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Applications of gene expression involve the comparative analysis. Analysis of relative expression of same set of gene in different conditions is main applications of the high throughput approaches. The important and useful comparative analyses are mentioned below:

1 a) The comparative expression pattern of same set of genes in mutant and wild type

2 b) The analysis of gene expression in disease and control one

3 c) For time point comparison between the same set of gene during any drug treatment or during development

4 d) The comparison of same set of gene expression in different tissues or organs

5 e) To determine drug efficacy by relative comparison of same set of genes in control and treated with a particular drug.

In case of medical and clinical diagnostics study of gene expression plays a very important role, as any change be it under-expression, over-expression or loss of function plays a role in various disease etiology. So, it is really important to equip our clinicians, pathologist and the researchers with such advanced computing devices to come to a valid and informed conclusion related to disease condition. Such result interprets health data arising from a large set of unstructured data form for example the identification or forecasting of a disease state.

AI interpretation tasks related to clinical aspect can be grouped into various classes of which includes computer vision, time series analysis, speech recognition, and natural language processing. Each of these problems is well suited to address specific types of clinical diagnostic tasks [20].

1 a) Computer vision is useful for the interpretation of radiological images; time series analysis is useful for the analysis of continuously streaming health data such as those provided by an electrocardiogram [21].

2 b) Speech-recognition techniques can be used for detection of neurological disorders [22].

3 c) AI-based natural language processing can be helpful in the extraction of meaningful information from electronic health record (EHR) data [23].

4 d) These techniques also aid in analysing areas which are not very obvious such as regulation of genome.

AI aided systems can identify functional regulatory elements present in the human genome, where they can be used to identify recurrent motifs in DNA sequences in a manner analogous to that in which pixel patterns are detected in images by convolutional neural networks [24] AI algorithm deep learning is able to interpret features from large and complex datasets by using deep neural network architectures. Neural networks are computational systems of artificial neurons (also called ‘nodes’) that transmit signals to one another, often in interconnected layers as neurons in a human body do. In such computational systems there are layers known as hidden layers which are not the input or the output layer. A deep neural network consists of many hidden layers of artificial neurons. Neural networks often take as input the fundamental unit of data that it is trained to interpret: for example, pixel intensity in images; diagnostic, prescription, and procedure codes in EHR data; or nucleotide sequence data in genomic applications [25]. A multitude of these simple features are combined in successive layers of the neural network in a lot of ways, as designed by the human neural network architect, in order to represent more sophisticated concepts or features of the input health data. Ultimately, the output of the neural network is the interpretation task that the network has been trained to execute. For example, successive layers of a computer vision algorithm might learn to detect edges in an image, then patterns of edges that represent shapes, then collections of shapes that represent certain objects, and so on. Thus, AI systems synthesize simple features into more complex concepts to derive conclusions about health data in a manner that is analogous to human interpretation, although the complex concepts used by the AI systems are not necessarily recognizable or obvious concepts to humans.