Machine Learning Models Predict the Effects of Genetic Mutation

Los Angeles CA

17 December, 2021

8:40 AM

Description

Whenever any mutation happens in our genome, the question is - will it eventually go unnoticed, or will it cause harm to our health? One of the highly prioritized directions of this research focuses on cancer driver genes to build a solid foundation for oncological screening and personalized cancer treatment. However, a genetic mutation leads to more effects, which need research attention and the application of computational methods. Machine learning models used for predicting the genetic mutation effects can help health systems advance medical decision-making. At the border of biotechnology and AI, the most effective scientific research takes place. Human error remains the major factor in the patients’ worsening condition when the wrong diagnosis or treatment is assigned. It happens because of the dependence on the individual decision-making among the medical specialists. Below we will consider several latest machine learning applications aimed at reducing individual decision-making in the health system. Machine learning for hemophilia research Hemophilia, a genetic disease that affects blood clotting, stays at the center of research attention for some machine learning engineers. Hemophilia research already encounters successful machine learning-based studies. Such algorithms as Position-Specific Mutation and One-Hot Encoding decide the extent to which hemophilia is severe in a patient’s case. Severity prediction accuracy shows up to 99% scores for Position-Specific Mutation in some studies. This means that not only the mutation itself causes hemophilia, but the position of this mutation plays a key role in the development of the disorder as well. The successful application of machine learning for hemophilia research leaves space for the promising future of computer-aided diagnostics. Simulation of mutation effects with neural networks Another area of genetic mutation research focuses on proteins, large organic compounds responsible for the basic biological functions in our organisms. The structure of protein data type is the primary reason for researchers to utilize machine learning methods. In protein research, scientists train neural networks to reveal how to simulate the effect of mutations on proteins. Human language processing in genomics Not only do human organisms attract research for genetic mutations effect, but the hosts within our organisms, viruses. This case is quite fascinating as it has gone beyond the traditional application of human language processing algorithms. Recent research has used human language modeling algorithms to reveal how viruses develop and invade our immune systems. For an algorithm, there is no significant difference between sequences. The sequences of genetic information look quite the same as the paragraphs of texts. MIT engineers easily spotted out this analogy. They have trained language models on the available COVID-19, flu, and HIV data just as if they were paragraphs of texts, and their findings have confirmed the sequences’ similarity assumptions. As a result, those sequences similar in ‘meaning’ would contaminate the same hosts. Now it is known that there are semantics for a distinct type of disease. The future development of this approach will reveal how different samples of COVID-19 will be prone to evade vaccine effects. The custom machine learning application for the prediction of genetic mutation effects is not limited to virus, cancer, and hemophilia research, as genetic mutation causes a variety of disorders. The new knowledge about the diseases generated from genetic research helps both in diagnostics and further treatment. Moreover, it makes the scope of machine learning application even wider, as the human language algorithms implementation in genomics has inspired scientists to think outside of the traditional understanding of the algorithms’ purpose.