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Search Results: 1 - 10 of 4455 matches for " Gill Lawrence "
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Investigation of factors affecting prediction of protein-protein interaction networks by phylogenetic profiling
Anis Karimpour-Fard, Lawrence Hunter, Ryan T Gill
BMC Genomics , 2007, DOI: 10.1186/1471-2164-8-393
Abstract: Here, we examined how various aspects of this method affect the accuracy and topology of protein interaction networks. We have shown that the choice of reference genome influences the number of predictions involving proteins of previously unknown function, the accuracy of predicted interactions, and the topology of predicted interaction networks. We show that while such results are relatively insensitive to the E-value threshold used in defining homologs, predicted interactions are influenced by the similarity metric that is employed. We show that differences in predicted protein interactions are biologically meaningful, where judicious selection of reference genomes, or use of a new scoring scheme that explicitly considers reference genome relatedness, produces known protein interactions as well as predicted protein interactions involving coordinated biological processes that are not accessible using currently available databases.These studies should prove valuable for future studies seeking to further improve phylogenetic profiling methodologies as well for efforts to efficiently employ such methods to develop new biological insights.Genome sequencing projects are rapidly increasing the raw data available for predicting protein function and protein interaction networks. The best established method for function prediction is based on sequence homology to proteins of known function. Unfortunately, strictly homology-based predictions are of limited use due to the large number of homologous protein families with no known function for any single member [1-3]. An alternative method for predicting protein function is the Phylogenetic profile method, also known as the Co-Conservation method, which rests on the premise that functionally related proteins are gained or lost together over the course of evolution [4]. This method predicts functional interactions between pairs of proteins in a target organism by determining whether both proteins are consistently present or abse
Use of name recognition software, census data and multiple imputation to predict missing data on ethnicity: application to cancer registry records
Ronan Ryan, Sally Vernon, Gill Lawrence, Sue Wilson
BMC Medical Informatics and Decision Making , 2012, DOI: 10.1186/1472-6947-12-3
Abstract: Routine records from cancer screening services, name recognition software (Nam Pehchan and Onomap), 2001 national Census data, and multiple imputation were used to predict the ethnicity of the 23% of cases that were still missing following linkage with self-reported ethnicity from inpatient hospital records.The name recognition software were good predictors of ethnicity for South Asian cancer cases when compared with data on ethnicity derived from hospital inpatient records, especially when combined (sensitivity 90.5%; specificity 99.9%; PPV 93.3%). Onomap was a poor predictor of ethnicity for other minority ethnic groups (sensitivity 4.4% for Black cases and 0.0% for Chinese/Other ethnic groups). Area-based data derived from the national Census was also a poor predictor non-White ethnicity (sensitivity: South Asian 7.4%; Black 2.3%; Chinese/Other 0.0%; Mixed 0.0%).Currently, neither method for assigning individuals to an ethnic group (name recognition and ethnic distribution of area of residence) performs well across all ethnic groups. We recommend further development of name recognition applications and the identification of additional methods for predicting ethnicity to improve their precision and accuracy for comparisons of health outcomes. However, real improvements can only come from better recording of ethnicity by health services.This paper presents a method for imputing missing data on the ethnicity of cancer patients, developed for a regional cancer registry in the UK. It implements existing approaches in a novel situation and evaluates their utility. It combines four differing approaches to dealing with missing data of this type: the use of an additional source of self-reported ethnicity to replace the missing data; the use of name recognition software to predict the ethnicity of individuals; the use of Census data based on area of residence to predict the ethnicity of individuals; and finally, the use of multiple imputation (MI) to make an allowance for
A Research on Eurozone Bond Market and Determinants of Sovereign Bond Yields  [PDF]
Navjeet Gill
Journal of Financial Risk Management (JFRM) , 2018, DOI: 10.4236/jfrm.2018.72012
Abstract: This empirical research uses an OLS regression framework to examine the effect of the overall debt crisis on European sovereign bonds by conducting an overview of the bond market. It identifies the determinants which affect the generation of the indebtedness of sovereign bonds and play a major role in the determination of their solvency and hence, the spreads. These results reveal that Interest Rate, Inflation, Debt to GDP, Deficit to GDP, Gross Domestic Product rate of growth, and VSTOXX index are the most significant determinants of the sovereign bond spreads in the 6 sample countries, i.e. France, Germany, United Kingdom, Greece, Italy and Spain. To summarize, the main factors which affected bond spreads before the crisis, were not the country-specific fundamentals but rather the convergence of bond yields in the euro-zone countries due to and following the launch of the monetary union but during the crisis, increased risk aversion and lack of lender of last resort, shifted the focus to country specific factors and the bond spreads began to diverge according to the determinants highlighted in this study.
Cross-species cluster co-conservation: a new method for generating protein interaction networks
Anis Karimpour-Fard, Corrella S Detweiler, Kimberly D Erickson, Lawrence Hunter, Ryan T Gill
Genome Biology , 2007, DOI: 10.1186/gb-2007-8-9-r185
Abstract: The exponential increase in sequence information has widened the gap between the number of predicted and experimentally characterized proteins. At present, about 400 microbial genomes are fully sequenced. The prediction of protein function from sequence is a critical issue in genome annotation efforts. Currently, the best established method for function prediction is based on sequence similarity to proteins of known function. Unfortunately, homoogy-based prediction is of limited use due to the large number of homologous protein families with no known function for any member. An alternative method for predicting protein function is the phylogenetic profiles approach, also known as the co-conservation (CC) method first introduced by Pellegrini et al. [1]. Co-conservation predicts interactions between pairs of proteins by determining whether both proteins are consistently present or absent across diverse genomes [2-8]. CC methods have been shown to be more powerful than sequence similarity alone at predicting protein function.Even though all CC methods rely on the premise that functionally related proteins are gained or lost together over the course of evolution, several different strategies for performing CC studies have been reported. For example, Date et al. [7] used real BLASTP best hit E-values normalized across 11 bins instead of binary classification for conservation, while Zheng and coworkers [9] constructed phylogenetic profiles using presence/absence of neighboring gene pairs. Alternatively, Pagel et al. [10] constructed phylogenetic profiles between domains, instead of genes, and then created domain interaction maps. Barker et al. [11] applied maximum likelihood statistical modeling for predicting functional gene linkages based on phylogenetic profiling. Their method detected independent instances of protein pair correlated gain or loss on phylogenetic trees, reducing the high rates of false positives observed in conventional across-species methods that do n
Predicting protein linkages in bacteria: Which method is best depends on task
Anis Karimpour-Fard, Sonia M Leach, Ryan T Gill, Lawrence E Hunter
BMC Bioinformatics , 2008, DOI: 10.1186/1471-2105-9-397
Abstract: Using Escherichia coli K12 and Bacillus subtilis, linkage predictions made by each of these methods were evaluated against three benchmarks: functional categories defined by COG and KEGG, known pathways listed in EcoCyc, and known operons listed in RegulonDB. Each evaluated method had strengths and weaknesses, with no one method dominating all aspects of predictive ability studied. For functional categories, as previous studies have shown, the Rosetta Stone method was individually best at detecting linkages and predicting functions among proteins with shared KEGG categories while the Phylogenetic profile method was best for linkage detection and function prediction among proteins with common COG functions. Differences in performance under COG versus KEGG may be attributable to the presence of paralogs. Better function prediction was observed when using a weighted combination of linkages based on reliability versus using a simple unweighted union of the linkage sets. For pathway reconstruction, 99 complete metabolic pathways in E. coli K12 (out of the 209 known, non-trivial pathways) and 193 pathways with 50% of their proteins were covered by linkages from at least one method. Gene neighbor was most effective individually on pathway reconstruction, with 48 complete pathways reconstructed. For operon prediction, Gene cluster predicted completely 59% of the known operons in E. coli K12 and 88% (333/418)in B. subtilis. Comparing two versions of the E. coli K12 operon database, many of the unannotated predictions in the earlier version were updated to true predictions in the later version. Using only linkages found by both Gene Cluster and Gene Neighbor improved the precision of operon predictions. Additionally, as previous studies have shown, combining features based on intergenic region and protein function improved the specificity of operon prediction.A common problem for computational methods is the generation of a large number of false positives that might be caused
The topology of the bacterial co-conserved protein network and its implications for predicting protein function
Anis Karimpour-Fard, Sonia M Leach, Lawrence E Hunter, Ryan T Gill
BMC Genomics , 2008, DOI: 10.1186/1471-2164-9-313
Abstract: Our results showed, like most biological networks, our bacteria co-conserved protein-protein interaction networks had scale-free topologies. Our results indicated that some properties of the physical yeast interaction network hold in our bacteria co-conservation networks, such as high connectivity for essential proteins. However, the high connectivity among protein complexes in the yeast physical network was not seen in the co-conservation network which uses all bacteria as the reference set. We found that the distribution of node connectivity varied by functional category and could be informative for function prediction. By integrating of functional information from different annotation sources and using the network topology, we were able to infer function for uncharacterized proteins.Interactions networks based on co-conservation can contain information distinct from networks based on physical or other interaction types. Our study has shown co-conservation based networks to exhibit a scale free topology, as expected for biological networks. We also revealed ways that connectivity in our networks can be informative for the functional characterization of proteins.Co-conservation, a measure of the degree to which proteins are gained and lost together through evolution (also known as a phylogenetic profile [1]), has demonstrated utility as a protein function prediction method [2-13], particularly in bacteria. Pairwise co-conservation scores can be aggregated into networks [7], and assessments of connectivity within the resulting graph can further improve the quality of function prediction. Function prediction methods based on biological networks is an active area of research [14].Topological analysis of other types of biological networks, including protein-protein interactions, regulatory interactions, and metabolic networks, has demonstrated that structural features of network subgraphs can provide quantitative insight into biological function [15-33]. For example, M
Changes in and predictors of length of stay in hospital after surgery for breast cancer between 1997/98 and 2004/05 in two regions of England: a population-based study
Amy Downing, Mark Lansdown, Robert M West, James D Thomas, Gill Lawrence, David Forman
BMC Health Services Research , 2009, DOI: 10.1186/1472-6963-9-202
Abstract: Cases of female invasive breast cancer diagnosed in two English cancer registry regions were linked to Hospital Episode Statistics data for the period 1st April 1997 to 31st March 2005. A subset of records where women underwent mastectomy or breast conserving surgery (BCS) was extracted (n = 44,877). Variations in LOS over the study period were investigated. A multilevel model with patients clustered within surgical teams and NHS Trusts was used to examine associations between LOS and a range of factors.Over the study period the proportion of women having a mastectomy reduced from 58% to 52%. The proportion varied from 14% to 80% according to NHS Trust. LOS decreased by 21% from 1997/98 to 2004/05 (LOSratio = 0.79, 95%CI 0.77-0.80). BCS was associated with 33% shorter hospital stays compared to mastectomy (LOSratio = 0.67, 95%CI 0.66-0.68). Older age, advanced disease, presence of comorbidities, lymph node excision and reconstructive surgery were associated with increased LOS. Significant variation remained amongst Trusts and surgical teams.The number of days spent in hospital after breast cancer surgery has continued to decline for several decades. The change from mastectomy to BCS accounts for only 9% of the overall decrease in LOS. Other explanations include the adoption of new techniques and practices, such as sentinel lymph node biopsy and early discharge. This study has identified wide variation in practice with substantial cost implications for the NHS. Further work is required to explain this variation.The average time spent in hospital after treatment has been decreasing for many years. Data show that in the UK the average length of stay (LOS) for patients receiving acute care decreased from 19.8 days in 1953 to 8.8 days in 1982[1]. More recent data show that the decrease has continued and by 2005 the average LOS was 6.1 days[2]. Similar reductions have been seen in most developed countries.LOS can vary according to a wide range of factors, including patien
Correction: PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer
Gordon C Wishart, Elizabeth M Azzato, David C Greenberg, Jem Rashbass, Olive Kearins, Gill Lawrence, Carlos Caldas, Paul DP Pharoah
Breast Cancer Research , 2010, DOI: 10.1186/bcr2480
PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer
Gordon C Wishart, Elizabeth M Azzato, David C Greenberg, Jem Rashbass, Olive Kearins, Gill Lawrence, Carlos Caldas, Paul DP Pharoah
Breast Cancer Research , 2010, DOI: 10.1186/bcr2464
Abstract: Using the Eastern Cancer Registration and Information Centre (ECRIC) dataset, information was collated for 5,694 women who had surgery for invasive breast cancer in East Anglia from 1999 to 2003. Breast cancer mortality models for oestrogen receptor (ER) positive and ER negative tumours were derived from these data using Cox proportional hazards, adjusting for prognostic factors and mode of cancer detection (symptomatic versus screen-detected). An external dataset of 5,468 patients from the West Midlands Cancer Intelligence Unit (WMCIU) was used for validation.Differences in overall actual and predicted mortality were <1% at eight years for ECRIC (18.9% vs. 19.0%) and WMCIU (17.5% vs. 18.3%) with area under receiver-operator-characteristic curves (AUC) of 0.81 and 0.79 respectively. Differences in breast cancer specific actual and predicted mortality were <1% at eight years for ECRIC (12.9% vs. 13.5%) and <1.5% at eight years for WMCIU (12.2% vs. 13.6%) with AUC of 0.84 and 0.82 respectively. Model calibration was good for both ER positive and negative models although the ER positive model provided better discrimination (AUC 0.82) than ER negative (AUC 0.75).We have developed a prognostication model for early breast cancer based on UK cancer registry data that predicts breast cancer survival following surgery for invasive breast cancer and includes mode of detection for the first time. The model is well calibrated, provides a high degree of discrimination and has been validated in a second UK patient cohort.Accurate prediction of survival is an essential part of the decision making process following surgery for early breast cancer and allows clinicians to determine which patients will benefit from adjuvant therapy. At present these decisions are largely based on known pathological prognostic factors that retain independent significance on multivariate analysis including tumour size, tumour grade and lymph node status in addition to the efficacy of any adjuvant thera
Exploring the Factors that Affect the Choice of Destination for Medical Tourism  [PDF]
Neha Singh, Harsimran Gill
Journal of Service Science and Management (JSSM) , 2011, DOI: 10.4236/jssm.2011.43037
Abstract: Medical Tourism has become one of the latest trends in the tourism industry which has been and has the potential to continue growing exponentially every year. More travelers than ever before are now travelling abroad to get high quality medical treatments for less cost. The purpose of my study is to explore the interest in US travelers in medical tourism. Results from the survey indicated that “competent doctors”, “high quality medical treatment facility”, and “prompt medical treatment when needed” where the top three factors before deciding whether or not to take a trip abroad. The results will be useful to businesses that are either directly or indirectly involved with this industry, such as insurance companies, credit card companies, travel agencies, hotels, food and beverage companies, medical facilities and services, and spas.
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