Improved Python Package for DNA Sequence Encoding using Frequency Chaos Game Representation
Halder, A.; Piyush, ; Mathew, B.; Sengupta, D.
Show abstract
SummaryFrequency Chaos Game Representation (FCGR), an extended version of Chaos Game Representation (CGR), emerges as a robust strategy for DNA sequence encoding. The core principle of the CGR algorithm involves mapping a onedimensional sequence representation into a higher-dimensional space, typically in the two-dimensional spatial domain. This paper introduces a use case wherein FCGR serves as a kmer frequency-based encoding method for motif classification using a publicly available dataset. Availability and implementationThe FCGR python package, use case, along with additional functionalities, is available in the GitHub. Our FCGR package demonstrates superior accuracy and computational efficiency compared to a leading R-based FCGR library [1], which is designed for versatile tasks, including proteins, letters, and amino acids with user-defined resolution. Nevertheless, it is important to note that our Python package is specifically designed for DNA sequence encoding, where the resolution is predetermined based on the kmer length. It is a drawback of our current package compared to the state-of-the-art R-based kaos package [1].
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- GenomeBits insight into omicron and delta variants of coronavirus pathogen 96%
- Artificial intelligence tool for the study of COVID-19 microdroplet spread across the human diameter and airborne space 95%
- Improving prediction of drug-target interactions based on fusing multiple features with data balancing and feature selection techniques 95%
Similar papers in this journal
- Feature Extraction Approaches for Biological Sequences: A Comparative Study of Mathematical Models 97%
- DBpred: A deep learning method for the prediction of DNA interacting residues in protein sequences 97%
- An in silico approach to identification, categorization and prediction of nucleic acid binding proteins 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.