Predictive and Regenerative Medicine for Humans

Abstract

In human history, finding a cure for the devastating disease of cancer has been an ongoing battle long. Recently, scientific research has shown several feasible bio-medicinal ways for the relief of cancer. The first method is through predictive-regenerative medicine, which converts cancer cells back into normal cells with the Bayesian inference prediction. In part, this event relates to cell regeneration. However, the conversion rate of these cells may not be completely effective. Another method is the rewriting of damaged coordinating genes, but this has underlying moral issues – who will authorize the right to alter human genes? To answer such questions, this paper will discuss elementary point-set topologies, physical gene mapping, and gene sequencing. By implementing genetic transcription and expression, a suitable Bayesian inference could be used for genetic reprogramming. Finally, the aim of reversing cancer cells to normal cells (or the transition) will be achieved. As mentioned before, since the conversion rate is somewhat ineffective, the main purpose of this essay is to see whether it is possible to increase the probability of cancer cells changing into normal cells (i.e., to minimize obstacles which prevent cancer cells converting backwards, and to maximize factors that enhance the deserved to reprogram process for the cancer cells changing into the normal ones). The peak value of cancer cell conversion is its Bayesian regression (the model’s parameters) from the corresponding Bayesian inference (for normal posterior distribution). The relevant predictive conditions can be determined from the Bayesian regression, which could be used to reveal elements that help optimization for the backwards transformation (changing) of cancer.