Detection of Unknown Polymorphic Patterns Using Feature-Extracting Part of a Convolutional Autoencoder

Background: The present paper proposes a novel approach for detecting the presence of unknown polymorphic patterns in random symbol sequences that also comprise already known polymorphic patterns. Methods: We propose to represent rules that define the considered patterns as regular expressions and show how these expressions can be modeled using filter cascades of neural convolutional layers. We adopted a convolutional autoencoder (CAE) as a pattern detection framework. To detect unknown patterns, we first incorporated knowledge of known rules into the CAE's convolutional feature extractor by fixing weights in some of its filter cascades. Then, we executed the learning procedure, where the weights of the remaining filters were driven by two different objectives. The first was to ensure correct sequence reconstruction, whereas the second was to prevent weights from learning the already known patterns. Results: The proposed methodology was tested on sample sequences derived from the human genome. The analysis of the experimental results provided statistically significant information on the presence or absence of polymorphic patterns that were not known in advance. Conclusions: The proposed method was able to detect the existence of unknown polymorphic patterns.

Automated summary

This paper proposes a novel approach to detect both known and unknown polymorphic patterns in symbol sequences. By representing rules as regular expressions and using filter cascades of neural convolutional layers, a convolutional autoencoder is utilized for pattern detection. The method successfully detects the presence of unknown polymorphic patterns.