In this article, we will look at the development and history of the occurrence of genetic diseases, since proper treatment and diagnosis is now an important problem, which can be successfully solved with the ML technologies. The main reason for looking for new approaches to the diagnostics is if the problem is identified in time, the chance of the patient’s survival increases several times.
Artificial intelligence technologies are actively scouring the field and offer a wide range of approaches to model the genome modification process. But they are not 100% true, as the model works within the borders of hypothesis limitations. Now we will look at some issues, which scientists try to overcome in the field of genetic prototyping.
The main difficulty of detecting a genetic disease is laying in the multivariativity of possible changes and the necessity to collect and analyse myriads of data arrays. So, to reproduce all the possible changes and collect and analyse the results, a lot of money is needed for research. Here, ML scientists, like Serokell.io can help in modeling.
Origins
Man began to learn about genetics in the 19th century. Mendel suggested that genetic diseases are inherited.
In recent years, we have not evolved badly in this area. We have learned to understand the genome, can decipher it, and even modify it. The genetic information is stored in DNA and also includes all the information about us. We need to understand the problem, our existence depends on it.
Difficulties in solving the problem
The DNA strand is a complex system made up of a billion separate pieces. The process of obtaining it is very hard. AI explores a certain area, and then uses its algorithms to calculate the data, but after analysis and tests it turned out that the material is not accurate.
Therefore, another method was invented that works with the molecule as a whole.
Dangers and Risks
When obtaining all the data about the DNA strand, it is still necessary to investigate mutations that occur for various reasons, for example, due to environmental problems. People are born with a set of certain gene variations, and dangerous mutations, such as cancer, absorb a huge number of different mutations. Therefore, it is important to find the right way to detect genetic diseases.
The researchers already were able to predict some of them, via solving a complex mathematical problem by modeling 100,000 reactions to different changes. But it’s not enough to guaranty the representative results for the majority of experiments.
Another example, where the ML technologies are necessary to make further progress, describes the work of artificial material based on nanorods, integrated into the human body.
As their gold-coated tips touch an electrode, their functional group converts light into electricity. This is the first time than a flexible organic conductor has been used to solve a real-world problem with the potential to improve the quality of life worldwide.
This network of organic and inorganic materials is a fully functional biorobotic system that can potentially be implanted into any tissue or organ.” The researchers have been able to show that the combination of organic and inorganic components is particularly powerful. But to predict the consequences of such integration, to forecast any changes to the human, like mutations, as well as the occurrence of new diseases, wide experiments are needed.
Expensive research
Currently, the cost of diagnostics is thousands of dollars, so only wealthy people can afford this study. Let’s hope that artificial intelligence will be able to conduct such research, then it will be possible to detect various viruses and allergies from early childhood.
Precise indicators
Precise indicators that help to find the root of the problem, as well as identify the biological state, are called biomarkers and are divided into three types in order to work as accurately as possible in the body. If we want to track a pathology at the DNA level, such as a cancerous tumor, we will need molecular ones. If we need to see the problem on X-rays, we need images. Biomarkers used in medical practice should have a strict set of parameters and classifications so as not to harm an already sick person. They are used to track diseases such as:
- Cancer
- Alzheimer’s disease
- Parkinson’s disease
- Dementia
With this method, you can determine the predisposition. Machine learning is a key technology in genetic data analysis. It allows us to group patients with similar DNA structures together and helps us with the early diagnostics of the disease when the phenomenon is only in its initial stages. And, of course, it helps in reducing the time needed for the diagnosis itself.
The Future of Machine Learning
It is difficult to diagnose people’s health in the field of genetics. ML gives us some results, providing both, quicker checks for hypothesis, and highlighting the group of patients, who can be set to a risk group even at the beginning of the illness. Work with the united DB of medical data allows biotech scientists to bring the diagnosis to a new level. To describe a couple of the practical examples, the screening of mammographics can be named.
- The diagnosis of cancer can be predicted at an early stage by the ML application based on the cumulative data of many patients with similar diagnosis.
- Sequences of the gene mutations can be predicted more precisely for the future child if the scientist can add the data about the parents and work out the model with the assistance of ML algorithms.
Result
ML technologies, integrated into medical science, improve the efficiency of healthcare research and diagnostics. It includes both features of quick check results and highlighting the patients that are at risk. A huge number of lives can be saved if the disease is detected in time.
Scientists believe that the sooner we get to an objective way of solving this problem, the more precise diagnosis will be set, the more lives can be saved from the heave disease. If you have some cases, where the assistance of ML scientists is needed, you can get in touch with the experts. Artificial Intelligence, where machine-made software is given the ability to learn autonomously by experiencing the world – creates tremendous value, both for medical experts and for patience.
