VIRALSENSE
Understanding SARS-CoV-2
Viral Sense is an initiative designed by a duo of data scientists with the aim of building tools to facilitate a better understanding of SARS-CoV-2.
Mission Statement
We believe that by synthesizing the output of large scale data analysis across different domains we better understand the virus and therefore deliver a more effective response.
This data can be at different spatial and temporal scales from genes, protein-protein interactions, clinical diagnoses/treatment to epidemiology.
We are keen to push the needle of discussion among subject matter experts through our analytic tools and to transparently communicate our insight to the public.We aim to foster collaboration through seeding the data with new data sources to help us gain more insight into the determinants, strengths and vulnerabilities of the virus and our approaches to combat it.
DISEASE-SYMPTOM KNOWLEDGE GRAPH
The associations of diseases/conditions with certain symptoms has been the preserve of textbooks and clinical professionals.
We have mined medical databases to find such associations and developed a knowledge graph that can preferentially display viral-symptom associations based on natural language processing techniques.
Reported diseases and symptoms associated with the virus which are reported in the literature are tired to this knowledge graph.
VIRAL LINEAGE
We have used RNA data available through the effort of GISAID (https://www.gisaid.org/) to build a phylogenetic tree of the virus as it has been sampled in the different labs across the world by the number of mutations we see.
We can do look at this on a gene-by-gene basis and look at which parts of the world have certain lineages. We can also tie this to certain demographic features such as age and gender.
LOOKS GREAT
ON ANY DEVICE.
I'm a paragraph. Click here to add your own text and edit me. I’m a great place for you to tell a story and let your users know a little more about you.
