BACKGROUND: The Geminiviridae family encompasses a group of single-stranded DNA viruses with twinned and quasi-isometric virions, which infect a wide range of dicotyledonous and monocotyledonous plants and are responsible for significant economic los...
IEEE/ACM transactions on computational biology and bioinformatics
Apr 18, 2017
Biomolecular controlled annotations have become pivotal in computational biology, because they allow scientists to analyze large amounts of biological data to better understand test results, and to infer new knowledge. Yet, biomolecular annotation da...
Bacterial small non-coding RNAs (sRNAs) are not translated into proteins, but act as functional RNAs. They are involved in diverse biological processes like virulence, stress response and quorum sensing. Several high-throughput techniques have enable...
IEEE/ACM transactions on computational biology and bioinformatics
Apr 5, 2017
Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown t...
IEEE/ACM transactions on computational biology and bioinformatics
Apr 3, 2017
Gene set enrichment (GSE) is a useful tool for analyzing and interpreting large molecular datasets generated by modern biomedical science. The accuracy and reproducibility of GSE analysis are heavily affected by the quality and integrity of gene sets...
BACKGROUND: Lung adenocarcinoma is one of most threatening disease to human health. Although many efforts have been devoted to its genetic study, few researches have been focused on the transcription factors which regulate tumor initiation and progre...
BACKGROUND: A key step in microbiome sequencing analysis is read assignment to taxonomic units. This is often performed using one of four taxonomic classifications, namely SILVA, RDP, Greengenes or NCBI. It is unclear how similar these are and how to...
BACKGROUND: A family of parsimonious Gaussian mixture models for the biclustering of gene expression data is introduced. Biclustering is accommodated by adopting a mixture of factor analyzers model with a binary, row-stochastic factor loadings matrix...
Selective ensemble learning is a technique that selects a subset of diverse and accurate basic models in order to generate stronger generalization ability. In this paper, we proposed a novel learning algorithm that is based on parallel optimization a...