Biomarker Discovery Platform for Cancer Therapeutics
CRO services for collaborative drug development
kinase cascade
Target Discovery
Identify pathways that drive disease progression and its molecular subtypes by learning from data gathered in clinicogenomic cohorts.
Therapeutic (Tx)
immune checkpoint drug
Patient Selection
Predict patient health outcomes based on the genomic biomarkers they exhibit. Match them to clinical trials that target those biomarkers.
Prognostic (Px)
Biomarkers involved in the pathway of a drug's mechanism of action (MOA) can be used to select patients for a clinical trial via in vitro diagnostics (IVD).
Despite average R&D costs of $793M, 90% of cancer clinical trials fail. Are fundamental biomarkers being overlooked?
ASCO 2023: discovery of untapped biomarkers of cancer survival
Study of bladder cancer survival biomarkers
  • Analysis antithetically highlights germline genomics over tumor multi-omics
  • Neural network predicts 5-year overall survival (OS) at 95% accuracy, and ranks the most influential survival biomarkers
OncoGerm — in silico prognostic target discovery technology
analytical workflow
Insight derived from in silico simulations can be used to inform the design of in vitro & in vivo experiments.
Built on a deep learning technology platform
  • AI Quality Control (AIQC) is our open source Python framework for systematic deep learning
  • Rapidly train & evaluate neural networks on multi-modal data without sacrificing scientific rigor
  • Designed to automate reproducibility, explainablity, & data integrity for audits
track experiments
compare models
run simulations
Bult-in UI for real-time experiment tracking
Biology is interactive – computational biology should be too
Association Studies are Univariate
Correlate each mutation with the health outcome one-by-one. Lack of interaction means a separate hypothesis for each mutation. No patient-specific predictions, only population-wide statistics.
gwas simplicity
neural network topology
Neural Networks are Multivariate
Genes interact with each other, as they would in biology, within a unified algorithm. Predict patient-specific health outcomes. Prioritize population-wide biomarkers via permutation.
Stop chasing the latest biostatistics tool written by a graduate student, and embrace the flexibility and power of neural networks
Guidance from scientific & strategic experts
Director – GU Oncology & Phase I Clinical Trials: AdventHealth Cancer Institute
Guru has published and practiced extensively in the field of clinical oncology at multiple cancer institutes. He has served as the principal investigator for several clinical trials, currently directs Advent's phase I program, and is extremely cognizant of the latest developments in the cancer therapeutics industry.
Physician Scientist, Medical Oncologist, Genomics Researcher:
BWH, Dana-Farber, Broad Institute
Arvind has developed comprehensive expertise across quantitative, experimental, and clinical domains at the highest level of excellence. This gives him the rare ability to not only see the whole picture but also zoom in wherever necessary. His foundational research on the role of miRNA in oncogenesis and gene expression refined the known biological mechanisms of this omic.
Vice President – Data Strategy:
Northwell Holdings
Marc has a proven track record of leading the development of enterprise data science initiatives in both clinical and digital health settings at companies such as Optum. Now he is steering the strategy of New York's largest hospital system as they integrate machine learning into the clinic and build a data exchange platform for health networks.
Life Science Executive:
Pfizer, WuXi AppTec
When it comes to the biotech industry, Alex has seen it all. He has managed legal, R&D, and commercial strategy at companies ranging from major pharmaceuticals, contract research organizations (CROs), and growth stage technology startups – his accumen and innate ability to navigate challenging situations is unparalleled.
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