2000/08/29 by Bin Wang
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Fractal and DNA sequence analysis #Machine Learning in Bioinformatics #RNA and protein synthesis mechanisms #cond-mat.stat-mech #physics.bio-ph #physics.data-an #q-bio
paper · pdf · doi:10.1016/s0378-4371(00)00545-8
13 pages, 4 figures
arxiv created 2000/08/29 · openalex publication_date 2001/04/01 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Given a sequence composed of a limit number of characters, we try to "read" it as a "text". This involves to segment the sequence into "words". The difficulty is to distinguish good segmentation from enormous number of random ones.Aiming at revealing the nonrandomness of the sequence as strongly as possible, by applying maximum likelihood method, we find a quantity called Segmentation Entropy that can be used to fulfill the duty. Contrary to commonplace where maximum entropy principle was applied to obtain good solution, we choose to \em minimize the segmentation entropy to obtain good segmentation. The concept developed in this letter can be used to study the noncoding DNA sequences, e.g., for regulatory elements prediction, in eukaryote genomes.