Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Yue Joseph Wang"'
Autor:
Yi Fu, Yingzhou Lu, Bai Zhang, Yue Joseph Wang, Zhen Zhang, David M. Herrington, Robert Clarke, Van Eyk Je
SummaryData-driven differential dependency network analysis identifies in a complex and often unknown overall molecular circuitry a network of differentially connected molecular entities (pairwise selective coupling or uncoupling depending on the spe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f9857557dff715ed145a3a438c27628
https://doi.org/10.1101/2021.04.10.439301
https://doi.org/10.1101/2021.04.10.439301
Autor:
Niya Wang, Douglas A. Levine, Jianhua Xuan, Ie Ming Shih, Lulu Chen, Zhen Zhang, Yue Joseph Wang, Robert Clarke, Ting Gong
Publikováno v:
Bioinformatics. 31:137-139
Summary: We develop a novel unsupervised deconvolution method, within a well-grounded mathematical framework, to dissect mixed gene expressions in heterogeneous tumor samples. We implement an R package, UNsupervised DecOnvolution (UNDO), that can be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d6257c6d65746576ecf3ac05f3b8e7f5
https://doi.org/10.1093/bioinformatics/btu607
https://doi.org/10.1093/bioinformatics/btu607
Autor:
Bai Zhang, Ie-Ming Shih, Zhen Zhang, David M. Herrington, Yue Joseph Wang, Jianhua Xuan, Ye Tian, Robert Clarke, Eric P. Hoffman
Publikováno v:
Bioinformatics. 31:287-289
Summary: We have developed an integrated molecular network learning method, within a well-grounded mathematical framework, to construct differential dependency networks with significant rewiring. This knowledge-fused differential dependency networks
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1d19935310dca24e73a8fa5b8962ea3c
https://doi.org/10.1093/bioinformatics/btu632
https://doi.org/10.1093/bioinformatics/btu632
Autor:
Michael Nebozhyn, Malik Yousef, Yitan Zhu, Jiajing Wang, Jianhua Xuan, Louise C. Showe, Yue Joseph Wang, Robert Clarke, Huai Li, Michael Showe
Publikováno v:
Bioinformatics. 23:2024-2027
Summary: VISDA (Visual Statistical Data Analyzer) is a caBIG™ analytical tool for cluster modeling, visualization and discovery that has met silver-level compatibility under the caBIG initiative. Being statistically principled and visually interfac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c4263894ade8804230737bdc36f17c19
https://doi.org/10.1093/bioinformatics/btm290
https://doi.org/10.1093/bioinformatics/btm290
Autor:
Eric P. Hoffman, Bai Zhang, Subha Madhavan, Huai Li, Leena Hilakivi-Clarke, Yue Joseph Wang, Robert Clarke, Ie-Ming Shih, Ye Tian, Lu Jin, Jianhua Xuan
Publikováno v:
Bioinformatics. 27:1036-1038
Summary: Differential dependency network (DDN) is a caBIG® (cancer Biomedical Informatics Grid) analytical tool for detecting and visualizing statistically significant topological changes in transcriptional networks representing two biological condi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77d37cc37589f222b05ad38927b5e44f
https://doi.org/10.1093/bioinformatics/btr052
https://doi.org/10.1093/bioinformatics/btr052
Autor:
Subha Madhavan, Yue Joseph Wang, Guoqiang Yu, Ie-Ming Shih, Jianhua Xuan, Eric P. Hoffman, Sook S. Ha, Robert Clarke, Huai Li
Publikováno v:
Bioinformatics. 27:736-738
Summary: Phenotypic Up-regulated Gene Support Vector Machine (PUGSVM) is a cancer Biomedical Informatics Grid (caBIG™) analytical tool for multiclass gene selection and classification. PUGSVM addresses the problem of imbalanced class separability,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::64509b688c5ba1020550b275ac17e8a6
https://doi.org/10.1093/bioinformatics/btq721
https://doi.org/10.1093/bioinformatics/btq721
Autor:
Guoqiang Yu, Roger R. Wang, Xuchu Hou, Ie Ming Shih, Yi Fu, Yue Joseph Wang, Robert Clarke, Xiguo Yuan, Zhen Zhang, Subha Madhavan, Bai Zhang
Publikováno v:
Bioinformatics (Oxford, England). 30(3)
Summary: Accurate identification of significant aberrations in cancers (AISAIC) is a systematic effort to discover potential cancer-driving genes such as oncogenes and tumor suppressors. Two major confounding factors against this goal are the normal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4cc679ab124a827b1ae8cc1ad1247a9d
https://pubmed.ncbi.nlm.nih.gov/24292941
https://pubmed.ncbi.nlm.nih.gov/24292941
Publikováno v:
International Journal of Imaging Systems and Technology. 9:340-350
We present a segmentation scheme for magnetic resonance (MR) image sequences based on vector quantization of a block-partitioned image followed by a relaxation labeling procedure. By first searching a coarse segmentation, the algorithm yields very fa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::efc01abeec7e2b60c38b51740cef1ccd
https://doi.org/10.1002/(sici)1098-1098(1998)9:5<340::aid-ima4>3.0.co
https://doi.org/10.1002/(sici)1098-1098(1998)9:5<340::aid-ima4>3.0.co
Autor:
Yehia Mechref, Mahlet G. Tadesse, Lewis K. Pannell, Yue Joseph Wang, Cristina Di Poto, Tsung-Heng Tsai, Habtom W. Ressom
Publikováno v:
Bioinformatics (Oxford, England). 29(21)
Motivation: Liquid chromatography-mass spectrometry (LC-MS) has been widely used for profiling expression levels of biomolecules in various ‘-omic’ studies including proteomics, metabolomics and glycomics. Appropriate LC-MS data preprocessing ste
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e6b952a903a68c087a66c5eb426c5be4
https://pubmed.ncbi.nlm.nih.gov/24013927
https://pubmed.ncbi.nlm.nih.gov/24013927
Autor:
Yue Joseph Wang, Joel M. Morris
Publikováno v:
Journal of biomedical optics. 1(3)
In ultrasound tomography the time-domain moment method is very promising in that it has been shown to yield a close agreement between the time-spatial moment expansion and the true field representation. This paper introduces a numerical technique to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::275480e5a17d5ea59b2c1a49399fcca1
https://pubmed.ncbi.nlm.nih.gov/23014732
https://pubmed.ncbi.nlm.nih.gov/23014732