Bias

Researchers have concentrated much of their attention on a limited number of disease-associated proteins to develop life-changing therapeutics. However, this research bias overlooks many important therapeutic targets yet to be explored. Here we provide a user friendly tool that aggregates data from several databases to inspire the investigation of understudied but biomedically important proteins. Our method combines literature analysis [Pubmed], protein interaction data [STRING], and diverse sources of clinical, genetic, and molecular information to evaluate both how well a protein has been studied and its potential biomedical importance [Mutation, CNA, Expression, Gene-Disease].

Pubmed Link for Paper

GitLab Repo

Criteria to Select Understudied Proteins

Maximun number of publications from PubMed :

Protein-Protein Interactions - STRING

Experimental Scores

Combined Scores

Maximun Number of Interactions:

Criteria for Biomedical Importance

Threshold

Select the percentage of the top ranking hits to display.

Gene-Disease Association

Information on Gene-Disease Associations from the MalaCards database.

Mutation

Cancer related mutational data from cBioPortal.

Copy Number Alteration

Cancer related Copy Number Alteration from cBioPortal.

Expression

Information on cancer expression changes from databases TCGA and GTEx.

List of CBioPortal Studies Unselect - Select All

Exclude Low Sample Studies

Number :

Protein Structure Classification - CATH-GENE3D

Enter CATH_GENE3D id

CATH_GENE3D

Enter ALL or a CATH_GENE3D identifier. Example: 3.30.160.20

Functional Analysis - INTERPRO

Enter INTERPRO id

INTERPRO id

Enter ALL or a unique InterPro identifier. Example:IPR008972

ORB

Overcoming Research Bias
Identifying biomedically important but understudied proteins