Biomedical subjects

Yingnian Wu

Publications and source records attributed to Yingnian Wu.

2 recordsLinked to original sources

PubMed2007

A random-effects Markov transition model for Poisson-distributed repeated measures with non-ignorable missing values.

In biomedical research with longitudinal designs, missing values due to intermittent non-response or premature withdrawal are usually 'non-ignorable' in the sense that unobserved values are related to the patterns of missingness. By drawing the framework of a shared-parameter mechanism, the process yielding the repeated count measures and the process yielding missing values can be modelled separately, conditionally on a group of shared parameters. For chronic diseases, Markov transition models can be used to study the transitional features of the pathologic processes. In this paper, Markov Chain Monte Carlo algorithms are developed to fit a random-effects Markov transition model for incomplete count repeated measures, within which random effects are shared by the counting process and the missing-data mechanism. Assuming a Poisson distribution for the count measures, the transition probabilities are estimated using a Poisson regression model. The missingness mechanism is modelled with a multinomial-logit regression to calculate the transition probabilities of the missingness indicators. The method is demonstrated using both simulated data sets and a practical data set from a smoking cessation clinical trial.

Algorithms↗

PubMed2005

A high-resolution map of active promoters in the human genome.

In eukaryotic cells, transcription of every protein-coding gene begins with the assembly of an RNA polymerase II preinitiation complex (PIC) on the promoter. The promoters, in conjunction with enhancers, silencers and insulators, define the combinatorial codes that specify gene expression patterns. Our ability to analyse the control logic encoded in the human genome is currently limited by a lack of accurate information regarding the promoters for most genes. Here we describe a genome-wide map of active promoters in human fibroblast cells, determined by experimentally locating the sites of PIC binding throughout the human genome. This map defines 10,567 active promoters corresponding to 6,763 known genes and at least 1,196 un-annotated transcriptional units. Features of the map suggest extensive use of multiple promoters by the human genes and widespread clustering of active promoters in the genome. In addition, examination of the genome-wide expression profile reveals four general classes of promoters that define the transcriptome of the cell. These results provide a global view of the functional relationships among transcriptional machinery, chromatin structure and gene expression in human cells.

Chromatin↗