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In [2] the author suggested that the estimated price of sequencing the human DNA (the “combing cost” of) has dropped significantly in the past few years [cost to combing the 30,000 to 35,000 gene map is now inversely proportional to how many genes are found] on the grand scale, and as it is to computational biology, developing genome-scale solutions that are applied to the field of public health can have implications for current and future public health policies and services. In 2013 [3] researcher claimed that, the most important factor in making recommendations in a clinical setting is the cost and time to put them in place. Prospective/preventive, and proctical health-focused strategies aim to acquire information on 100,000 individuals for more than two decades, known as P4-predicted (stating only if it is possible); research using the predictive-targeted, or integrated omics, referred to as personalomics (using your personal data). In [4] the author suggested to include seeking solutions over with regard to the following four aspects such as:

1 1. Developing scalable genome-scale data states

2 2. Use of tools

3 3. Clinical states

4 4. Data challenges in target validation, and integration, a big data project.

Project (P4) is making strides by acquiring tools to help with handling massive datasets, and then, following this, they have developed continuous monitoring tools that aid in understanding a subject’s condition, as well as obtaining new information, and they are moving forward in their search for medication delivery and analytical tools. Everything that is known about a person’s physiology and his/her physiological states in-based person wellness is summarized and is added to person omics (usage-driven genomics methods) which are used to identify and detail the subject’s medical state [5]. Although an actionable course of action at the level of care may be one of the most difficult aspects, many improvements at the clinical level can be pursued (even though it may be arduous). According to [6], a lot of high-resolution data is required for exploration, discovery, and implementing novel approaches. These two aspects of big data necessitate the use of novel data analytics.