scholarly journals Computational Techniques to Recover Missing Gene Expression Data

2018 ◽  
Vol 3 (6) ◽  
pp. 233-242
Author(s):  
Negin Fraidouni ◽  
Gergely Zaruba
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Computational Techniques ◽  
Expression Data ◽  
Missing Gene

Computational Models for the Analysis of Modern Biological Data

2009 ◽  
pp. 117-125
Author(s):  
Tuan D. Pham
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Computational Models ◽  
Biological Data ◽  
Computational Techniques ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Proteomic Data ◽  
Microarray Gene ◽  
Increasing Demand

Computational models have been playing a significant role for the computer-based analysis of biological and biomedical data. Given the recent availability of genomic sequences and microarray gene expression, and proteomic data, there is an increasing demand for developing and applying advanced computational techniques for exploring these types of data such as: functional interpretation of gene expression data, deciphering of how genes, and proteins work together in pathways and networks, extracting and analysing phenotypic features of mitotic cells for high throughput screening of novel anti-mitotic drugs. Successful applications of advanced computational algorithms to solving modern life-science problems will make significant impacts on several important and promising issues related to genomic medicine, molecular imaging, and the scientific knowledge of the genetic basis of diseases. This chapter reviews the fusion of engineering, computer science, and information sciences with biology and medicine to address some latest technical developments in the computational analyses of modern biological data: microarray gene expression data, mass spectrometry data, and bioimaging.

scholarly journals Analysis of Microarray Data using Artificial Intelligence Based Techniques

2016 ◽  
pp. 216-239 ◽  
Author(s):  
Khalid Raza
Keyword(s):  
Gene Expression ◽  
Artificial Intelligence ◽  
Gene Expression Data ◽  
Microarray Gene Expression Data ◽  
Computational Techniques ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Genome Wide ◽  
Microarray Gene ◽  
Nature Inspired Algorithms

Microarray is one of the essential technologies used by the biologists to measure genome-wide expression levels of genes in a particular organism under some particular conditions or stimuli. As microarrays technologies have become more prevalent, the challenges of analyzing these data for getting better insight about biological processes have essentially increased. Due to availability of artificial intelligence based sophisticated computational techniques, such as artificial neural networks, fuzzy logic, genetic algorithms, and many other nature-inspired algorithms, it is possible to analyse microarray gene expression data in a better way. In this chapter, we present artificial intelligence based techniques for the analysis of microarray gene expression data. Further, challenges in the field and future work direction have also been suggested.

Analysis of Microarray Data Using Artificial Intelligence Based Techniques

Biotechnology ◽  
2019 ◽  
pp. 865-888
Author(s):  
Khalid Raza
Keyword(s):  
Gene Expression ◽  
Artificial Intelligence ◽  
Gene Expression Data ◽  
Microarray Gene Expression Data ◽  
Computational Techniques ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Genome Wide ◽  
Microarray Gene ◽  
Nature Inspired Algorithms

Microarray is one of the essential technologies used by the biologists to measure genome-wide expression levels of genes in a particular organism under some particular conditions or stimuli. As microarrays technologies have become more prevalent, the challenges of analyzing these data for getting better insight about biological processes have essentially increased. Due to availability of artificial intelligence based sophisticated computational techniques, such as artificial neural networks, fuzzy logic, genetic algorithms, and many other nature-inspired algorithms, it is possible to analyse microarray gene expression data in a better way. In this chapter, we present artificial intelligence based techniques for the analysis of microarray gene expression data. Further, challenges in the field and future work direction have also been suggested.

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