ESPD: a pattern detection model underlying gene expression profiles
نویسندگان
چکیده
منابع مشابه
ESPD: a pattern detection model underlying gene expression profiles
MOTIVATION DNA arrays permit rapid, large-scale screening for patterns of gene expression and simultaneously yield the expression levels of thousands of genes for samples. The number of samples is usually limited, and such datasets are very sparse in high-dimensional gene space. Furthermore, most of the genes collected may not necessarily be of interest and uncertainty about which genes are rel...
متن کاملFundamental patterns underlying gene expression profiles: simplicity from complexity.
Analysis of previously published sets of DNA microarray gene expression data by singular value decomposition has uncovered underlying patterns or "characteristic modes" in their temporal profiles. These patterns contribute unequally to the structure of the expression profiles. Moreover, the essential features of a given set of expression profiles are captured using just a small number of charac...
متن کاملA fully Bayesian model to cluster gene-expression profiles
MOTIVATION With cDNA or oligonucleotide chips, gene-expression levels of essentially all genes in a genome can be simultaneously monitored over a time-course or under different experimental conditions. After proper normalization of the data, genes are often classified into co-expressed classes (clusters) to identify subgroups of genes that share common regulatory elements, a common function or ...
متن کاملStatistical detection of short periodic gene expression time series profiles
Many cellular processes exhibit periodic behaviors. Hence, one of the important tasks in gene expression data analysis is to detect subset of genes that exhibit cyclicity or periodicity in their gene expression time series profiles. Unfortunately, gene expression time series profiles are usually of very short length and highly contaminated with noise. This makes detection of periodic profiles a...
متن کاملPattern-Based Gene-Set Recognition for Interpreting Genome-Wide Gene Expression Profiles
Background: Accurate recognition of important gene sets from genome-wide gene expression profiles provides great insights into the underlying biological mechanisms that govern the gene expression dynamics. However, most gene set recognition algorithms rely solely on supervised sample phenotypic information, overlooking the unsupervised gene-gene expression correlations that are inherently infor...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bioinformatics
سال: 2004
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btg486