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Dynamic modeling of gene expression data [PDF]

Proceedings of the National Academy of Sciences, 2001
We describe the time evolution of gene expression levels by using a time translational matrix to predict future expression levels of genes based on their expression levels at some initial time. We deduce the time translational matrix for previously published DNA microarray gene expression data sets by modeling them within a linear framework by ...
HOLTER N, MARITAN, AMOS, CIEPLAK M, FEDOROFF NV, BANAVAR JR   +4 more
openaire   +3 more sources

The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases

Molecular Systems Biology, 2020
Modifier genes are believed to account for the clinical variability observed in many Mendelian disorders, but their identification remains challenging due to the limited availability of genomics data from large patient cohorts.
Noam Auslander, Daniel M Ramos, Ivette Zelaya, Hiren Karathia, Thomas O. Crawford, Alejandro A Schäffer, Charlotte J Sumner, Eytan Ruppin   +7 more
doaj   +1 more source

Identification of common oncogenic and early developmental pathways in the ovarian carcinomas controlling by distinct prognostically significant microRNA subsets

BMC Genomics, 2017
Background High-grade serous ovarian carcinoma (HG-SOC) is the dominant tumor histologic type in epithelial ovarian cancers, exhibiting highly aberrant microRNA expression profiles and diverse pathways that collectively determine the disease ...
Vladimir A. Kuznetsov, Zhiqun Tang, Anna V. Ivshina   +2 more
doaj   +1 more source

Seed-based biclustering of gene expression data. [PDF]

PLoS ONE, 2012
BACKGROUND: Accumulated biological research outcomes show that biological functions do not depend on individual genes, but on complex gene networks. Microarray data are widely used to cluster genes according to their expression levels across experimental
Jiyuan An, Alan Wee-Chung Liew, Colleen C Nelson   +2 more
doaj   +1 more source

Time-Course Gene Set Analysis for Longitudinal Gene Expression Data. [PDF]

PLoS Computational Biology, 2015
Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies.
Boris P Hejblum, Jason Skinner, Rodolphe Thiébaut   +2 more
doaj   +1 more source

Techniques for clustering gene expression data [PDF]

Computers in Biology and Medicine, 2008
Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile.
Kerr, Gráinne, Ruskin, Heather J., Crane, Martin, Doolan, Padraig   +3 more
openaire   +4 more sources

Interactive Analysis, Exploration, and Visualization of RNA-Seq Data with SeqCVIBE

Methods and Protocols, 2022
The rise of modern gene expression profiling techniques, such as RNA-Seq, has generated a wealth of high-quality datasets spanning all fields of current biological research.
Efthimios Bothos, Pantelis Hatzis, Panagiotis Moulos   +2 more
doaj   +1 more source

Gene expression data analysis

Microbes and Infection, 2000
Microarrays are one of the latest breakthroughs in experimental molecular biology, which allow monitoring of gene expression for tens of thousands of genes in parallel and are already producing huge amounts of valuable data. Analysis and handling of such data is becoming one of the major bottlenecks in the utilization of the technology.
Brazma, Alvis, Vilo, Jaak
openaire   +3 more sources

sgnesR: An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters

BMC Bioinformatics, 2017
Background sgnesR (Stochastic Gene Network Expression Simulator in R) is an R package that provides an interface to simulate gene expression data from a given gene network using the stochastic simulation algorithm (SSA).
Shailesh Tripathi, Jason Lloyd-Price, Andre Ribeiro, Olli Yli-Harja, Matthias Dehmer, Frank Emmert-Streib   +5 more
doaj   +1 more source

Gene set analysis for longitudinal gene expression data

BMC Bioinformatics, 2011
Background Gene set analysis (GSA) has become a successful tool to interpret gene expression profiles in terms of biological functions, molecular pathways, or genomic locations.
Piepho Hans-Peter, Harrar Solomon W, Bathke Arne C, Wang Haiyan, Zhang Ke, Deng Youping   +5 more
doaj   +1 more source