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Browsing by Author "Zhang, Wei"

Now showing 1 - 6 of 6
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    Computational Analysis of Transcript Interactions and Variants in Cancer
    (2015-11) Zhang, Wei
    New sequencing and array technologies for transcriptome-wide profiling of RNAs have greatly promoted the interest in gene and isoform-based functional characterizations of a cellular system. Many statistical and machine learning methods have been developed to quantify the isoform/gene expression and identify the transcript variants for cancer outcome prediction. Since building reliable learning models for cancer transcriptome analysis relies on accurate modeling of prior knowledge and interactions between the cellular components, it is still a computational challenge. This thesis proposes several robust and reliable learning models to integrate both large-scale array and sequencing data with biological prior knowledge for cancer transcriptome analysis. First, we explore two signed network propagation algorithms and general optimization frameworks for detecting differential gene expressions and DNA copy number variations (CNV). Second, we present a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets to identify highly consistent signature genes and improve the accuracy of survival prediction. Third, we introduce a Network-based method for RNA-Seq-based Transcript Quantification (Net-RSTQ) to integrate protein domain-domain interaction network with short read alignments for transcript abundance estimation. Finally, we perform computational analysis of mRNA 3'-UTR shortening on mouse embryonic fibroblast (MEF) cell lines to understand changes of molecular features on dysregulated activation of mammalian target of rapamycin (mTOR). We evaluate our models and findings with simulations and real genomic datasets. The results suggest that our models explore the global topological information in the networks, improve the transcript quantification for better sample classification, identified consistent biomarkers to improve cancer prognosis and survival prediction. The analysis of 3'-UTR with RNA-Seq data find an unexpected link between mTOR and ubiquitin-mediated proteolysis pathway through 3'-UTR shortening.
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    Functional characterization of the three isoforms of Fbw7 (F-box and WD repeat domain containing 7) in ubiquitin dependent proteolysis.
    (2010-01) Zhang, Wei
    Fbw7 is the F-box protein of SCFFbw7 E3 ubiquitin ligase, which specifically associates with the substrates to be ubiquitinated. Substrates of Fbw7 play important roles in cell cycle regulation, proliferation, signal transduction and metabolism, which are related to tumor formation, suggesting that Fbw7 functions as a tumor suppressor. Fbw7 has three splicing variants α, β and γ, and the biological function of each isoform is not well understood. Our lab is interested in how the Fbw7 isoforms regulate cyclin E proteolysis and the cell cycle. By using mammalian and insect cell culture systems, I demonstrate that the three isoforms can form homo- and heterodimers in vivo and in vitro. The dimerization domain is located immediately upstream of the F-box motif, and it is highly conserved in all Fbw7 homologues and other related F-box proteins, indicating the dimerization may be common feature of a subset of F-box proteins. Abolishment of dimerization inhibits cyclin E proteolysis and leads to a prolonged half-life of cyclin E, although it does not affect Fbw7 binding to cyclin E or to the Cul-Rbx1-Skp1 E3 catalytic module. Cyclin E accumulation can be commonly found in many primary tumors and cancer cell lines. These results suggest a novel mechanism of how F-box proteins recognize their substrates. Fbw7 isoforms show different protein stabilities, where the α isoform is stable, but the β and γ isoforms are not. The stability of the β and γ isoforms is largely controlled by their N- terminal unique region. In order to better understand the mechanism regulating their stability, we performed a yeast two hybrid screen and identified SLP1 (stomatin like protein 1) as an Fbw7γ isoform specific interacting protein. SLP1 binds to the unique region of γ isoform, and stabilizes γ. We find that Cdk2 promotes the degradation of both SLP1 and the γ isoform, and this function of Cdk2 is dependent on its kinase activity. SLP1 also physically interacts with Cdk2 through its membrane association domain. These results support a model in which Fbw7γ and SLP1 are coordinately targeted for ubiquitin mediated degradation by Cdk2.
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    Inferring Disease and Gene Set Associations with Rank Coherence in Networks
    (2011-01-18) Hwang, TaeHyun; Zhang, Wei; Xie, MaoQiang; Kuang, Rui
    A computational challenge to validate the candidate disease genes identi?ed in a high-throughput genomic study is to elucidate the associations between the set of candidate genes and disease phenotypes. The conventional gene set enrichment analysis often fails to reveal associations between disease phenotypes and the gene sets with a short list of poorly annotated genes, because the existing annotations of disease causative genes are incomplete. We propose a network-based computational approach called rcNet to discover the associations between gene sets and disease phenotypes. Assuming coherent associations between the genes ranked by their relevance to the query gene set, and the disease phenotypes ranked by their relevance to the hidden target disease phenotypes of the query gene set, we formulate a learning framework maximizing the rank coherence with respect to the known disease phenotype-gene associations. An e?cient algorithm coupling ridge regression with label propagation, and two variants are introduced to ?nd the optimal solution of the framework. We evaluated the rcNet algorithms and existing baseline methods with both leave-one-out cross-validation and a task of predicting recently discovered disease-gene associations in OMIM. The experiments demonstrated that the rcNet algorithms achieved the best overall rankings compared to the baselines. To further validate the reproducibility of the performance, we applied the algorithms to identify the target diseases of novel candidate disease genes obtained from recent studies of GWAS, DNA copy number variation analysis, and gene expression pro?ling. The algorithms ranked the target disease of the candidate genes at the top of the rank list in many cases across all the three case studies. The rcNet algorithms are available as a webtool for disease and gene set association analysis at http://compbio.cs.umn.edu/dgsa_rcNet.
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    Memory Design for Centimeter-Scale Organic and Nanometer-Scale Silicon Technologies
    (2012-07) Zhang, Wei
    Low power memory is always desired due to its significance in many large-scale applications. It is important to emerging technologies such as organic electronics, since it is an indispensible component to extend the technology towards larger application scope with complicated functionalities. It is also a hot topic in the mature silicon technology because the device scaling makes memory designs challenging with increasing leakage currents and process variations. Organic electronics deals with conductive polymers and plastics, and is capable of realizing large area flexible applications, which cannot be fulfilled by modern silicon technology. Conventional organic devices require a high operation voltage due to its low carrier mobility. Ion-gel gated OTFTs (gel-OTFTs), however, deliver unusually high gate capacitance through an electrolyte-gated structure, and therefore offer sufficient drive currents under a low voltage. Being an emerging technology, few attempts have been made on organic memory designs. In this dissertation, we first propose an improved design-fabrication-testing flow to significantly facilitate the entire process, which boosts the design efficiency and fabrication yield and thus enables the implementation of complex circuits such as memory array. An organic process design kit (OPDK) with various modeling approaches allows designers to easily design organic circuits in a similar way as that in silicon technology. Various circuit components including logic gates, ring oscillators and a D-flipflop were demonstrated and a general purpose organic dynamic memory cell was proposed for the first time. The cell, known as a DRAM gain cell, achieves a sub-10nW-per-cell refresh power with a retention time of over 1 minute, which is 5 orders of magnitude longer than that in silicon designs. The same DRAM gain cell architecture is also found potential as embedded memory in the modern silicon technology, where the prevailing 6T SRAM is suffering from leakage power and poor low voltage margin when devices keep scaling down. In this dissertation we report the first variation-aware performance analysis on the silicon gain cell and reveal that conventional corner simulations are no longer valid in capturing worst cases of gain cells. Insights can be obtained through the various analysis approaches described in the dissertation to benefit future memory design strategy and device optimization. With innovations in cell structure and peripheral circuitry, the silicon gain cell performance can be further enhanced to compete with the mainstream 6T SRAM. In this dissertation, we for the first time experimentally demonstrate a gain cell design with write-back-free read operations, utilizing its non-destructive read nature to improve the read speed into GHz regime without sacrificing retention time. Various circuit techniques including a local-sense-amplifier architecture are proposed to eliminate the need of a complex current-sensing scheme, and a dual-row-access mode is proposed for further power saving in half-utilization scenarios. The test chip in a 65nm low power process achieves a 23.9% power saving compared to a 6T SRAM at 0.6V retention voltage and an additional 27.8% power saving during cases when only half array is needed.
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    Reassessment of Diametral Compression Test on Asphalt Concrete
    (Minnesota Department of Transportation, 1996-12) Drescher, Andrew; Newcomb, David; Zhang, Wei
    This report examines the diametral compression test, as described in ASTM D4123-82 (1987) and SHRP Protocol P07 (1993) procedures. The test helps determine the resilient modulus of asphalt concrete, and less frequently its Poisson's ratio, both mechanical parameters of an ideally elastic material. However, the actual behavior of asphalt concrete is not elastic, but viscoelastic. The viscoelastic behavior of asphalt concrete under traffic-induced loads can be described by the phase angle and the magnitude of the complex compliance or complex modulus. These can be determined from the diametral compression tests that subject the specimen to haversine load history, and from the viscoelastic data interpretation algorithms derived in the current research. To avoid inaccuracies in the data interpretation, the vertical deformation should be measured over a 1/4 diameter central sector of the cylinder by means, for example, of the in-house developed displacement gage. A series of tests on specimens with various asphalt binder viscosity verified the validity of the viscoelastic data interpretation. Specimens from Mn/ROAD materials showed the presence of viscoelastic properties even at temperatures well below freezing.
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    Vehicle networks: achieving regular formation
    (2002-07) Chaves, Madalena; Day, Robert; Gomez-Ramos, Lucia; Nag, Parthasarathi; Williams, Anca; Zhang, Wei; Glavaski, Sonja (mentor)
    In this paper we will consider a network of vehicles exchanging information among themselves with the intention of achieving a specified polygonal formation. The network achieves the formation through decentralized feedback control, which is constructed from the available information. Several information flow laws are considered in order to improve the performance of the vehicle network. A stochastic model for information flow is also considered, allowing for the randomly breaking of the communication links among the vehicles.