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

박동선|김성환
뇌·AI기반교육연구소 (Institute of Brain·AI based Education)|2023.12|Brain, Digital, & Learning|제13권 제4호|pp. 415-451

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참고문헌

키워드

keyword searchgene ontologyGene prioritizationnetwork propagationprotein–protein interaction network

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.

Abstract

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.

참고문헌 (42)

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