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A Microuidics-Driven Cloud Service: Genomic Association Studies
Published in Mohamed Ibrahim, Krishnendu Chakrabarty, Optimization of Trustworthy Biomolecular Quantitative Analysis Using Cyber-Physical Microfluidic Platforms, 2020
Mohamed Ibrahim, Krishnendu Chakrabarty
Integration and Evaluation: The above components need to be integrated to evaluate BioCyBig. For evaluation, publicly available information about microfluidic protocols and single-cell data can be used. Examples include a rich set of high-throughput functional genomic data a from the Bioconductor project [27]. Framework scalability can be assessed by tuning the number of microfluidic devices, labs, and users. It is required to systematically consider a range of simultaneous users and devices (e.g., in the range of tens to thousands). The responsiveness of BioCyBig can be evaluated against varying frequencies of adaptation requests. These evaluations are considered for future work.
Methods for the visualization of circadian rhythms: From molecules to organisms
Published in Raquel Seruca, Jasjit S. Suri, João M. Sanches, Fluorescence Imaging and Biological Quantification, 2017
Rukeia El-Athman, Jeannine Mazuch, Luise Fuhr, Mónica Abreu, Nikolai Genov, Angela Relógio
This technology is a very powerful tool for the large-scale analysis of gene expression. The most commonly used type of arrays are provided by the company Affymetrix [25] and allow for genome-wide screening in a single sample. Microarrays are glass-coated chips with a matrix type distribution of oligonucleotides of known sequences, named probes. The hybridization of the target sample (fragmented and labeled) to its complementary probe allows for a relative quantification of gene expression via measurement of optical intensity of the hybridized sample fragments. A pipeline of open-source tools provides the means for the data analysis, mainly based on R [26]. Graphical user interface (GUI) tools such as the RStudio IDE allow for better development of individual scripts [27]. The Bioconductor/Biobase [28] software repository provides an increasing number of packages that automate many steps related to computational biology and bioinformatics. For exon arrays, the R package oligo provides all necessary tools for reading and normalizing the raw data [29]. The most common normalization technique is the robust multiarray average (RMA) [30] that is also part of the oligo package. The quality control of the arrays can be performed with the R package arrayQualityMetrics [31]. Subsequent analysis can be done with the R package limma [32] as it provides the tools for the creation of linear models that are fitted to the data of the arrays given an appropriate design matrix. The design matrix represents a numerical vector or a matrix which delivers information on the experimental design. The genes that show the biggest difference between the elements specified in the matrix can be determined and further investigated.
Re-Analysis of Non-Small Cell Lung Cancer and Drug Resistance Microarray Datasets with Machine Learning
Published in Cybernetics and Systems, 2023
Çiğdem Erol, Tchare Adnaane Bawa, Yalçın Özkan
To reanalyze datasets associated with non-small cell lung cancer and drug resistance, the datasets were downloaded from the NCBI GEO database and the analyzes were performed using the R programming language via R-Studio 4.0.0 and Bioconductor 3.11. In order to access the microarray datasets included in the research, 18 datasets were accessed by searching the NCBI GEO database with the keywords "Non-Small Cell Lung Cancer" and "NSCLC". From these datasets, 6 microarray datasets (GSE21656, GSE6410, GDS2297, GDS2298, GSE6914, GSE4127) associated with drug resistance were identified. The properties of these datasets are presented in Table 1. No missing values were detected in all datasets.