Individual-specific edge-network analysis for disease prediction

Yu, Xiangtian; Zhang, Jingsong; Sun, Shaoyan; Zhou, Xin; Zeng, Tao; Chen, Luonan

doi:10.1093/nar/gkx787

Cited by 69 publications

(49 citation statements)

References 43 publications

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“…Changes in ambulation and activities of daily living (ADL) have been used to predict the last 3 years of life . Other examples include attempts to identify indicators through functional analysis and descriptions of pathways that predict a disease state and using omics data to identify pre‐disease states and predict disease progression . Exactly four distress symptoms were found that were clinically and statistically significant for predicting the point of crisis for caregivers of people with dementia…”

Section: Resultsmentioning

confidence: 99%

“Tipping point” concept analysis in the family caregiving context

et al. 2019

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Aim Analyze the concept “tipping point” in the older adult family caregiving context to further knowledge about caregiving families, enhancing transdisciplinary theory, research, and practice. Background While used commonly in some disciplines, how “tipping point” has been used in health care, generally, and in relation to caregiving families, specifically, is less clear. This project was conducted to offer conceptual clarity to tipping point. Design Walker and Avant's framework. Data Source Searches of scholarly literature in PsycINFO, CINAHL, and PubMed using the search term “tipping point” in either title or abstract. Review Methods Definitions used were extracted; instances when the concept was implied but the actual term “tipping point” was not used and contexts where the term was used or implied were identified. Results The composite definition of a caregiving tipping point is a seemingly abrupt, severe, and absolute change event involving either the older adult or caregiver(s), or both that indicates a breakdown in the status quo of the caregiving system. Conclusions Transdisciplinary research, care, and policy should treat caregiving families as complex systems, use longitudinal assessments, and include colloquial communication. Early detection of impending tipping points will provide family‐centered decisional support and enhance families’ quality of life and safety.

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Section: Resultsmentioning

confidence: 99%

“Tipping point” concept analysis in the family caregiving context

et al. 2019

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“…Gene expression signatures, easily measured from a tissue sample using high-throughput assays, have been used as a means of stratifying breast cancer samples. This has resulted in computational methods that identify personalized "driver mutation" genes [19], differentially expressed genes and pathways [34], and individualized gene networks [35]. Although genes have been used successfully as biomarkers for cancer prediction tasks [18], it is not clear that gene biomarkers are the most appropriate substrate for classification.…”

Section: Introductionmentioning

confidence: 99%

PAN: Personalized Annotation-based Networks for the Prediction of Breast Cancer Relapse

Nguyen

Lee

Quinn

et al. 2019

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The classification of clinical samples based on gene expression data is an important part of precision medicine. However, it has proved difficult to accurately predict survival outcomes and treatment responses, despite advancements in the field. In this manuscript, we show how transforming gene expression data into a set of personalized (sample-specific) networks can allow us to harness existing graphbased methods to improve classifier performance. Existing approaches to personalized gene networks, based on protein-protein interactions (PPI) or population-level models, all have the limitation that they depend on other samples in the data and must get re-computed whenever a new sample is introduced. Here, we propose a novel method, called Personalized Annotation-based Networks (PAN), that avoids this limitation by using curated annotation databases to transform gene expression data into a graph. These databases organize genes into overlapping gene sets, called annotations, that we use to build a network where nodes represent functional terms and edges represent the similarity between them. Unlike competing methods, PANs are calculated for each sample independent of the population, making it a more general solution to the single-sample network problem. Using three breast cancer datasets as a case study (METABRIC and a super-set of GEO studies), we show that PAN classifiers not only predict cancer relapse better than gene features alone, but also outperform PPI and population-level graph-based classifiers. This work demonstrates the practical advantages of graph-based classification for high-dimensional genomic data, while offering a new approach to making sample-specific networks.

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“…但是, 这两个基于基因组学的进化生物学研究新方向或学科还没有能准确反映宏观进化和微观进化的整合, 没有精准瞄准多姿多彩生物性状(phenotype)演化的遗传进化基础(genotype)这一核心问题. 有鉴于此, 我们在2014年提出了进化系统生物学(evolutionary genotype-phenotype systems biology, eGPS)的新方向, 并得到了中国科学院"战略性先导专项"- [14,15] , 找出关键的遗传调控基因标记和通路 [16] , 通过 1 反刍动物的进化反刍亚目是大型陆地哺乳动物中最成功、最大的一类, 分为6个科: 鼷鹿科(Tragulidae)、长颈鹿科(Giraffidae)、叉角羚科(Antilocapridae)、麝科(Moschidae)、鹿科(Cervidae)和牛科(Bovidae) [28,29] . 反刍动物物种丰富, 包含了超过200种现存的物种 [30] , 其中牛科的物种最为丰富, 有140多种 [31,32] ; 而且地理分布非常广泛, 分布于不同的纬度范围(从热带到北极寒带)、不同的海拔地区(从平原到高原)、不同的生态环境(从沙漠到雨林).…”

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