Yue Joseph Wang

Bio: Yue Joseph Wang is an academic researcher from Virginia Tech. The author has contributed to research in topics: Image registration & Visualization. The author has an hindex of 16, co-authored 47 publications receiving 599 citations. Previous affiliations of Yue Joseph Wang include Children's National Medical Center & The Catholic University of America.

Inferring condition-specific miRNA-Gene modules from miRNA and mRNA profiling data

Abstract: Recently, a class of small RNA molecules, microRNAs or miRNAs, has attracted interest from researchers for their unique role in post-transcriptional regulation. Due to their distinct cell-type/tissue-specific expression patterns, it is of high importance to identify conditionspecific miRNA-gene modules for a complete depiction of gene regulatory networks. In this paper, we propose a novel method to integrate miRNA and mRNA data to identify condition-specific miRNA-gene modules. Specifically, a Consistency-based Masking Nonnegative Matrix Factorization (CMNMF) method is developed to incorporate existing biological constraints (like the repression of miRNAs on potential target genes) with simultaneous miRNA and mRNA profiling data for an improved performance in module identification. The experimental results on simulation data show that the condition-specific modeling framework improves the performance in predicting miRNA-gene relationships. More importantly, application of CMNMF to human colon cancer data revealed a biologically significant miRNA-gene module, which contains four up-regulated miRNAs (miR-182, miR-183, miR-221 and miR-222) and six down-regulated target genes annotated as cytotoxity mediated by nature killer cells. The proposed method can also be applied to various biological conditions, even with limited number of samples, to elucidate miRNAinvolved gene networks.

ModVis: An information visualization tool for gene module discovery

Abstract: The repeated grouping of genes across module sets produced by different module discovery methods may prove to be biologically significant in the area of functional genomics. To ease in the exploration of these overlapping groups of genes, we introduce ModVis, a software tool that allows for the interactive visualization of module discovery data, with an emphasis on the overlap between data sets derived from different methods. Our tool provides a high-level overview of multiple module sets, displaying the modules in each set, and the location and magnitude of the overlap between sets. The tool’s more-detailed visualization displays a heat map for a particular gene module, and organizes the heat maps for its overlapping modules so that the individual genes involved in the overlap become readily apparent.

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

Abstract: Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications.

Copy number analysis indicates monoclonal origin of lethal metastatic prostate cancer

Abstract: Many studies have shown that primary prostate cancers are multifocal and are composed of multiple genetically distinct cancer cell clones. Whether or not multiclonal primary prostate cancers typically give rise to multiclonal or monoclonal prostate cancer metastases is largely unknown, although studies at single chromosomal loci are consistent with the latter case. Here we show through a high-resolution genome-wide single nucleotide polymorphism and copy number survey that most, if not all, metastatic prostate cancers have monoclonal origins and maintain a unique signature copy number pattern of the parent cancer cell while also accumulating a variable number of separate subclonally sustained changes. We find no relationship between anatomic site of metastasis and genomic copy number change pattern. Taken together with past animal and cytogenetic studies of metastasis and recent single-locus genetic data in prostate and other metastatic cancers, these data indicate that despite common genomic heterogeneity in primary cancers, most metastatic cancers arise from a single precursor cancer cell. This study establishes that genomic archeology of multiple anatomically separate metastatic cancers in individuals can be used to define the salient genomic features of a parent cancer clone of proven lethal metastatic phenotype.

Integrative approaches for finding modular structure in biological networks

Abstract: A central goal of systems biology is to elucidate the structural and functional architecture of the cell. To this end, large and complex networks of molecular interactions are being rapidly generated for humans and model organisms. A recent focus of bioinformatics research has been to integrate these networks with each other and with diverse molecular profiles to identify sets of molecules and interactions that participate in a common biological function - that is, 'modules'. Here, we classify such integrative approaches into four broad categories, describe their bioinformatic principles and review their applications.

Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum

Abstract: The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest among researches and practitioners in a wide variety of fields. The SOM has been analyzed extensively, a number of variants have been developed and, perhaps most notably, it has been applied extensively within fields ranging from engineering sciences to medicine, biology, and economics. We have collected a comprehensive list of 5384 scientific papers that use the algorithms, have benefited from them, or contain analyses of them. The list is intended to serve as a source for literature surveys. The present addendum contains 2092 new articles, mainly from the years 1998-2002. We have provided a keyword index to help finding articles of interest, and additionally a modern automatically constructed variant of a thematic index: a WEBSOM interface to the whole article collection of years 1981-2000. The SOM of SOMs is available at http://websom.hut.fi/websom/somref/search.cgi for browsing and searching the collection.