DEARGEN - Model WX - Target Discovery Through Genome Wide Feature Data Analysis

Target Discovery through Genome Wide Feature Data Analysis

WX presents highly accurate biomarkers by selecting a succinct set of genes that are most specific to disease from raw transcriptome data. Based on GWX (Gene WX) that is our own deep-learning algorithm, WX presents candidate genes that are the most likely to be chosen as biomarkers by recognizing differences in gene expression between a selected experimental and control group and calculating the scores of the importance. WX features highly accurate results increased by using the entire genome data instead of DEG data that is filtered by the difference in gene expression. By using the WX, target genes were currently selected from many pipelines such as Alzheimer ’s Disease, Rheumatism, Amyotrophic lateral sclerosis (ALS), and sarcopenia, etc.

Prognostic Biomarkers Prediction

Prognostic biomarkers are an important concept for diagnosing and treating disease. Predicting prognostic biomarkers enables medical treatments personalized to each patient`s characteristics more in the strategy establishment for diagnosis and treatment of disease. We present a highly accurate prognostic biomarker by more accurately categorizing a high-risk and low-risk group among the relevant disease patients and calculating the importance of genome expression through Casexed Wx (CWx) that is own deep learning algorithm.

MOA (Mode Of Action) Prediction

In drug development, it is important to identify the mode of action (MOA). Drug development pipelines with an unclear MOA are not only costly in clinical trials but are also difficult to obtain approvals. DEARGEN predicts GO terms with a high priority by building Gene Ontology(GO) terms-based Neural Network Model and MOA by analyzing the weight of the Neural Network.