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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.

Papers
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Proceedings ArticleDOI
15 May 2003
TL;DR: A new registration method has been developed using finite-element deformable sheet-curve models to accurately register MR breast images for extraction of glandular tissue and the experimental results demonstrate that this method is of a great improvement over point-based registration methods in both boundary alignment and local deformation recovery.
Abstract: It is clinically important to develop novel approaches to accurately assess early response to chemoprevention. We propose to quantitatively measure changes of breast density and breast vascularity in glandular tissue to assess early response to chemoprevention. In order to accurately extract glandular tissue using pre- and post-contrast magnetic resonance (MR) images, non-rigid registration is the key to align MR images by recovering the local deformations. In this paper, a new registration method has been developed using finite-element deformable sheet-curve models to accurately register MR breast images for extraction of glandular tissue. Finite-element deformable sheet-curve models are coupling dynamic systems to physically model the boundary deformation and image deformation. Specifically, deformable curves are used to obtain a reliable matching of the boundaries using physically constrained deformations. A deformable sheet with the energy functional of thin-plate-splines is used to model complex local deformations between the MR breast images. Finite-element deformable sheet-curve models have been applied to register both digital phantoms and MR breast image. The experimental results have been compared to point-based methods such as the thin-plate-spline (TPS) approach, which demonstrates that our method is of a great improvement over point-based registration methods in both boundary alignment and local deformation recovery.

1 citations

Proceedings ArticleDOI
26 May 1999
TL;DR: The development of an automated and intelligent procedure for generating the hierarchy of minimize entropy models and principal component visualization spaces for improved data explanation is reported, both statistically principles and visually effective at revealing all of the interesting aspects of the data set.
Abstract: As a step toward understanding the complex information from data and relationships, structural and discriminative knowledge reveals insight that may prove useful in data interpretation and exploration. This paper reports the development of an automated and intelligent procedure for generating the hierarchy of minimax entropy models and principal component visualization spaces for improved data explanation. The proposed hierarchical minimax entropy modeling and probabilistic principal component projection are both statistically principled and visually effective at revealing all of the interesting aspects of the data set. The methods involve multiple use of standard finite normal mixture models and probabilistic principal component projections. The strategy is that the top-level model and projection should explain the entire data set, best revealing the presence of clusters and relationships, while lower-level models and projections should display internal structure within individual clusters, such as the presence of subclusters and attribute trends, which might not be apparent in the higher-level models and projections. With many complementary mixture models and visualization projections, each level will be relatively simple while the complete hierarchy maintains overall flexibility yet still conveys considerable structural information. In particular, a model identification procedure is developed to select the optimal number and kernel shapes of local clusters from a class of data, resulting in a standard finite normal mixtures with minimum conditional bias and variance, and a probabilistic principal component neural network is advanced to generate optimal projections, leading to a hierarchical visualization algorithm allowing the complete data set to be analyzed at the top level, with best separated subclusters of data points analyzed at deeper levels. Hierarchical probabilistic principal component visualization involves (1) evaluation of posterior probabilities for mixture data set, (2) estimation of multiple principal component axes from probabilistic data set, and (3) generation of a complete hierarchy of visual projections. With a soft clustering of the data set t i via the EM algorithm, data points will effectively belong to more than one cluster at any given level with posterior probabilities denoted by z ik . Thus, the effective input values are z ik t i for an independent visualization space k in the hierarchy. Further projections can again be performed using the effective input values z ik z j|k t i for the visualization subspace j. The complete visual explanation hierarchy is generated by performing principal projection (dimensionality reduction) and model identification (structure decomposition) in two iterative steps using information theoretic criteria, EM algorithm, and probabilistic principal component analysis.

1 citations

Proceedings ArticleDOI
15 May 2003
TL;DR: A blind source separation method is proposed which allows for a computed simultaneous imaging of multiple biomarkers from composite DCE-MRI sequences and is based on a partially-independent component analysis, whose parameters are estimated using a subset of informative pixels defining the independent portion of the observations.
Abstract: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has emerged as an effective tool to access tumor vascular characteristics. DCE-MRI can be used to characterize noninvasively, microvasculature providing information about tumor microvessel structure and function (e.g., tumor blood volume, vascular permeability, tumor perfusion). However, pixels of DCE-MRI represent a composite of more than one distinct functional biomarker (e.g., microvessels with fast or slow perfusion) whose spatial distributions are often heterogeneous. Complementary to various existing methods (e.g., compartment modeling, factor analysis), this paper proposes a blind source separation method which allows for a computed simultaneous imaging of multiple biomarkers from composite DCE-MRI sequences. The algorithm is based on a partially-independent component analysis, whose parameters are estimated using a subset of informative pixels defining the independent portion of the observations. We demonstrate the principle of the approach on simulated image data set, and we then apply the method to the tissue heterogeneity characterization of breast tumors where spatial distribution of tumor blood volume, vascular permeability, and tumor perfusion, as well as their time activity curves (TACs) are simultaneously estimated.

1 citations

Proceedings ArticleDOI
25 Apr 1997
TL;DR: A neural network based technique for the quantification of MR brain image sequences by utilizing a newly developed information theoretic criterion to determine the suitable number of mixture components such that the network can adjust its structure to the characteristics of each image in the sequence.
Abstract: This paper presents a neural network based technique for the quantification of MR brain image sequences. We studied image statistics to justify the correct use of the standard finite normal mixture model and formulated image quantification as a distribution learning problem. From information theory, we used relative entropy as the information distance measure and developed an adaptive structure probabilistic self- organizing mixture to estimate the parameter values. New learning scheme has the capability of achieving flexible classifier shapes in terms of winner-takes-in probability splits of data, allowing data to contribute simultaneously to multiple regions. The result is unbiased and holds the asymptotic properties of maximum likelihood estimation. To achieve a fully automatic function and incorporate the correlation between slices, we utilized a newly developed information theoretic criterion (minimum conditional bias/variance) to determine the suitable number of mixture components such that the network can adjust its structure to the characteristics of each image in the sequence. Compared with the results of the algorithms based on expectation- maximization, K-means, and Kohonen's self-organizing map, the new method yields a very efficient and accurate performance.

1 citations


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Journal ArticleDOI
28 Jul 2016-Cell
TL;DR: A view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC is provided.

728 citations

Journal ArticleDOI
TL;DR: The recent progress of SVMs in cancer genomic studies is reviewed and the strength of the SVM learning and its future perspective incancer genomic applications is comprehended.
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.

635 citations

Journal ArticleDOI
TL;DR: It is shown 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.
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.

631 citations

Journal ArticleDOI
TL;DR: This work classifies such integrative approaches into four broad categories, describes their bioinformatic principles and review their applications.
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.

532 citations

01 Jan 2003
TL;DR: This work has 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.
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.

402 citations