PUBLICATIONs

THE LATEST PUBLICATIONS

AACR Poster | Infer Cancer Cell Gene Dependency in Multiple Myeloma Using Causal AI in-silico Patient Model

ASH Poster | Causal AI in silico Patient Model Identifies Minichromosome Maintenance (MCM) Family Genes as Novel Predictors of Overall Survival in Multiple Myeloma

ASCO | PTCH1 and AXIN2 modulation of AR copy number effects on AR gene overexpression in metastatic castration resistant prostate cancer (mCRPC).

The case for AI-driven cancer clinical trials – The efficacy arm in silico

Bayesian Machine Learning on CALGB/SWOG 80405 (Alliance) and PEAK Data Identifies Heterogeneous Landscape of Clinical Predictors of Overall Survival (OS) in Different Populations of Metastatic Colorectal Cancer (mCRC)

Causal modeling of CALGB 80405 (Alliance) identifies network drivers of metastatic colorectal cancer (mCRC)

Accurate Prediction of Clinical Disease Progression in Patients With Advanced Fibrosis Due to NASH using a Bayesian Machine Learning Approach

Machine learning approach to personalized medicine in breast cancer patients: development of data-driven, personalized, causal modeling through identification and understanding of optimal treatments for predicting better disease outcomes

Bayesian network inference modeling identifies TRIB1 as a novel regulator of cell cycle progression and survival in cancer cells

Bayesian Network Models of Multiple Myeloma: Drivers of High Risk and Durable Response