Bioinformatics approaches for studying molecular sex differences in complex diseases.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-09-23 DOI:10.1093/bib/bbae499
Rebecca Ting Jiin Loo, Mohamed Soudy, Francesco Nasta, Mirco Macchi, Enrico Glaab
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Abstract

Many complex diseases exhibit pronounced sex differences that can affect both the initial risk of developing the disease, as well as clinical disease symptoms, molecular manifestations, disease progression, and the risk of developing comorbidities. Despite this, computational studies of molecular data for complex diseases often treat sex as a confounding variable, aiming to filter out sex-specific effects rather than attempting to interpret them. A more systematic, in-depth exploration of sex-specific disease mechanisms could significantly improve our understanding of pathological and protective processes with sex-dependent profiles. This survey discusses dedicated bioinformatics approaches for the study of molecular sex differences in complex diseases. It highlights that, beyond classical statistical methods, approaches are needed that integrate prior knowledge of relevant hormone signaling interactions, gene regulatory networks, and sex linkage of genes to provide a mechanistic interpretation of sex-dependent alterations in disease. The review examines and compares the advantages, pitfalls and limitations of various conventional statistical and systems-level mechanistic analyses for this purpose, including tailored pathway and network analysis techniques. Overall, this survey highlights the potential of specialized bioinformatics techniques to systematically investigate molecular sex differences in complex diseases, to inform biomarker signature modeling, and to guide more personalized treatment approaches.

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研究复杂疾病分子性别差异的生物信息学方法。
许多复杂疾病都表现出明显的性别差异,这种差异既会影响患病的初始风险,也会影响临床疾病症状、分子表现、疾病进展和患合并症的风险。尽管如此,对复杂疾病分子数据的计算研究往往将性别作为一个混杂变量,旨在过滤掉性别特异性效应,而不是试图解释这些效应。对性别特异性疾病机制进行更系统、更深入的探索,可以大大提高我们对具有性别依赖性的病理和保护过程的理解。本报告讨论了研究复杂疾病分子性别差异的专用生物信息学方法。它强调,除了传统的统计方法外,还需要整合相关激素信号相互作用、基因调控网络和基因性别关联的先验知识的方法,以提供疾病中性别依赖性改变的机理解释。本综述研究并比较了为此目的进行的各种传统统计和系统级机理分析的优势、缺陷和局限性,包括量身定制的通路和网络分析技术。总之,这项调查强调了专业生物信息学技术在系统研究复杂疾病的分子性别差异、为生物标志物特征建模提供信息以及指导更加个性化的治疗方法方面的潜力。
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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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