DNA sequencing is the process of experimentally finding the sequence of nucleotides (A, C, G and T) – the chemical building blocks of genes – of a piece of DNA. DNA sequencing is largely used to study human diseases and genetics, but in recent years, sequencing has become a routine part of viral point of care, and as sequencing becomes cheaper and cheaper, viral sequencing will become even more frequent as time progresses.RNA is a molecule similar to DNA, and it is essentially a temporary copy of a short segment of DNA. Specifically, in the central dogma of biology, DNA is transcribed into RNA. SARS-CoV-2 is an RNA virus, meaning our DNA sequencing technologies cannot directly decode its sequence. However, scientists can first reverse transcribe the RNA of the virus into complementary DNA (or cDNA), which can then be sequenced. Given a collection of viral genome sequences, we can use our models of sequence evolution to predict the virus’s history, and we can use this to answer questions like, “How fast do mutations occur?” or “Where in the genome do mutations occur?” Knowing which genes are mutating frequently can be useful in drug design.
Genetic Algorithms(GAs) are adaptive heuristic search algorithms that belong to the larger part of evolutionary algorithms. Genetic algorithms are based on the ideas of natural selection and genetics. These are intelligent exploitation of random search provided with historical data to direct the search into the region of better performance in solution space. They are commonly used to generate high- quality solutions for optimization problems and search problems. Genetic algorithms simulate the process of natural selection which means those species who can adapt to changes in their environment are able to survive and reproduce and go to next generation. In simple words, they simulate “survival of the fittest” among individual of consecutive generation for solving a problem. Each generation consist of a population of individuals and each individual represents a point in search space and possible solution. Each individual is represented as a string of character/integer/float/bits. This string is analogous to the Chromosome.
In this research, an optimized deep learning method was proposed to explore the possibility and practicality of neural net-work applications in medical imaging. The method was used to achieve the goal of judging common pneumonia and even COVID-19 more effectively. Where, the genetic algorithm was taken advantage to optimize the Dropout module, which is essential in neural networks so as to improve the performance of typical neural network models. The experiment results demonstrate that the proposed method shows excellent performance and strong practicability in judging pneumonia, and the application of advanced artificial intelligence technology in the field of medical imaging has broad prospects.
- https://www.ge.com/news/reports/software-please-doctors-looking-ai-speed-diagnosis
- https://towardsdatascience.com/introduction-to-genetic-algorithms-including-example-code-e396e98d8bf3
- https://theconversation.com/heres-how-scientists-are-tracking-the-genetic-evolution-of-covid-19-134201
- https://en.wikipedia.org/wiki/Genetic_algorithm
- https://www.geeksforgeeks.org