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Vertex Assignment for Frequency Chaos Game Representation of Proteins Affects Classification Performance
Bálint Biró1,2, Tímea Subicz1, Róbert Barta2
1Group BM, Data Insights Team, _VOIS, Budapest, Hungary.
Abstract:
Chaos game representation and its frequency matrix variant are popular sequence encodings commonly assumed to be invariant to alternative nodal orientations. In this paper, we systematically test this assumption on 3 protein benchmark datasets with 2 convolutional neural networks and show that different vertex assignments lead to significant differences in classification performance. Evaluating 1,000 random encodings revealed a wide performance range, proving that vertex assignment is a critical parameter rather than an arbitrary choice. Furthermore, in most of the cases, frequency chaos game representation-based data augmentation proved to be effective only under moderate settings, while aggressive augmentation degraded performance due to distributional shifts introduced by permuted encodings.
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