인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Background Some phenotypical changes may be related to changes in the associations among genes. The set of such associations is referred to as gene interaction (or association) networks. An association network represents the set of associations among genes in a given condition. Given two experimental conditions, Differential network analysis (DNA) algorithms analyse these differences by deriving a novel network representing the differences. Such algorithms receive as input experimental gene-expression data of two different conditions (e.g. healthy vs. diseased), then they derive experimental networks of associations among genes and, finally, they analyse differences among networks using statistical approaches. We explore the possibility to study possible rewiring due to sex factors, differently from classical approaches. Methods We apply DNA methods to evidence possible sex based differences on genes responsible for comorbidities of type 2 diabetes mellitus. Results Our analysis evidences the presence of differential networks in tissues that may explain the difference in the insurgence of comorbidities between males and females. Conclusion Main contributions of this work are (1) the definition of a novel framework of analysis able to shed light on the differences between males and females; (2) the identification of differential networks related to diabetes comorbidities.
#Gene
#Set (abstract data type)
#Diabetes mellitus
#Differential (mechanical device)
#Type 2 diabetes
#Gene regulatory network
#Identification (biology)
#Genetic association
#Phenotype
#Computational biology
#Biology
#Genetics
#Computer science
#Gene expression
#Genotype
#Endocrinology
#Single-nucleotide polymorphism
#Ecology
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