Computational Molecular Biology 2016, Vol.6, No.3, 1-6
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3 Discussions
Above examples of machine learning methods for HIV clearly shows that present era needs computationally
efficient methods to accumulate, change and update intelligent systems. As machine learning methods are fast and
economical they can very well help wet lab techniques. Machine learning provides methods, techniques and tools,
which can help solving diagnostic and prognostic problems in a variety of medical domains. It also helps medical
practitioners in treatment of diseases as in HIV-AIDS. And therefore ethical issues are also presented. It is humans
not systems who can act as moral agents and make system efficient that not morality will be maintained.
Molecular biology is now seen as encouraging more “personalized medicine” the closer alignment of biological
information and therapy selection. The evolution of molecular medicine coupled with the discovery and clinical
applications will play a significant role in reshaping medicine or treatment of HIV-AIDS.
In the conclusion, there are several areas of machine learning would be discussed that seem to be of particular
challenge and importance in future research. With the growing sophistication of learning programs, there is an
interest in increasing the transfer of machine learning programs from university laboratories to the real world,
where they can be applied to problems of practical significance. It is a challenge to researchers to test their
research in the context of real life problems i.e. HIV and other deadly diseases and may lead to economical
treatment available worldwide. It would be better if it is said that machine learning is a cutting edge technology
which achieves heights by its betterment in research and medicine.
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