Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved
its effectiveness in bioinformatics due to its capacity to produce local instead of global …

Biclustering is an unsupervised machine-learning technique that simultaneously clusters
rows and columns in a data matrix. Over the past two decades, the field of biclustering has …

Background Biclustering techniques are capable of simultaneously clustering rows and
columns of a data matrix. These techniques became very popular for the analysis of gene …

Collaborative filtering (CF) recommender systems are static in nature and does not adapt
well with changing user preferences. User preferences may change after interaction with a …

Background Mass spectrometry is one of the most important techniques in the field of
proteomics. MALDI-TOF mass spectrometry has become popular during the last decade due …

A recommender system aims to recommend items that a user is interested in among many
items. The need for the recommender system has been expanded by the information …

Biclustering is an unsupervised machine learning technique that simultaneously clusters
rows and columns in a data matrix. Biclustering has emerged as an important approach and …

Automatic sequence annotation is an essential component of modern 'omics' studies, which
aim to extract information from large collections of sequence data. Most existing tools use …

Motivation: Meta-analysis of genomics data seeks to identify genes associated with a
biological phenotype across multiple datasets; however, merging data from different …

Biclustering algorithm is an effective tool for processing gene expression datasets. There are
two kinds of data matrices, binary data and non-binary data, which are processed by …