GEDAS – Gene Expression Data Analysis Suite
نویسندگان
چکیده
منابع مشابه
GEDAS ‐ Gene Expression Data Analysis Suite
UNLABELLED 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. The software is an extension of Cluster 3.0 (...
متن کاملGene expression data analysis.
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. The raw microarray data are images, which have to be trans...
متن کاملData Analysis: Microarray Gene Expression
Most genomic data within the NextBio platform are generated using the Affymetrix platform (Figure 2). Ideally, all Affymetrix data would be imported as CEL files, and processed using the same normalization method, such as Robust Multi-array Average (RMA)1; however, for pre-existing experiments, this is often impossible. In this case, probeset-level Microarray Suite version 5 (MAS5) intensities2...
متن کاملData Analysis: Microarray Gene Expression
Most genomic data within the NextBio platform are generated using the Affymetrix platform (Figure 2). Ideally, all Affymetrix data would be imported as CEL files, and processed using the same normalization method, such as Robust Multi-array Average (RMA)1; however, for pre-existing experiments, this is often impossible. In this case, probeset-level Microarray Suite version 5 (MAS5) intensities2...
متن کاملO-3: Drug Repositioning by Merging Gene Expression Data Analysis and Cheminformatics Target Prediction Approaches
The transcriptional responses of drug treatments combined with a protein target prediction algorithm was utilised to associate compounds to biological genomic space. This enabled us to predict efficacy of compounds in cMap and LINCS against 181 databases of diseases extracted from GEO. 18/30 of top drugs predicted for leukemia (e.g. Leflunomide and Etoposide) and breast cancer (e.g. Tamoxifen a...
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ژورنال
عنوان ژورنال: Bioinformation
سال: 2006
ISSN: 0973-8894,0973-2063
DOI: 10.6026/97320630001083