Motivation The discovery of novel biological knowledge from the ab initio analysis of post-
genomic data relies upon the use of unsupervised processing methods, in particular …

Clustering is ubiquitously applied in bioinformatics with hierarchical clustering and k-means
partitioning being the most popular methods. Numerous improvements of these two …

Despite advances in DNA sequencing technology, assembly of complex genomes remains
a major challenge, particularly for genomes sequenced using short reads, which yield highly …

Motivation: UPGMA (average linking) is probably the most popular algorithm for hierarchical
data clustering, especially in computational biology. However, UPGMA requires the entire …

Assembly of reference-quality genomes from next-generation sequencing data is a key
challenge in genomics. Recently, we and others have shown that Hi-C data can be used to …

Data clustering is a challenging task to gain insights into data in various fields. In this paper,
an Enhanced Quantum-Inspired Evolutionary Fuzzy C-Means (EQIE-FCM) algorithm is …

In an era of rapid genome sequencing and high‐throughput technology, automatic function
prediction for a novel sequence is of utter importance in bioinformatics. While automatic …

Background Proteins are comprised of one or several building blocks, known as domains.
Such domains can be classified into families according to their evolutionary origin. Whereas …

Background Cluster analysis has been widely applied for investigating structure in bio-
molecular data. A drawback of most clustering algorithms is that they cannot automatically …

Identifying genomic regions that descended from a common ancestor is important for
understanding the function and evolution of genomes. In distantly related genomes, clusters …