Machine learning, being a universal industry as it is, has its implications on biotechnology as well. So how can we use machine learning in biotech?
First of all, machine learning scientists and software engineers can make creating disease analysis and statistics comparison much easier and much more optimized a task, benefitting pharmacy and healthcare. With this implication, creating medication is much faster and much less expensive.
Secondly, as we already know, machine learning is good at predicting, making it a vital tool in medicine and biotech. With biotechnology and machine learning working together, it is much easier for doctors to predict particular types of diseases, whether mental or physical: depression, types of cancer, dementia, etc., and provide the essential treatment or therapy in time.
And lastly, machine learning makes it possible for bioprinting to exist, which vastly benefits the branch of biotech that creates artificial body parts. Speaking of which, we would like to dedicate our next block to this particular branch.
Amont the valuable feature of biotechnology is monitoring and predicting possible genetic heritage and mutations. This particular feature of biotech reminded us greatly of the video about the evolution simulator, as the very concept is the same in multiple ways. AI, keeping in mind all the factors and natural circumstances, runs hundreds of checks upon the object to analyze all the possible variations of genetic heritage in a certain generation. That said, it is much easier to predict changes and mutations, as well as related genetic diseases, and combat them. Using the patient’s DNA may be of service, as it may provide scientists with a more accurate picture. Developing this study may result in getting acquainted with new features of the human DNA and genetics, as well as new biotech applications. The latter will prove to be valuable knowledge to benefit and save lives.
However, working with human DNA is pretty hard. First things first, such procedures take quite some time and can be tiresome. Another major complication of these operations is that sometimes an experiment or any other act with the DNA can be performed only once, meaning the slightest mistake can ruin the whole procedure.
Data management is still a problem for machine learning in biotech. Lack of skilled resources, inadequate infrastructure, and poor optimization of an AI system can hardly benefit the facility in terms of keeping and structuring vital information.
Hazards of Biotech
Biotech is not only about benefits. Biotechnology can be potentially harmful and dangerous as well. So what are the hazards of biotechnological applications?
1.4.1 Biological Weapon
Accelerated developments in biotechnology are creating environmental, ethical, and social challenges for society. They also have critical implications for international peace and security because they provide humanity with new ways for the creation of biological weapons. Such weapons, designed for new types of warfare situations are increasingly becoming a reality that we have to face.
As diagnosis is getting a huge boost in development with the help of biotechnological applications, the question of privacy is getting speculated more and more often. The vital information about the patient’s health might leak and not stay confidential, creating numerous problems.
Biotechnology is truly an industry of great importance, as well as an industry of great potential. With the help of pioneering scientists, ML researchers, and software engineers, biotech is developing into a beneficial tool to save and improve our lives, which is something we look forward to.