An extensively validated deep learning platform optimized for the discovery of clinical diagnostic tests from multiple technologies, including genomics, transcriptomics, proteomics, and radiomics.
We combine blood-based biological information related to the tumor, immune response, and host-status with clinical and radiomic data as inputs for our proprietary AI platform, which enables us to interpret the holistic disease state of each patient or clinical dataset we encounter. We continuously incorporate new market insights and patient data to enhance our platform through a data-driven learning loop.
Researchers commonly encounter issues with machine learning-based biological discoveries that cannot be repeated or validated when assessed in additional specimen cohorts. This challenge, commonly referred to as overfitting, occurs when the machine identifies a perfect pattern in an initial training dataset but is unable to identify the same pattern in a new dataset.
We believe our platform overcomes standard machine learning challenges faced in life sciences research. For over 15 years we have focused on developing proprietary computational techniques to ensure each diagnostic test that is discovered with the Diagnostic Cortex can be further developed to perform consistently in the clinical testing environment.
Publications:
RODER, H ET AL. BMC BIOINFORMATICS (2019) 20:273
RODER, J ET AL. BMC BIOINFORMATICS (2019) 20:325
A test developed for anti-PD-1 therapy in second-line non-small cell lung cancer that identifies a group of patients that likely won’t demonstrate long term benefit from nivolumab and is likely predictive for nivolumab vs. docetaxel.
Source: Goldberg 2017 (SITC poster)
WEBER, J ET AL. CANCER IMMUNOL RES 2018
KOWANETZ, M ET AL. SITC 2018 POSTER
MULLER, M ET AL. CLIN CANCER RES 2020
GRIGORIEVA, J ET AL. INT J MOL SCI 2020