Multidimensional Stochastic Process Model and its applications to analysis of longitudinal data with genetic information
| dc.contributor.author | Zhbannikov, I | |
| dc.contributor.author | Arbeev, K | |
| dc.contributor.author | Yashin, A | |
| dc.date.accessioned | 2017-06-05T18:06:37Z | |
| dc.date.available | 2017-06-05T18:06:37Z | |
| dc.date.issued | 2016-10-02 | |
| dc.description.abstract | Copyright 2016 ACM.Stochastic Process Model has many applications in analysis of longitudinal biodemographic data. In general, such data contain various physiological variables (sometimes known as covariates or physiological indices). Longitudinal data can also contain genetic information available for all or a part of participants. Taking advantage from both genetic and non-genetic information can provide future insights into a broad range of processes describing aging-related changes in the organism. In this work, we implemented a multi-dimensional Genetic Stochastic Process Model (GenSPM) in newly developed software tool, an R-package stpm (available from CRAN: https://cran.rproject.org/web/packages/stpm), which allows researchers performing such kind of analysis. | |
| dc.identifier.uri | ||
| dc.publisher | ACM | |
| dc.relation.ispartof | ACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics | |
| dc.relation.isversionof | 10.1145/2975167.2985634 | |
| dc.title | Multidimensional Stochastic Process Model and its applications to analysis of longitudinal data with genetic information | |
| dc.type | Journal article | |
| duke.contributor.orcid | Arbeev, K|0000-0002-4195-7832 | |
| pubs.begin-page | 467 | |
| pubs.end-page | 468 | |
| pubs.organisational-group | Center for Population Health & Aging | |
| pubs.organisational-group | Duke | |
| pubs.organisational-group | Duke Population Research Institute | |
| pubs.organisational-group | Sanford School of Public Policy | |
| pubs.organisational-group | Staff | |
| pubs.publication-status | Published |