Therapeutic Target and Biomarker ID
There are two steps on how to work with the tool:
Filtering and score prioritization area
Main target assessment
Prioritization
PandaOmics scoring approach is based on the combination of multiple scores derived from text and omics data associating genes with a disease of interest. This kind of approach allows us to unveil the hidden hypotheses that might not be obvious over common general knowledge or simple bioinformatics analysis. AI tools are extremely helpful for efficient target hypothesis generation. The overall scoring approach results in the ranked list of target hypothesis for a given disease (or disease subtype).
There are many prioritization strategies you can apply based on your target hunting strategy or therapeutic area, but we recommend you to start with one of the following three (see detailed score description on the corresponding tab):
There are many prioritization strategies you can apply based on your target hunting strategy or therapeutic area, but we recommend you to start with one of the following three (see detailed score description on the corresponding tab):
- Default − all available scores are active and the genes are prioritized by the combination of all the information extracted from OMICs data, AI based scores, external databases, publications, grants and more. This prioritization is active by default when you open the Target ID page.
- Molecular vs. Text Evidence. Text evidence prioritization (Text, Financial and KOL score families) singles out the genes, extensively mentioned across scientific literature and grant description. OMICs-based scores, in contrast, explore the molecular connection of genes with diseases based on differential expression, gene variants, interactome topology, pathways and more.
Filtering
Once the gene list is prioritized according to the required strategy. Druggability filter uses traffic light logic and allows to filter target hypotheses according to their novelty, accessibility by small molecules and biologics and safety. Safety accounts for the involvement of the genes in toxicity related pathways and lethality of the corresponding gene knockout in mice. Additional filters include the protein and mRNA expression tissue specificity and protein class
| Filters |
Switching
on/off
Comparisons
on/off
Comparisons
Switching on/off Comparisons is now allowed in the Target ID interface without reassembling and recalculating the entire Meta-analysis. Just use the new feature of the Target ID dashboard to turn on and off Comparisons or Comparison groups, and get the updated ranking immediately. This feature is specifically useful to compare the target ID outputs calculated using public data only versus the proprietary datasets uploaded by the user. Another example is switching between the Target ID results for different tissues.
