Statistical and Functional Analysis of Genomic and Proteomic Data

High-throughput technologies have led to an explosion in the availability of data at the genome scale. Such data provide important information about cellular processes and causes of human diseases, as well as for drug discovery. Deciphering the biologically relevant results from these data requires comprehensive analytical methods. In this dissertation, we present methods for gene and protein expression data analysis. Our major contributions include a method for differential in-gelelectrophoresis data analysis capable of removing protein-specific dye bias in the data, a method for finding unknown biological groups using expression data, and a method for identifying active and inactive signaling pathways in a gene expression signature based on the enrichment of downstream target genes of pathways

Heterogeneous Information and Appraisal Smoothing

This study examines the heterogeneous appraiser behavior and its implication on the traditional appraisal smoothing theory. We show that the partial adjustment model is consistent with the traditional appraisal smoothing argument only when all the appraisers choose the same smoothing technique. However, if appraiser behavior is heterogeneous and exhibits cross-sectional variation due to the difference in their access to, and interpretation of information, the model actually leads to a mixed outcome: The variance of the appraisal-based returns can be higher or lower than the variance of transaction-based return depending on the degree of such heterogeneity. Using data from the residential market, we find that, contrary to what the traditional appraisal smoothing theory would predict, appraisal-based indices may not suffer any “smoothing” bias. These findings suggest that the traditional appraisal smoothing theory, which fails to consider the heterogeneity of appraiser behaviors, exaggerates the effect of appraisal smoothing.

Explaining the Black-White Homeownership Gap: The Role of Own Wealth, Parental Externalities and Locational Preferences

African Americans in the United States are considerably less likely to own their homes compared to Whites. Differences in household income and other socio-economic and demographic characteristics can only partially explain this gap and previous studies suggest that the ‘unexplained’ gap has increased over time. In this paper we use the Panel Study of Income Dynamics (PSID) intergenerational data, which provides information on household wealth, parental characteristics and macro-location choice. We find that African-American households are 6.5 percent less likely to own if only traditional explanatory variables are controlled for. However, the black-white homeownership gap disappears if differences in own and parental wealth and in the preferred macro-location type are accounted for.Homeownership; housing tenure choice; location choice; wealth effects; intergenerational effects

Ownership Restriction and Housing Value: Evidence from American Housing survey

Amendments to the Fair Lending Act have exempted an age restriction on ownership from fair housing prohibitions. This paper studies the economic impact of such ownership restriction on housing values. Using American Housing Survey data, we find that there is a significant premium attached to the restrictive covenant when other factors are controlled. In particular, we find that imposing age restriction on ownership increases the housing values by anywhere from 10.5% to 12.7%. At the average house value, this is equivalent to a dollar amount between $14,642 and$17,399. The estimates are robust to different specifications in hedonic equations.

Revealing signaling pathway deregulation by using gene expression signatures and regulatory motif analysis

A strategy for identifying cell signaling pathways whose deregulation result in an observed expression signature is presented

Multiclass discovery in array data

BACKGROUND: A routine goal in the analysis of microarray data is to identify genes with expression levels that correlate with known classes of experiments. In a growing number of array data sets, it has been shown that there is an over-abundance of genes that discriminate between known classes as compared to expectations for random classes. Therefore, one can search for novel classes in array data by looking for partitions of experiments for which there are an over-abundance of discriminatory genes. We have previously used such an approach in a breast cancer study. RESULTS: We describe the implementation of an unsupervised classification method for class discovery in microarray data. The method allows for discovery of more than two classes. We applied our method on two published microarray data sets: small round blue cell tumors and breast tumors. The method predicts relevant classes in the data sets with high success rates. CONCLUSIONS: We conclude that the proposed method is accurate and efficient in finding biologically relevant classes in microarray data. Additionally, the method is useful for quality control of microarray experiments. We have made the method available as a computer program

A Knowledge Mapping Analysis of Digital Photogrammetry Research Using CiteSpace

In order to clearly understand the current status and application trends of digital photogrammetry domestic and overseas research, taking the core journals of Web of Science as the data source, using bibliometric methods and CiteSpace to carry out statistical analysis of the relevant literature of digital photogrammetry research. The results show that since 2011, the research literature on digital photogrammetry has shown a steady growth year by year.  Digital photogrammetry is most closely related to the three disciplines of geology, earth science integration, and physical geography; countries such as the United States, the United Kingdom, Italy, and China publish the most papers, and these countries have strong research capabilities. Lane SN and Chandler JH have been shared with a high number of citations, who are representative scholars in this field; Digital photogrammetry contains multiple research directions. This article studies the research hotspots and frontiers of digital photogrammetry through keyword co-occurrence analysis and mutation detection analysis

BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning

An ever increasing number of configuration parameters are provided to system users. But many users have used one configuration setting across different workloads, leaving untapped the performance potential of systems. A good configuration setting can greatly improve the performance of a deployed system under certain workloads. But with tens or hundreds of parameters, it becomes a highly costly task to decide which configuration setting leads to the best performance. While such task requires the strong expertise in both the system and the application, users commonly lack such expertise. To help users tap the performance potential of systems, we present BestConfig, a system for automatically finding a best configuration setting within a resource limit for a deployed system under a given application workload. BestConfig is designed with an extensible architecture to automate the configuration tuning for general systems. To tune system configurations within a resource limit, we propose the divide-and-diverge sampling method and the recursive bound-and-search algorithm. BestConfig can improve the throughput of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce the running time of Hive join job by about 50% and that of Spark join job by about 80%, solely by configuration adjustment