Until recently, we have collaborated with more than 40 biopharma and biotech companies worldwide, mainly in biomarker discovery, indication selection & expansion, and target prioritization based on omics data from pre-clinical or early clinical studies. 

AIBERT has collected PB-level omics data of cancer genomics, functional genomics, and pharmacogenomics from cancer patients, PDX models, and cell lines. All data have been well curated, cleaned, and normalized. For cancer genomics data, it covers more than 50,000 patients with approximately 70 cancer types. Genome-wide genetic perturbation data from about 2,000 cancer cell lines are also included.

AIBERT will generate comprehensive and even advanced molecular features at gene level, pathway level, omics level, as well as tumor microenvironment level, based on the private omics data from biopharma and biotech companies. The system will robustly identify the features related to drug response as many as possible through the statistical techniques optimized for small sample size scenarios and taking diverse clinical confounding factors into consideration.

AIBERT will assess all the signals detected from private data one by one based on multi-dimensional datasets and different algorithms of distinct rationales. The most consistent signal will be considered as the most promising one and will be recommended to the biopharma and biotech companies for drug R&D decision-making.

The core team members of AIBERT are from world-class institutes, including MD Anderson Cancer Center, John Hopkins University, Princeton University, Chinese Academy of Sciences, Peking University and so forth. Meanwhile these members have very diverse backgrounds from artificial intelligence, statistics, bioinformatics, population genetics, pharmacology and molecular biology. Our world-class talents with diverse backgrounds are driven by a single passion for unprecedentedly accelerating drug R&D process via big data plus AI algorithms.