Biomedical subjects

Sungroh Yoon

Publications and source records attributed to Sungroh Yoon.

3 recordsLinked to original sources

PubMed2006

Computational identification of microRNAs and their targets.

One of the most important advances in biology in recent years may be the discovery of RNAs that can regulate gene expression. As one kind of such functional noncoding RNAs, microRNAs (miRNAs) form a class of endogenous 19-23-nucleotide RNAs that can have important regulatory roles in animals and plants by targeting transcripts for cleavage or translational repression. Since the discovery of the very first miRNAs, computational methods have been an invaluable tool that can complement experimental approaches to understand the biology of miRNAs. Most computational approaches associated with miRNA research can be classified into two broad categories, namely miRNA gene identification and miRNA target prediction. In this review, we summarize the principles of in silico prediction of miRNAs and their targets, and provide a comprehensive survey of specific methods that have been proposed in the field.

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PubMed2005

Prediction of regulatory modules comprising microRNAs and target genes.

MOTIVATION: MicroRNAs (miRNAs) are small endogenous RNAs that can play important regulatory roles via the RNA-interference pathway by targeting mRNAs for cleavage or translational repression. We propose a computational method to predict miRNA regulatory modules (MRMs) or groups of miRNAs and target genes that are believed to participate cooperatively in post-transcriptional gene regulation. RESULTS: We tested our method with the human genes and miRNAs, predicting 431 MRMs. We analyze a module with genes: BTG2, WT1, PPM1D, PAK7 and RAB9B, and miRNAs: miR-15a and miR-16. Review of the literature and annotation with Gene Ontology terms reveal that the roles of these genes can indeed be closely related in specific biological processes, such as gene regulation involved in breast, renal and prostate cancers. Furthermore, it has been reported that miR-15a and miR-16 are deleted together in certain types of cancer, suggesting a possible connection between these miRNAs and cancers. Given that most known functionalities of miRNAs are related to negative gene regulation, extending our approach and exploiting the insight thus obtained may provide clues to achieving practical accuracy in the reverse-engineering of gene regulatory networks. AVAILABILITY: A list of predicted modules is available from the authors upon request.

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PubMedDate not supplied

Discovering coherent biclusters from gene expression data using zero-suppressed binary decision diagrams.

The biclustering method can be a very useful analysis tool when some genes have multiple functions and experimental conditions are diverse in gene expression measurement. This is because the biclustering approach, in contrast to the conventional clustering techniques, focuses on finding a subset of the genes and a subset of the experimental conditions that together exhibit coherent behavior. However, the biclustering problem is inherently intractable, and it is often computationally costly to find biclusters with high levels of coherence. In this work, we propose a novel biclustering algorithm that exploits the zero-suppressedbinary decision diagrams (ZBDDs) data structure to cope with the computational challenges. Our method can find all biclusters that satisfy specific input conditions, and it is scalable to practical gene expression data. We also present experimental results confirming the effectiveness of our approach.

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