LRDB: LSTM Raw data DNA Base-caller based on long-short term models in an active learning environmentThe first important step in extracting DNA characters is using the output
data of MinION devices in the form of electrical current signals. Various
cutting-edge base callers use this data to detect the DNA characters based on
the input. In this paper, we discuss several shortcomings of prior base callers
in the case of time-critical applications, privacy-aware design, and the
problem of catastrophic forgetting. Next, we propose the LRDB model, a
lightweight open-source model for private developments with a better
read-identity (0.35% increase) for the target bacterial samples in the paper.
We have limited the extent of training data and benefited from the transfer
learning algorithm to make the active usage of the LRDB viable in critical
applications. Henceforth, less training time for adapting to new DNA samples
(in our case, Bacterial samples) is needed. Furthermore, LRDB can be modified
concerning the user constraints as the results show a negligible accuracy loss
in case of using fewer parameters. We have also assessed the noise-tolerance
property, which offers about a 1.439% decline in accuracy for a 15dB noise
injection, and the performance metrics show that the model executes in a medium
speed range compared with current cutting-edge models.
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