Results 41 to 50 of about 3,887,902 (293)
A model for gene deregulation detection using expression data [PDF]
, 2015 In tumoral cells, gene regulation mechanisms are severely altered, and these modifications in the regulations may be characteristic of different subtypes of cancer. However, these alterations do not necessarily induce differential expressions between the
Birmelé, Etienne +5 more
core +5 more sources
, 2008 This report contains a gene expression summary of the area postrema (AP), derived from the "Allen Brain Atlas":http://www.brain-map.org/welcome.do;jsessionid=EDE40ADC940845D169DE378ADC9B71BD (ABA) in-situ hybridization (ISH) mouse data set ...
Allen Institute for Brain Science +3 more
core +2 more sources
GEDAS – Gene Expression Data Analysis Suite [PDF]
Bioinformation, 2006 Currently available micro-array gene expression data analysis tools lack standardization at various levels. We developed GEDAS (gene expression data analysis suite) to bring various tools and techniques in one system. It also provides a number of other features such as a large collection of distance measures and pre-processing techniques.
Tangirala Venkateswara, Prasad +2 more
openaire +2 more sources
VIGLA-M: visual gene expression data analytics
BMC Bioinformatics, 2019 Background The analysis of gene expression levels is used in many clinical studies to know how patients evolve or to find new genetic biomarkers that could help in clinical decision making.
Ismael Navas-Delgado +6 more
doaj +1 more source
Long non-coding RNA expression profiling in the NCI60 cancer cell line panel using high-throughput RT-qPCR [PDF]
, 2016 Long non-coding RNAs (lncRNAs) form a new class of RNA molecules implicated in various aspects of protein coding gene expression regulation. To study lncRNAs in cancer, we generated expression profiles for 1707 human lncRNAs in the NCI60 cancer cell line
Derveaux, Stefaan +5 more
core +2 more sources
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 +4 more
openaire +3 more sources
Frontiers in Genetics, 2023 Many clustering techniques have been proposed to group genes based on gene expression data. Among these methods, semi-supervised clustering techniques aim to improve clustering performance by incorporating supervisory information in the form of pairwise ...
Zeyuan Wang +4 more
doaj +1 more source
Synergistic drug combinations from electronic health records and gene expression. [PDF]
, 2016 ObjectiveUsing electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including ...
Chen, William +19 more
core +2 more sources
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 +3 more
openaire +3 more sources
Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data.
PLoS Computational Biology, 2016 Many genes are expressed in bursts, which can contribute to cell-to-cell heterogeneity. It is now possible to measure this heterogeneity with high throughput single cell gene expression assays (single cell qPCR and RNA-seq). These experimental approaches
Daphne Ezer +3 more
doaj +1 more source