WalkIm: Compact image-based encoding for high-performance classification of biological sequences using simple tuning-free CNNs.

ABSTRACT: The classification of biological sequences is an open issue for a variety of data sets, such as viral and metagenomics sequences. Therefore, many studies utilize neural network tools, as the well-known methods in this field, and focus on designing customized network structures. However, a few works focus on more effective factors, such as input encoding method or implementation technology, to address accuracy and efficiency issues in this area. Therefore, in this work, we propose an image-based encoding method, called as WalkIm, whose adoption, even in a simple neural network, provides competitive accuracy and superior efficiency, compared to the existing classification methods (e.g. VGDC, CASTOR, and DLM-CNN) for a variety of biological sequences. Using WalkIm for classifying various data

SUBMITTER: Akbari Rokn Abadi S 

PROVIDER: S-EPMC9012348 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature