Deargen has announced research results on an AI technology Wx that can discover novel biomarkers. Research achievements are being published in the journal of the Nature Scientific Report, and two inventions related to these have been patented.
A biomarker can be understood as a marker that represents biological characteristics or status. If a marker that indicates the mechanism of a particular disease is found, new targets of the drugs can be derived. And if a marker for the condition of the disease is found, it can be used as a diagnostic marker.
In particular, the rapid generation of NGS (Next Generation Sequencing) data and the digitization of patient data have led to an increasing number of attempts to discover new biomarkers as drug and diagnostic targets. In this regard, we are making this biomarker discovery platform using self-developed artificial intelligence technology.
The key to discovering biomarkers is to select the major marker candidates among the many biomarker candidates. Among them, the most core part is to select marker genes among numerous genes. With the advent of RNA-seq technology, it is possible to analyze the entire distribution of genes according to diseases or the results of experiments. Thus, we come to know which genes play a role as biomarkers by analyzing these genetic data.
Until now, we have selected a significant group of gene candidates using a statistical method called differentially expressed gene (DEG). However, this is a mathematical statistics-based method, so this does not reflect the actual pheno-type pattern. As the number of genes and samples increases, so many candidates are pulled out, which makes the selection of candidates difficult. There is also a problem that the significance of the selected gene is unknown except for the statistical p-value. It was confirmed that other feature selection algorithms (MRMR, fisher score, LLL21, SVM etc.) also showed no significant results in the large-scale feature space.
Therefore, we have developed Wx that is a deep learning based feature selection algorithm overcoming these shortcomings. Previously, even though it was learned though the deep learning, it was difficult to analyze outcome and input data because internal networks were considered as a black box. We derived the correlation between input feature and outcome by analyzing weight values inside the training of the deep learning and made it gene and biomarkers be selected by calculating the importance score.