Atom Progress Prediction

AI tool for the future precision medicine and precision health.

Disease Risk Evaluation

Although the blank area of human genetic diversity is now gradually enriched by the research development, quantifying the overall impact toward a disease from massive related variants remains a next-stage issue. We quantified each variant’s impact through the clinical criteria and research significance. By leveraging the capability of variant knowledge graph established by Graphen, the consequence of each disease’s risk are calculated and conjugated from all the connected variants and its digitalized impact.

In: Annotated Mutations in Omics data/ Out: the quantified risk of considered diseases.


Disease Progress

Current genome-based analysis approaches are limited by incomplete biological understanding of the relationship between clinical phenotype and disease genotype. Graph-based models combing with gene/protein associations, biological pathways and clinical labeling data can predict associations between the disease-related pathways and clinical phenotype.

In: Omics files / Out: Disease-related genes and pathways .


Race Differentiation

Although human, as a kind of species, has our own genetics compositions, the genetic divergence still lies between different races, and contributes to how we genetically are in health and medical issues. Graphen collects the genetic compositions in different areas and races globally to deploy the genetic frequency divergence into our genetic variant knowledge graph. By utilizing the information of frequency, we can quantify the divergent outcome of disease risk in different areas and races, precisely understand our intrinsic natures.

In: Annotated Mutations in Omics data / Out: the diverse between areas and races in disease risk.


Drug Resistance

Resistance to treatment with anticancer drugs results from a variety of factors including individual variations in patients and somatic cell genetic differences in tumors, even those from the same tissue of origin. GCN model combing with omics data and treatment labeling data can predict the prognosis before treatment, help patients select their best treatment strategies.

In: Omics files / Out: Disease-related genes and pathways.


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