Biology subjects

Ng, K.-L.

Publications and source records attributed to Ng, K.-L..

3 recordsLinked to original sources

bioRxiv2019

Identification of miRNA signatures for kidney renal clear cell carcinoma using the tensor-decomposition method

Cancer is a highly complex disease caused by multiple genetic factors. MicroRNA (miRNA) and mRNA expression profiles are useful for identifying prognostic biomarkers for cancer. Kidney renal clear cell carcinoma (KIRC), which accounts for more than 70% of all renal malignant tumour cases, was selected for our analysis.Traditional methods of identifying cancer prognostic markers may not be accurate. Tensor decomposition (TD) is a useful method uncovering the underlying low-dimensional structures in the tensor. The TD-based unsupervised feature extraction method was applied to analyse mRNA and miRNA expression profiles. Biological annotations of the prognostic miRNAs and mRNAs were examined utilizing the pathway and oncogenic signature databases DIANA-miRPath and MSigDB.TD identified the miRNA signatures and the associated genes. These genes were found to be involved in cancer-related pathways, and 23 genes were significantly correlated with the survival of KIRC patients. We demonstrated that the results are robust and not highly dependent upon the databases we selected. Compared with traditional supervised methods tested, TD achieves much better performance in selecting prognostic miRNAs and mRNAs.These results suggest that integrated analysis using the TD-based unsupervised feature extraction technique is an effective strategy for identifying prognostic signatures in cancer studies.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2019

Computational analysis of molecular networks using spectral graph theory, complexity measures and information theory

Molecular networks are described in terms of directed multigraphs, so-called network motifs. Spectral graph theory, reciprocal link and complexity measures were utilized to quantify network motifs. It was found that graph energy, reciprocal link and cyclomatic complexity can optimally specify network motifs with some degree of degeneracy. Biological networks are built up from a finite number of motif patterns; hence, a graph energy cutoff exists and the Shannon entropy of the motif frequency distribution is not maximal. Also, frequently found motifs are irreducible graphs. Network similarity was quantified by gauging their motif frequency distribution functions using Jensen-Shannon entropy. This method allows us to determine the distance between two networks regardless of their nodes identities and network sizes. This study provides a systematic approach to dissect the complex nature of biological networks. Our novel method different from any other approach. The findings support the view that there are organizational principles underlying molecular networks.

systems biology↗

bioRxiv2018

Tensor decomposition--based unsupervised feature extraction for integrated analysis of TCGA data on microRNA expression and promoter methylation of genes in ovarian cancer

Integrated analysis of epigenetic profiles is important but difficult. Tensor decomposition-based unsupervised feature extraction was applied here to data on microRNA (miRNA) expression and promoter methylation of genes in ovarian cancer. It selected seven miRNAs and 241 genes by expression levels and promoter methylation degrees, respectively, such that they showed differences between eight normal ovarian tissue samples and 569 tumor samples. The expression levels of the seven miRNAs and the degrees of promoter methylation of the 241 genes also correlated significantly. Conventional Students t test-based feature selection failed to identify miRNAs and genes that have the above properties. On the other hand, biological evaluation of the seven identified miRNAs and 241 identified genes suggests that they are strongly related to cancer as expected.

bioinformatics↗