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Open Access Publications from the University of California

Scholarly Works (4 results)

  • Thesis
  • Peer Reviewed
UC Irvine Electronic Theses and Dissertations (2017)

Big data continues to grow and diversify at an increasing pace. To understand constantly evolving situations, data is collected from various location-based sensors as well as people using effective participatory sensing. Static sensors are placed at particular locations, monitoring and measuring important variables from the environment. Additionally, people contribute data in the form of mobile streams through participatory sensing. To process such disparate data for situation recognition, we need to address two major research challenges, namely data quality and data analysis.

This dissertation presents graph-based models as a general framework to combine diverse data sources for situation recognition. To improve spatial data quality, we propose a multimodal geospatial prediction model that integrates heterogeneous data into a unified space-time-value format, extracts multi-resolution spectral features, and predicts values at unobserved locations. We demonstrate the effectiveness of the proposed model using air pollution data. We then combine predicted air pollution data with pollen data to analyze the situation of asthma risk across California. The proposed model based on our predicted values provides greater accuracy in detecting high asthma risk areas compared to previous studies.

Given the wide use of mobile technology, humans also act as sensors and increasingly contribute photos as mobile data streams. We investigate the use of visual concepts for situation recognition and develop new models to tackle the technical challenges of noisy data and processing real-time data, which are problems in existing approaches. First, we propose a graph-regularized linear regression PCA to address photo clustering in a real-time setting. Second, to overcome the noisy data that degrades graph quality, leading to poor recognition results, we incorporate a capped norm into a graph embedding method to remove the adverse effects of outliers. Our models not only outperform existing approaches in overcoming the problems of noisy data and real-time data processing, but also allow us to utilize a new cluster of data through a soft label method. Finally, we apply the proposed approaches to Yahoo Flicker 100 Million photos for detecting evolving situations.

    Cover page: Graph-based Geospatial Prediction and Clustering for Situation Recognition
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2013)
    Learning gene expression programs directly from a set of observations is challenging due to the complexity of gene regulation, high noise of experimental measurements, and insufficient number of experimental measurements. Imposing additional constraints with strong and biologically motivated regularizations is critical in developing reliable and effective algorithms for inferring gene expression programs. Here we propose a new form of regulation that constrains the number of independent connectivity patterns between regulators and targets, motivated by the modular design of gene regulatory programs and the belief that the total number of independent regulatory modules should be small. We formulate a multi-target linear regression framework to incorporate this type of regulation, in which the number of independent connectivity patterns is expressed as the rank of the connectivity matrix between regulators and targets. We then generalize the linear framework to nonlinear cases, and prove that the generalized low-rank regularization model is still convex. Efficient algorithms are derived to solve both the linear and nonlinear low-rank regularized problems. Finally, we test the algorithms on three gene expression datasets, and show that the low-rank regularization improves the accuracy of gene expression prediction in these three datasets.
      Cover page: Low-rank regularization for learning gene expression programs.
      • Article
      • Peer Reviewed
      UC Irvine Previously Published Works (2016)
      The mismatch repair (MMR) family is a highly conserved group of proteins that function in correcting base-base and insertion-deletion mismatches generated during DNA replication. Disruption of this process results in characteristic microsatellite instability (MSI), repair defects, and susceptibility to cancer. However, a significant fraction of MSI-positive cancers express MMR genes at normal levels and do not carry detectable mutation in known MMR genes, suggesting that additional factors and/or mechanisms may exist to explain these MSI phenotypes in patients. To systematically investigate the MMR pathway, we conducted a proteomic analysis and identified MMR-associated protein complexes using tandem-affinity purification coupled with mass spectrometry (TAP-MS) method. The mass spectrometry data have been deposited to the ProteomeXchange with identifier PXD003014 and DOI 10.6019/PXD003014. We identified 230 high-confidence candidate interaction proteins (HCIPs). We subsequently focused on MSH2, an essential component of the MMR pathway and uncovered a novel MSH2-binding partner, WDHD1. We further demonstrated that WDHD1 forms a stable complex with MSH2 and MSH3 or MSH6,i.e.the MutS complexes. The specific MSH2/WDHD1 interaction is mediated by the second lever domain of MSH2 and Ala(1123)site of WDHD1. Moreover, we showed that, just like MSH2-deficient cells, depletion of WDHD1 also led to 6-thioguanine (6-TG) resistance, indicating that WDHD1 likely contributes to the MMR pathway. Taken together, our study uncovers new components involved in the MMR pathway, which provides candidate genes that may be responsible for the development of MSI-positive cancers.
        Cover page: Proteomic Analysis Reveals a Novel Mutator S (MutS) Partner Involved in Mismatch Repair Pathway*
        • Article
        • Peer Reviewed
        UC Irvine Previously Published Works (2013)
        • 1 supplemental PDF
        Cover page: Low-Rank Regularization for Learning Gene Expression ProgramsCreative Commons 'BY' version 4.0 license
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