model tissue biology as a structured, multi-scale system. By combining unbiased spatial analytics, multi-modal integration, and explainable AI, this foundation produces reproducible, biologically grounded representations of tissue organization that can be trusted, reused, and scaled.
All SpaceIQ applications are powered by a single analytics foundation
that remains consistent across studies, programs, and deployments.
Biology in tissue is inherently organized across scales. PredxBio’s technology captures this organization explicitly by modeling:
Cell-level phenotypes and functional states
Local neighborhoods and emergent microdomains
Tissue-scale architecture and spatial interactions
This enables the discovery of response-defining patterns that conventional image analysis and reductionist methods often overlook.
Explainability is not an add-on — it is a core design requirement of PredxBio’s technology.
The platform ensures that:
Computational features map to biologically interpretable structures
Model outputs can be inspected, validated, and reasoned about
Insights can be translated into testable biological hypotheses
Results can be reused across trials and programs with confidence
PredxBio’s technology is designed for co-analysis of spatial and molecular data, not parallel or siloed interpretation.
Rather than analyzing each modality independently and reconciling results after the fact, the platform jointly models spatial proteomics, spatial transcriptomics, imaging, and associated clinical data within a unified tissue framework.
This enables:
Cross-modal features that reflect shared biological context
Direct linkage between molecular signals and spatial organization
Consistent biological representations across datasets and technologies
Discovery of emergent patterns that are invisible to parallel analyses
Supported inputs include:
H&E, IHC, mIF, IMC, spatial proteomics
Spatial transcriptomics platforms
Molecular, clinical, and outcome metadata
By treating multi-omic data as co-registered components of a single tissue system, the technology enables deeper biological insight without re-engineering the analytics core for each modality.
PredxBio’s technology is cloud-native and designed to scale from individual studies to enterprise environments. The same core supports discovery research, translational analysis, and longitudinal program learning — enabling insights to compound as data accumulates.
CHIEF EXECUTIVE OFFICER
B. Dusty Majumdar, PhD is a seasoned leader in Precision Healthcare with more than 20 years of experience in building and successfully launching innovative technologies across oncology, multi-omics, Real-World Evidence (RWE). diagnostic imaging and liquid biopsy platforms leveraging some of the industry’s most advanced AI platforms, genomic and clinical simulations (digital twins), and emerging medical imaging technologies. Dr. Majumdar’s experience includes leading strategy and marketing as the Chief Marketing Officer (CMO) at IBM and multiple commercial and technical leadership roles at GE Healthcare, Exact Sciences and 3M. He has also led the commercial and strategic functions in a range of start-ups in the healthcare/biopharma space over the last few years. Dr. Majumdar is a widely respected global leader in the healthcare industry and holds a Ph.D. from the University of Texas at Austin and a bachelor’s degree from the Indian Institute of Technology (IIT), Kharagpur. He has authored several peer-reviewed scientific publications and holds multiple patents.
Sr. VP Business Development
Joe Camaratta specializes in medical technology innovation and commercialization, taking products from concept to clinical adoption. He held executive positions for GE Healthcare and Siemens Healthcare and built businesses in medical imaging, cardiology, and oncology. He founded and led two early stage medical technology companies that apply artificial intelligence to improve clinical decision-making.
Joe holds a Master of Science degree in Computer Science from Rutgers University and a Bachelor of Science degree in Computer Science from Drexel University. He currently serves as an advisor to the Entreprenuerial Investing Program of the Crohn’s & Colitis Foundation, and the University Science Center QED Program focused on commercialization of academic innovations
VP Software Engineering
Bruce leads the software engineering and quality assurance teams at PredxBio. As the VP of Software Engineering, he is responsible for analytical infrastructure, product development and the commercialization of machine learning, image processing, and computational biology algorithms. Prior to joining PredxBio, Bruce was at IBM and Xylem in global Director of Data Science roles. He has over 20 years of experience in digital pathology algorithms and is highly motivated to deliver care through digital and computational pathology. He has multiple successful FDA 510k device applications, and multiple CLIA certified algorithms deployed in clinical practice.
Bruce holds a BS in Physics and MS in Applied Mathematics from the University of Rochester, an MS in Statistics from North Carolina State University, and is currently pursuing graduate mathematics studies at The Ohio State University. Bruce loves the outdoors. You can find him hiking and rock climbing in Appalachia.
