Keyword-based Scoring for The Prioritization of Differentially Expressed Genes Using Gene Ontology Annotations and Network Propagation

• 발행기관: 한국교원대학교 뇌·AI기반교육연구소
• 저자명: 김성환 박동선
• 간행물 정보: 『Brain, Digital, & Learning』제13권 제4호, 415~451쪽, 전체 37쪽
• 주제분류: 사회과학 > 교육학
• 파일형태: PDF
• 발행일자: 2023.12.31

국문 초록

Computational gene prioritization provides a basis for the utilization of high-throughputexpression data; however, methods for prioritization considering relevance to both biologicalprocesses and selected keywords are lacking. In this study, a method was developed torank differentially expressed genes (DEGs) by utilizing the Gene Ontology (GO) database forkeyword searches and propagating the results through a protein–protein interaction network. A scoring system that effectively biases the scores of genes relevant to given keywords foravoidance and preference was established. This scoring method was combined with scoringbased on expression characteristic groups (ECGs) with network propagation to obtain a finalcombined score (cScore). The performance of the new approach was evaluated using a ratmiddle cerebral artery occlusion (MCAO) dataset, revealing that the method more effectivelyfiltered out DEGs compared with conventional methods based on both significance and foldchange values, excluding 76% of genes in average, while retaining genes of interest. Furtherimprovements, including addressing the inability of downward accumulation to balanceterms with conflicting hits and the limited number of databases utilized for scoring, canfurther improve the performance of the method. Overall, the newly developed method canimprove the interpretation of DEG data.

목차

Introduction

Materials and Methods

Results

Discussions

Conclusions and Implications

Conflicts of Interest

References

키워드

Gene prioritization network propagation gene ontology protein–protein interaction network keyword search