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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.

      2.4.2 Applications of Gene Expression Analysis

      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.

2.5 Role of Computation in Protein Structure Prediction

      There are various critical and important processes and materials like personalized medicine, gene pathway, determination organs functioning, gene therapy, vaccine and drug development etc. Nowadays bioinformatics has been extensively used for the development of artificial intelligence. It also comprises softwares & programming for prediction of structure of protein however, it is still difficult to find the structure of a protein.

      The two most powerful approaches are being used for determining protein structure .These are Nuclear Magnetic Resonance and X-ray crystallography but these are too expensive & time consuming which are disadvantages associated with these techniques.

Recent advancement for getting precise & fine protein structure a powerful technique has been introduced named cryo-electron microscope (Cryo-EM). This revolutionary technique predicts high resolution large scale molecular structures. The principle of this approach is mainly used in machine learning & artificial intelligence. For interpretation of cryo-EM maps, machine learning & artificial intelligence are extensively used [26–29].

      Many liquid proteins cannot be crystallized. Getting Cryo-EM map crystallization of protein is mandatory. The solution of this problem can be done by AI which gives remedy for sequencing of protein without its crystallization.

      Artificial intelligence has numerous programmes which are trained enough to give enormous information on atomic features of protein like: bond angles, bond length, type of bonds, physical-chemical properties, bond energy, amino acids interaction, potential energy etc. Artificial intelligence is used for image recognition [30, 31]. It helps in giving precise, broad and accurate thousands of protein structure [32, 33].

      In this way these programmes suggest prediction model outputs which can be compared to the known crystal structures. There are several events organized for prediction model for protein.

      Critical Assessment of Structure Prediction (CASP) is an annual gathering for comparison of protein structures by various models to assess the quality of the model and find the most accurate model making it the important milestone for protein structure prediction for multiple applications.

      MULTICOM: in every two years all over the world researchers submit predicted protein structure while deep learning (Machine Learning) has been applied to make protein structure prediction with help of protein contact distance prediction. Professionals analyze the performance of these methods [34] and decide on the best models.

2.6 Application in Protein Folding Prediction

Understanding protein folding is inherent to understanding its function and its heterogenous nature. Cellular function is incomplete without proteins be it replication, transcription and translation, thus prediction of 3D or folded protein structure becomes very important to address various questions of molecular biology. Earlier various molecular biology techniques were used for

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