چكيده به لاتين
Biological sequences contain information that should be used in applications such as classification, sequence alignment, sequencing, and so on. Extracting this information and using them in these applications is complex and time-consuming. By applying a proper representation on these sequences, they can be made easier to understand and calculate. Nowadays with the advent of “Next Generation Sequencing”, an abundance of sequence data is now available to be processed for a range of bioinformatics applications and that cause machine learning methods on different bioinformatics problems sequences becomes more applicable. In recent studies, we must know the basic knowledge in the biology domain to describe and analyze the biological sequence. Each biological sequence can be represented as a n-dimentional vector that characterizes the biophysical and biochemical properties of the sequence by using machine learning models. This representation vectors can be applied to a wide range of problems in bioinformatics, such as protein family classification and structure prediction. This motivates us to show the using vector representations efficacy in the "Sequencing" application. Based on the results, it was observed that the use of artificial intelligence based methods can have a significant impact on performance.
