A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression.

As increasing experimental studies have shown that microRNAs (miRNAs) are closely related to multiple biological processes and the prevention, diagnosis and treatment of human diseases, a growing number of researchers are focusing on the identification of associations between miRNAs and diseases. Identifying such associations purely via experiments is costly and demanding, which prompts researchers to develop computational methods to complement the experiments. In this paper, a novel prediction model named Ensemble of Kernel Ridge Regression based MiRNA-Disease Association prediction (EKRRMDA) was developed.

Whole-proteome phylogeny of large dsDNA viruses and parvoviruses through a composition vector method related to dynamical language model.

Background: The vast sequence divergence among different virus groups has presented a great challenge to alignment-based analysis of virus phylogeny. Due to the problems caused by the uncertainty in alignment, existing tools for phylogenetic analysis based on multiple alignment could not be directly applied to the whole-genome comparison and phylogenomic studies of viruses. There has been a growing interest in alignment-free methods for phylogenetic analysis using complete genome data.

Human Pol II promoter recognition based on primary sequences and free energy of dinucleotides.

Background: Promoter region plays an important role in determining where the transcription of a particular gene should be initiated. Computational prediction of eukaryotic Pol II promoter sequences is one of the most significant problems in sequence analysis. Existing promoter prediction methods are still far from being satisfactory.

A fractal method to distinguish coding and non-coding sequences in a complete genome based on a number sequence representation.

A fractal method to distinguish coding and non-coding sequences in a complete genome is proposed, based on different statistical behaviors between these two kinds of sequences. We first propose a number sequence representation of DNA sequences. Multifractal analysis is then performed on the measure representation of the obtained number sequence.