A patient-specific functional module and path identification technique from RNA-seq data.

With the advancement of new technologies, a huge amount of high dimensional data is being generated which is opening new opportunities and challenges to the study of cancer and diseases. In particular, distinguishing the patient-specific key components and modules which drive tumorigenesis is necessary to analyze. A complex disease generally does not initiate from the dysregulation of a single component but it is the result of the dysfunction of a group of components and networks which differs from patient to patient.

CDSImpute: An ensemble similarity imputation method for single-cell RNA sequence dropouts.

Background: Single-cell RNA-sequencing enables the opportunity to investigate cell heterogeneity, discover new types of cells and to perform transcriptomic reconstruction at a single-cell resolution. Due to technical inadequacy, the presence of dropout events hinders the downstream and differential expression analysis. Therefore, it demands an efficient and accurate approach to recover the true gene expression.

Cell-specific gene association network construction from single-cell RNA sequence.

The recent development of a high throughput single-cell RNA sequence devises the opportunity to study entire transcriptomes in the smallest detail. It also leads to the characterization of molecules and subtypes of a cell. Cancer epigenetics induced not only from individual molecules but also from the dysfunction of the system and the coupling effect of genes.

Purity estimation from differentially methylated sites using Illumina Infinium methylation microarray data.

Solid tissues collected from patient-driven clinical settings are composed of both normal and cancer cells, which often precede complications in data analysis and epigenetic findings. The Purity estimation of samples is crucial for reliable genomic aberration identification and uniform inter-sample and inter-patient comparisons as well. Here, an effective and flexible method has been developed and designed to estimate the level of methylation, which infers tumor purity without prior knowledge from the other datasets.

Predicting potential miRNA-disease associations by combining gradient boosting decision tree with logistic regression.

MicroRNAs (miRNAs) have been proved to play an indispensable role in many fundamental biological processes, and the dysregulation of miRNAs is closely correlated with human complex diseases. Many studies have focused on the prediction of potential miRNA-disease associations. Considering the insufficient number of known miRNA-disease associations and the poor performance of many existing prediction methods, a novel model combining gradient boosting decision tree with logistic regression (GBDT-LR) is proposed to prioritize miRNA candidates for diseases.