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The following methods and high throughput approaches have been used for analysis of gene expression

1 Microarrays: It is a very effective tool for analysis of gene expression. Microarray has been used for comparison of the same set of genes in different conditions, or in different cells or in same cells in different timings. Microarray has been used for analysis of gene expression on a large scale. It is act usually comparative study. It has been used for tens of thousands of target gene comparisons at one time. In different sets of conditions with the same set of genes expressed differently, microarray has been used to predict the different expression of the same set of genes in different conditions. It gives an idea about a particular set of get for their up regulation or down regulation when compared with standard one. Therefore relative expression levels between the two populations can be calculated. This high throughput approach allows for large scale screening of gene pathways or disease-related gene families. It provides a useful approach in disease-prognosis and disease diagnosis studies. It is a very effective method to determine the effects of chemicals or drugs on biological processes in pharmaceutical research.

Microarray has been used for analyzing large amounts of genes which have either been recorded previously or new samples. Microarray is a very sensitive technique. It can detect even a single nucleotide change in a given gene. This highly precise determination of a single nucleotide change or SNPs (single nucleotide polymorphisms) via microarray make this approach very useful applicable to identify strains of viruses, to identify mutation in cancer cells and subsequently facilitate disease’s treatment

2 Serial Analysis of Gene Expression (SAGE) SAGE is an important quantization technique for determination of gene expression. The principle of SAGE is based on counting of the number of tags in a particular gene. The total number of gene tags gives a strong idea for how much gene is expressed or how much abundance of gene product will be there in cell. The total number of tags would give an idea to predict the abundance of a gene product.

3 Next Generation Sequencing (NGS) NGS is another technology used for gene expression analysis. RNA-Seq is an efficient technology. Millions of random position reads could be measured and compared with the help of NGS. Data can be used to map and align to each gene, in this way NGS provides an analysis of gene expression at a remarkable level of detail.

4 Real Time Reverse Trancriptase PCR (RT-PCR) Real time reverse transcriptase PCR (RT-qPCR) is another powerful approach for determination of high throughput gene expression analyses and for the analysis of moderate numbers of genes. It can detect accurate relative and in some cases absolute quantity of cDNA in a sample. RT-PCR is accurately used for qualitative and quantitative interpretation of gene expression. It is gold standard method for analysis of gene expression. Depending upon the experiment design, overall workflow and analysis techniques RT-qPCR gives efficient results. For getting 100% PCR efficiency, a number of models, software programs and calculation approaches are there. Depending upon the numbers or type of reference genes used for normalization and calculation methods RT-PCR results may vary. Once relative expression levels have been calculated, appropriate statistical analysis is used to ensure any conclusions drawn from the data. Conclusion can be made to find out if the data is biologically relevant.

Tasks of the nature that requires human intelligence is aided by Artificial intelligence (AI) installed in the software and hardware of the computer system. Multiple advancement has been achieved in deep learning algorithms, the graphic processing units (GPI) which has revolutionized its medical and clinical applications. In Advances in AI soft-ware and hardware, especially deep learning algorithms and the graphics processing units (GPUs) that power their training, have led to a recent and rapidly increasing interest in medical and clinical applications. In clinical diagnostics, AI-based computer vision approaches are poised to revolutionize image-based diagnostics, while other AI subtypes have begun to show similar promise in various diagnostic modalities. In case of clinical genomics, a specific type of AI algorithm known as deep learning is used to process large and complex genomic datasets to predict certain outcomes. These analyses are done based on large amount of data which is beyond human capability thus helping in prognosis, diagnosis and therapeutics.