Identifying High-Priority Proteins Across the Human Diseasome Using Semantic Similarity

Area of Science:

• Biomedical Research
• Computational Biology
• Bioinformatics

Background:

• Identifying key genes and proteins for biological processes and diseases is crucial but often relies on subjective expert evaluation and manual literature review.
• Systematic, objective methods for evaluating protein-disease associations are limited, hindering efficient research.
• Automating the prioritization of proteins for specific research topics is needed.

Purpose of the Study:

• To develop and validate a computational method for objectively identifying and ranking proteins associated with specific research topics and diseases.
• To introduce the weighted copublication distance (WCD) metric for quantifying protein-topic relationships.
• To create a comprehensive human "diseasome" of protein-disease associations.

Main Methods:

• Developed a method to calculate the semantic similarity between proteins and query terms in biomedical literature.
• Incorporated adjustments for the impact and immediacy of associated research articles.
• Calculated the weighted copublication distance (WCD) metric to rank protein-topic relationships.

Main Results:

• The WCD metric demonstrated strong performance in predicting benchmark protein lists across multiple biological processes.
• Prioritized "popular proteins" were extracted for various cell types, anatomical regions, and over 20,000 human disease terms.
• A human "diseasome" of protein-disease associations was generated, enabling reverse queries and functional annotation.

Conclusions:

• The WCD metric provides a robust and objective approach for prioritizing proteins relevant to research topics and diseases.
• The generated "diseasome" facilitates data analysis, hypothesis generation, and the annotation of experimental protein lists.
• This bibliometric approach enhances the understanding of protein functions and their roles in human diseases.