Next Generation Microarray Bioinformatics
A special issue of BioTech (ISSN 2673-6284).
Deadline for manuscript submissions: closed (15 September 2016) | Viewed by 199
Special Issue Information
Dear Colleagues,
DNA Microarrays are a key tool for molecular biology and biomedicine, and they are used to measure the expression levels of many genes simultaneously, or to genotype multiple regions of a genome, e.g., by detecting Single Nucleotide Polymorphisms (SNPs). Patterns found in microarray data can be used as biomarkers or prognosis markers for various diseases, and can be used to study the different responses to drugs in pharmacogenomics. The availability of high-throughput microarray platforms and their increasing use in clinical studies, is leading to an increasing production of experimental and clinical data that requires a complex analysis pipeline for data preprocessing, data transformation, statistical and data mining analysis, knowledge models building, and interpretation. Thus, the bottleneck in the microarray pipeline is moving from the wet lab (sample preparation and experiment execution) toward the "in silico" lab (storage, preprocessing, integration, and analysis of experimental data, as well as correlation and integration with publicly available databases). This Special Issue invites submissions on efficient algorithms, software tools, and comprehensive data analysis pipelines for the preprocessing and mining of microarray data; as well as submissions on applications of microarrays in biology, medicine, and clinical practice.
Prof. Dr. Mario Cannataro
Guest Editor
Manuscript Submission Information
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Keywords
- microarray data preprocessing
- microarray statistical analysis
- microarray data mining analysis
- microarray databases
- microarray standards
- microarray comprehensive software pipelines
- microarrays for disease diagnosis, prognosis, screening
- microarrays for P4 (predictive, preventive, personalized, and participatory) medicine
- microarrays for pharmacogenomics
- ontology-based annotation of microarray data
- integration of microarray and clinical data
- biomarker discovery
- gene expression
- genotyping
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