Low Complexity Classification of Thermophilic Protein using One Hot Encoding as Protein Representation

The laborious, and cost-inefficient biochemical methods for identifying thermophilic proteins necessarily require a rapid and accurate method for identifying thermophilic proteins. Recently, machine learning has become a more effective method for identifying specific classes of extremophiles. There is still a need for a low-cost method for identifying thermophilic proteins, despite the fact that studies employing machine learning yielded superior results to conventional methods. Here, we avoid the problem of manually crafted features, which involves experts defining and extracting a set of features using only protein sequences as input for various computational methods. This study classifies thermophilic proteins and their counterparts using only protein sequences in one-hot encoding representation and the bidirectional long short-term memory (BiLSTM) model. The model achieved an accuracy of 92.34 percent, a specificity of 91 percent, and a sensitivity of 93.77 percent, which is superior to other models reported elsewhere that rely on a number of manually crafted features. In addition, the more trustworthy and objective data set and the independent data set for evaluation make this model competitive with other, more accurate models.

@article{Susanty2022,
  title     = {Low Complexity Classification of Thermophilic Protein using One Hot Encoding as Protein Representation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {12},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Meredita Susanty and Rukman Hertadi and Ayu Purwarianti and Tati Latifah Erawati Rajab},
  doi       = {10.14569/IJACSA.2022.0131212},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131212}
}