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1.  Identification of cancer genomic markers via integrative sparse boosting 
Biostatistics (Oxford, England)  2012;13(3):509-522.
In high-throughput cancer genomic studies, markers identified from the analysis of single data sets often suffer a lack of reproducibility because of the small sample sizes. An ideal solution is to conduct large-scale prospective studies, which are extremely expensive and time consuming. A cost-effective remedy is to pool data from multiple comparable studies and conduct integrative analysis. Integrative analysis of multiple data sets is challenging because of the high dimensionality of genomic measurements and heterogeneity among studies. In this article, we propose a sparse boosting approach for marker identification in integrative analysis of multiple heterogeneous cancer diagnosis studies with gene expression measurements. The proposed approach can effectively accommodate the heterogeneity among multiple studies and identify markers with consistent effects across studies. Simulation shows that the proposed approach has satisfactory identification results and outperforms alternatives including an intensity approach and meta-analysis. The proposed approach is used to identify markers of pancreatic cancer and liver cancer.
doi:10.1093/biostatistics/kxr033
PMCID: PMC3577103  PMID: 22045909
Cancer genomics; Marker identification; Sparse boosting
2.  Health insurance coverage, medical expenditure and coping strategy: evidence from Taiwan 
Background
The health insurance system in Taiwan is comprised of public health insurance and private health insurance. The public health insurance, called “universal national health insurance” (NHI), was first established in 1995 and amended in 2011. The goal of this study is to provide an updated description of several important aspects of health insurance in Taiwan. Of special interest are household insurance coverage, medical expenditures (both gross and out-of-pocket), and coping strategies.
Methods
Data was collected via a phone call survey conducted in August and September of 2011. A household was the unit for survey and data analysis. A total of 2,424 households covering all major counties and cities in Taiwan were surveyed.
Results
The survey revealed that households with smaller sizes and higher incomes were more likely to have higher coverage of public and private health insurance. In addition, households with the presence of chronic diseases were more likely to have both types of insurance. Analysis of both gross and out-of-pocket medical expenditure was conducted. It was suggested that health insurance could not fully remove the financial burden caused by illness. The presence of chronic disease and inpatient treatment were significantly associated with higher gross and out-of-pocket medical expenditure. In addition, the presence of inpatient treatment was significantly associated with extremely high medical expenditure. Regional differences were also observed, with households in the northern, central, and southern regions having less gross medical expenditures than those on the offshore islands. Households with the presence of inpatient treatment were more likely to cope with medical expenditure using means other than salaries.
Conclusion
Despite the considerable achievements of the health insurance system in Taiwan, there is still room for improvement. This study investigated coverage, cost, and coping strategies and may be informative to stakeholders of both basic and commercial health insurance.
doi:10.1186/1472-6963-12-442
PMCID: PMC3519736  PMID: 23206690
Taiwan; Health insurance coverage; Medical expenditure; Coping strategy
3.  hnRNP Q Regulates Cdc42-Mediated Neuronal Morphogenesis 
Molecular and Cellular Biology  2012;32(12):2224-2238.
The RNA-binding protein hnRNP Q has been implicated in neuronal mRNA metabolism. Here, we show that knockdown of hnRNP Q increased neurite complexity in cultured rat cortical neurons and induced filopodium formation in mouse neuroblastoma cells. Reexpression of hnRNP Q1 in hnRNP Q-depleted cells abrogated the morphological changes of neurites, indicating a specific role for hnRNP Q1 in neuronal morphogenesis. A search for mRNA targets of hnRNP Q1 identified functionally coherent sets of mRNAs encoding factors involved in cellular signaling or cytoskeletal regulation and determined its preferred binding sequences. We demonstrated that hnRNP Q1 bound to a set of identified mRNAs encoding the components of the actin nucleation-promoting Cdc42/N-WASP/Arp2/3 complex and was in part colocalized with Cdc42 mRNA in granules. Using subcellular fractionation and immunofluorescence, we showed that knockdown of hnRNP Q reduced the level of some of those mRNAs in neurites and redistributed their encoded proteins from neurite tips to soma to different extents. Overexpression of dominant negative mutants of Cdc42 or N-WASP compromised hnRNP Q depletion-induced neurite complexity. Together, our results suggest that hnRNP Q1 may participate in localization of mRNAs encoding Cdc42 signaling factors in neurites, and thereby may regulate actin dynamics and control neuronal morphogenesis.
doi:10.1128/MCB.06550-11
PMCID: PMC3372263  PMID: 22493061
4.  Temporal Associations between Weather and Headache: Analysis by Empirical Mode Decomposition 
PLoS ONE  2011;6(1):e14612.
Background
Patients frequently report that weather changes trigger headache or worsen existing headache symptoms. Recently, the method of empirical mode decomposition (EMD) has been used to delineate temporal relationships in certain diseases, and we applied this technique to identify intrinsic weather components associated with headache incidence data derived from a large-scale epidemiological survey of headache in the Greater Taipei area.
Methodology/Principal Findings
The study sample consisted of 52 randomly selected headache patients. The weather time-series parameters were detrended by the EMD method into a set of embedded oscillatory components, i.e. intrinsic mode functions (IMFs). Multiple linear regression models with forward stepwise methods were used to analyze the temporal associations between weather and headaches. We found no associations between the raw time series of weather variables and headache incidence. For decomposed intrinsic weather IMFs, temperature, sunshine duration, humidity, pressure, and maximal wind speed were associated with headache incidence during the cold period, whereas only maximal wind speed was associated during the warm period. In analyses examining all significant weather variables, IMFs derived from temperature and sunshine duration data accounted for up to 33.3% of the variance in headache incidence during the cold period. The association of headache incidence and weather IMFs in the cold period coincided with the cold fronts.
Conclusions/Significance
Using EMD analysis, we found a significant association between headache and intrinsic weather components, which was not detected by direct comparisons of raw weather data. Contributing weather parameters may vary in different geographic regions and different seasons.
doi:10.1371/journal.pone.0014612
PMCID: PMC3031498  PMID: 21297940
5.  Incorporating gene co-expression network in identification of cancer prognosis markers 
BMC Bioinformatics  2010;11:271.
Background
Extensive biomedical studies have shown that clinical and environmental risk factors may not have sufficient predictive power for cancer prognosis. The development of high-throughput profiling technologies makes it possible to survey the whole genome and search for genomic markers with predictive power. Many existing studies assume the interchangeability of gene effects and ignore the coordination among them.
Results
We adopt the weighted co-expression network to describe the interplay among genes. Although there are several different ways of defining gene networks, the weighted co-expression network may be preferred because of its computational simplicity, satisfactory empirical performance, and because it does not demand additional biological experiments. For cancer prognosis studies with gene expression measurements, we propose a new marker selection method that can properly incorporate the network connectivity of genes. We analyze six prognosis studies on breast cancer and lymphoma. We find that the proposed approach can identify genes that are significantly different from those using alternatives. We search published literature and find that genes identified using the proposed approach are biologically meaningful. In addition, they have better prediction performance and reproducibility than genes identified using alternatives.
Conclusions
The network contains important information on the functionality of genes. Incorporating the network structure can improve cancer marker identification.
doi:10.1186/1471-2105-11-271
PMCID: PMC2881088  PMID: 20487548
6.  Semiparametric prognosis models in genomic studies 
Briefings in Bioinformatics  2010;11(4):385-393.
Development of high-throughput technologies makes it possible to survey the whole genome. Genomic studies have been extensively conducted, searching for markers with predictive power for prognosis of complex diseases such as cancer, diabetes and obesity. Most existing statistical analyses are focused on developing marker selection techniques, while little attention is paid to the underlying prognosis models. In this article, we review three commonly used prognosis models, namely the Cox, additive risk and accelerated failure time models. We conduct simulation and show that gene identification can be unsatisfactory under model misspecification. We analyze three cancer prognosis studies under the three models, and show that the gene identification results, prediction performance of all identified genes combined, and reproducibility of each identified gene are model-dependent. We suggest that in practical data analysis, more attention should be paid to the model assumption, and multiple models may need to be considered.
doi:10.1093/bib/bbp070
PMCID: PMC2905523  PMID: 20123942
genomic studies; semiparametric prognosis models; model comparison

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