Human Heredity最新文献_第7页

PSO-CFDP: A Particle Swarm Optimization-Based Automatic Density Peaks Clustering Method for Cancer Subtyping PSO-CFDP：一种基于粒子群优化的癌症亚型自动密度峰值聚类方法

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-08-14 DOI: 10.1159/000501481

Xuhui Zhu, J. Shang, Y. Sun, Feng Li, Jin-Xing Liu, Shasha Yuan

{"title":"PSO-CFDP: A Particle Swarm Optimization-Based Automatic Density Peaks Clustering Method for Cancer Subtyping","authors":"Xuhui Zhu, J. Shang, Y. Sun, Feng Li, Jin-Xing Liu, Shasha Yuan","doi":"10.1159/000501481","DOIUrl":"https://doi.org/10.1159/000501481","url":null,"abstract":"Cancer subtyping is of great importance for the prediction, diagnosis, and precise treatment of cancer patients. Many clustering methods have been proposed for cancer subtyping. In 2014, a clustering algorithm named Clustering by Fast Search and Find of Density Peaks (CFDP) was proposed and published in Science, which has been applied to cancer subtyping and achieved attractive results. However, CFDP requires to set two key parameters (cluster centers and cutoff distance) manually, while their optimal values are difficult to be determined. To overcome this limitation, an automatic clustering method named PSO-CFDP is proposed in this paper, in which cluster centers and cutoff distance are automatically determined by running an improved particle swarm optimization (PSO) algorithm multiple times. Experiments using PSO-CFDP, as well as LR-CFDP, STClu, CH-CCFDAC, and CFDP, were performed on four benchmark datasets and two real cancer gene expression datasets. The results show that PSO-CFDP can determine cluster centers and cutoff distance automatically within controllable time/cost and, therefore, improve the accuracy of cancer subtyping.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"84 1","pages":"9 - 20"},"PeriodicalIF":1.8,"publicationDate":"2019-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000501481","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45683405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Front & Back Matter 正面和背面

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-06-01 DOI: 10.1159/000501780

引用次数: 0

Contents Vol. 83, 2017/2018 目录第83卷，2017/2018

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-06-01 DOI: 10.1159/000501251

M. Devoto

引用次数: 0

Front & Back Matter 正面和背面事项

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-05-01 DOI: 10.1159/000501052

W. Kiess, C. Bornehag, C. Gennings

引用次数: 0

Front & Back Matter 正面和背面事项

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-04-01 DOI: 10.1159/000500054

W. Wiersinga, G. Kahaly, V. Blanchette, L. Brandão, V. Breakey, S. Revel-Vilk

引用次数: 0

Author Index/Subject Index, Vol. 83, No. 3, 2017/2018 作者索引/主题索引，第83卷第3期，2017/2018

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-02-01 DOI: 10.1159/000495737

引用次数: 0

Title Page/Table of Contents 标题页/目录

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-02-01 DOI: 10.1159/000495548

J. Wang

引用次数: 0

A Bayesian Hierarchical Framework for Pathway Analysis in Genome-Wide Association Studies. 全基因组关联研究中通路分析的贝叶斯层次框架。

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-01-01 Epub Date: 2020-09-23 DOI: 10.1159/000508664

Lei Zhang, Charalampos Papachristou, Pankaj K Choudhary, Swati Biswas

{"title":"A Bayesian Hierarchical Framework for Pathway Analysis in Genome-Wide Association Studies.","authors":"Lei Zhang, Charalampos Papachristou, Pankaj K Choudhary, Swati Biswas","doi":"10.1159/000508664","DOIUrl":"https://doi.org/10.1159/000508664","url":null,"abstract":"Background: Pathway analysis allows joint consideration of multiple SNPs belonging to multiple genes, which in turn belong to a biologically defined pathway. This type of analysis is usually more powerful than single-SNP analyses for detecting joint effects of variants in a pathway.Methods: We develop a Bayesian hierarchical model by fully modeling the 3-level hierarchy, namely, SNP-gene-pathway that is naturally inherent in the structure of the pathways, unlike the currently used ad hoc ways of combining such information. We model the effects at each level conditional on the effects of the levels preceding them within the generalized linear model framework. To deal with the high dimensionality, we regularize the regression coefficients through an appropriate choice of priors. The model is fit using a combination of iteratively weighted least squares and expectation-maximization algorithms to estimate the posterior modes and their standard errors. A normal approximation is used for inference.Results: We conduct simulations to study the proposed method and find that our method has higher power than some standard approaches in several settings for identifying pathways with multiple modest-sized variants. We illustrate the method by analyzing data from two genome-wide association studies on breast and renal cancers.Conclusion: Our method can be helpful in detecting pathway association.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"84 6","pages":"240-255"},"PeriodicalIF":1.8,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000508664","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38414153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The Role of miR-210 in the Biological System: A Current Overview. miR-210在生物系统中的作用:最新综述

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-01-01 Epub Date: 2020-09-09 DOI: 10.1159/000509280

Xu Hui, Hisham Al-Ward, Fahmi Shaher, Chun-Yang Liu, Ning Liu

引用次数: 8

The Contribution Plot: Decomposition and Graphical Display of the RV Coefficient, with Application to Genetic and Brain Imaging Biomarkers of Alzheimer's Disease. 贡献图:RV系数的分解和图形显示，应用于阿尔茨海默病的遗传和脑成像生物标志物

IF 1.1 4区生物学

Human Heredity Pub Date : 2019-01-01 Epub Date: 2019-08-20 DOI: 10.1159/000501334

JinCheol Choi, Donghuan Lu, Mirza Faisal Beg, Jinko Graham, Brad McNeney

{"title":"The Contribution Plot: Decomposition and Graphical Display of the RV Coefficient, with Application to Genetic and Brain Imaging Biomarkers of Alzheimer's Disease.","authors":"JinCheol Choi, Donghuan Lu, Mirza Faisal Beg, Jinko Graham, Brad McNeney","doi":"10.1159/000501334","DOIUrl":"10.1159/000501334","url":null,"abstract":"Background/aims: Alzheimer's disease (AD) is a chronic neurodegenerative disease that causes memory loss and a decline in cognitive abilities. AD is the sixth leading cause of death in the USA, affecting an estimated 5 million Americans. To assess the association between multiple genetic variants and multiple measurements of structural changes in the brain, a recent study of AD used a multivariate measure of linear dependence, the RV coefficient. The authors decomposed the RV coefficient into contributions from individual variants and displayed these contributions graphically.Methods: We investigate the properties of such a \"contribution plot\" in terms of an underlying linear model, and discuss shrinkage estimation of the components of the plot when the correlation signal may be sparse.Results: The contribution plot is applied to simulated data and to genomic and brain imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).Conclusions: The contribution plot with shrinkage estimation can reveal truly associated explanatory variables.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"84 1","pages":"59-72"},"PeriodicalIF":1.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9008771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48183543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0