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This analysis demonstrates high accuracy in classifying gene mutations in cancer, highlighting the role of specific genetic changes.
A gene mutation refers to a small alteration in DNA that can occur at any time. While many of these changes have minimal impact, some can lead to uncontrolled cell growth, potentially contributing to the development, progression, and spread of diseases such as cancer. These mutations may arise from various factors. For instance, in lung cancer, cigarette smoking is a well-known cause of genetic mutations that can elevate cancer risk. Similarly, exposure to air pollution or harmful toxins can produce the same effect. In some cases, however, mutations occur spontaneously, without any identifiable cause. While not all cancers are caused by gene mutations, certain mutations are strongly linked to specific types of cancer. For example, mutations genes are frequently within tumor cells and are not usually inherited or passed down through families. In this paper, we will investigate gene mutations and will examine the ways in which these mutations may cause various diseases including cancers. It is found that high accuracy classification of gene mutations can be reached by a combination of LSTM (Long Short-Term Memory (LSTM) is an enhanced version of the Recurrent Neural Network) and Markov probabilistic transitions.
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