The advent of the global pandemic caused a furious development in the biotech industry. Today biotechnological researches and innovations attract solid and profitable investments. For example, scientists around the world make their's best to find the most effective and harmless vaccine against Covid-19. Genetic engineering deals with an enormous quantity of data. It is hard to imagine the process of data collection, storage, and analysis without digital technologies, especially in biotechnology and biomedicine. Machines and artificial intelligence will never replace a human being and do not intend to depreciate human labor. But machine learning expert solutions can significantly expand the possibilities in research and contribute to getting the most accurate results. Let's have a look through some examples demonstrating the utility of machine learning application in the biotech field.
Retina stem cells growth
Stem cells differentiation always leads to the formation of a variety of specialized cells. And as we know retina has a multilayer structure consisting of different types of cells. Retina cell differentiation raises an issue among biotech scientists about which cell organ samples to pick out for further experiments. The process of cell organs selection is complicated, as it demands from a scientist to inject genes of fluorescent protein first, which, in turn, leads to cells modification. As a result, we have cell organs that are different from the initial ones. The solution is to learn to define cell proportions according to their structure and appearance. The human eye will inevitably make mistakes during researches. On the contrary, machine algorithms will be more accurate. The neural network may define the forming retina tissue before its final differentiation. By learning from images of forming retina cells and analyzing their structure, machines can forecast retina formation from stem cells. Retina cell growth has a practical aspect, as it is quite possible to transplant the cultured retina to people failing eyesight. However, if we want the transplantation to be successful and effective, it is vital to know whether the grown tissue sample will give the best fit. Machines algorithms may forecast cells growth and define whether these samples will have enough light-sensitive receptors.
Vaccine researches
Every year people around the globe get vaccine shots against different viruses. COVID-19 has complemented the list of vaccine-resistant diseases like AIDS or influenza virus. The viral proteins transform viruses very quickly. The immune system produces antibodies, but they do not work against changeable virus structures anymore. It is a real problem for the entire humanity. We still do not have medical agents to overcome AIDS. As we know, viral protein molecule has parts that are more stable than others. The possible solution is to find these invariable parts in viral molecules and train the immune system to produce antibodies from these viral parts.The idea to search these parts from thousand varieties by a human is a utopia. However, artificial intelligence and namely computational linguistics can cope with this task. Machine algorithms can analyze thousands of genetic codes of AIDS or SARS-CoV-2. The main goal of such machine learning is to identify unchangeable codes in the new and unknown viral proteins. After the immune system can determine less changeable viral proteins fragments, it can resist the virus.Disease diagnostics
Nowadays, artificial intelligence is successfully used in cancer diagnostics. IBM Watson holds the leading position in AI application in medicine and especially in oncology. The company successfully uses the cognitive system to diagnose cancer and find an appropriate and adequate treatment for thousands of patients. One of the widespread diseases of the 21st century is aortic stenosis. This heart disease is difficult to determine. However, with the help of machine learning algorithms, it can become possible to diagnose decease at early stages. At first, it is significant to build a cloud platform for keeping various patients data like including x-ray images, results of ultrasonography, and even research papers. After the cloud platform is ready, machines can analyze the data and distinguish stenosis from neoplasm, infection, or even individual anatomic anomalies. Thus, machines may significantly improve doctors' performance in treatment. Another example of ML and Big Data application is diagnostics of an apoplectic stroke. Despite computer tomography utilization, doctors may make mistakes in diagnosing patients. With the help of Big Data technology and the machine's ability to learn, it is possible to track aberrations and determine the stroke.
Medicaments design
It is not a secret that pharmaceutical companies bear enormous costs designing new medical preparation. Before entering the market, pharma companies deeply analyze medicines for a long time and then test them. Because of high costs and low profitability, many companies stop the researches on new preparations (right now, we are talking about rare diseases with a low sickness rate worldwide). Nevertheless, people suffering from these uncommon diseases still need effective medicines. Machine learning introduction into the pharmaceutical field may radically reduce companies’ costs and speed up the researches. Deep learning technology can analyze the results of thousands of experiments, including microscopy and laboratory data. Thus, the neural network can define distinctive features of various diseases and pick up appropriate treatment. The process of the clinical trial is the most dangerous and burdensome part of medicine invention. Machine learning can create a platform with patient data and forecast possible patients’ reactions on the biological level.
Perspectives of ML in the biotech industry
Today we are standing on the threshold of AI mass integration into all spheres of our lives. Bioinformatics and big data analysis in biotech have become the hottest fields.Of course, artificial intelligence cannot compete with the intelligence of doctors, scientists, and biochemists. However, the ML capacity and the ability to understand and analyze an excessive amount of different biological and medical data may significantly simplify the process. For many biotech companies, AI and ML integration are attractive because of considerable cost reduction and time-saving. Active development in deep learning, machine vision systems, natural language processing, and other branches of artificial intelligence offer a great opportunity as many people may become healthier, doctors will have more spare time for practice, and companies will save millions of dollars.