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1.  Estimating the financial cost of chronic kidney disease to the NHS in England 
Nephrology Dialysis Transplantation  2012;27(Suppl 3):iii73-iii80.
Background
Chronic kidney disease (CKD) is a major challenge for health care systems around the world, and the prevalence rates appear to be increasing. We estimate the costs of CKD in a universal health care system.
Methods
Economic modelling was used to estimate the annual cost of Stages 3–5 CKD to the National Health Service (NHS) in England, including CKD-related prescribing and care, renal replacement therapy (RRT), and excess strokes, myocardial infarctions (MIs) and Methicillin-Resistant Staphylococcus Aureus (MRSA) infections in people with CKD.
Results
The cost of CKD to the English NHS in 2009–10 is estimated at £1.44 to £1.45 billion, which is ∼1.3% of all NHS spending in that year. More than half this sum was spent on RRT, which was provided for 2% of the CKD population. The economic model estimates that ∼7000 excess strokes and 12 000 excess MIs occurred in the CKD population in 2009–10, relative to an age- and gender-matched population without CKD. The cost of excess strokes and MIs is estimated at £174–£178 million.
Conclusions
The financial impact of CKD is large, with particularly high costs relating to RRT and cardiovascular complications. It is hoped that these detailed cost estimates will be useful in analysing the cost-effectiveness of treatments for CKD.
doi:10.1093/ndt/gfs269
PMCID: PMC3484716  PMID: 22815543
chronic kidney disease; English NHS; expenditure; health economics
2.  Automatic categorization of diverse experimental information in the bioscience literature 
BMC Bioinformatics  2012;13:16.
Background
Curation of information from bioscience literature into biological knowledge databases is a crucial way of capturing experimental information in a computable form. During the biocuration process, a critical first step is to identify from all published literature the papers that contain results for a specific data type the curator is interested in annotating. This step normally requires curators to manually examine many papers to ascertain which few contain information of interest and thus, is usually time consuming. We developed an automatic method for identifying papers containing these curation data types among a large pool of published scientific papers based on the machine learning method Support Vector Machine (SVM). This classification system is completely automatic and can be readily applied to diverse experimental data types. It has been in use in production for automatic categorization of 10 different experimental datatypes in the biocuration process at WormBase for the past two years and it is in the process of being adopted in the biocuration process at FlyBase and the Saccharomyces Genome Database (SGD). We anticipate that this method can be readily adopted by various databases in the biocuration community and thereby greatly reducing time spent on an otherwise laborious and demanding task. We also developed a simple, readily automated procedure to utilize training papers of similar data types from different bodies of literature such as C. elegans and D. melanogaster to identify papers with any of these data types for a single database. This approach has great significance because for some data types, especially those of low occurrence, a single corpus often does not have enough training papers to achieve satisfactory performance.
Results
We successfully tested the method on ten data types from WormBase, fifteen data types from FlyBase and three data types from Mouse Genomics Informatics (MGI). It is being used in the curation work flow at WormBase for automatic association of newly published papers with ten data types including RNAi, antibody, phenotype, gene regulation, mutant allele sequence, gene expression, gene product interaction, overexpression phenotype, gene interaction, and gene structure correction.
Conclusions
Our methods are applicable to a variety of data types with training set containing several hundreds to a few thousand documents. It is completely automatic and, thus can be readily incorporated to different workflow at different literature-based databases. We believe that the work presented here can contribute greatly to the tremendous task of automating the important yet labor-intensive biocuration effort.
doi:10.1186/1471-2105-13-16
PMCID: PMC3305665  PMID: 22280404
3.  Annotation of the Drosophila melanogaster euchromatic genome: a systematic review 
Genome Biology  2002;3(12):research0083.1-83.22.
The recent completion of the Drosophila melanogaster genomic sequence to high quality, and the availability of a greatly expanded set of Drosophila cDNA sequences, afforded FlyBase the opportunity to significantly improve genomic annotations.
Background
The recent completion of the Drosophila melanogaster genomic sequence to high quality and the availability of a greatly expanded set of Drosophila cDNA sequences, aligning to 78% of the predicted euchromatic genes, afforded FlyBase the opportunity to significantly improve genomic annotations. We made the annotation process more rigorous by inspecting each gene visually, utilizing a comprehensive set of curation rules, requiring traceable evidence for each gene model, and comparing each predicted peptide to SWISS-PROT and TrEMBL sequences.
Results
Although the number of predicted protein-coding genes in Drosophila remains essentially unchanged, the revised annotation significantly improves gene models, resulting in structural changes to 85% of the transcripts and 45% of the predicted proteins. We annotated transposable elements and non-protein-coding RNAs as new features, and extended the annotation of untranslated (UTR) sequences and alternative transcripts to include more than 70% and 20% of genes, respectively. Finally, cDNA sequence provided evidence for dicistronic transcripts, neighboring genes with overlapping UTRs on the same DNA sequence strand, alternatively spliced genes that encode distinct, non-overlapping peptides, and numerous nested genes.
Conclusions
Identification of so many unusual gene models not only suggests that some mechanisms for gene regulation are more prevalent than previously believed, but also underscores the complex challenges of eukaryotic gene prediction. At present, experimental data and human curation remain essential to generate high-quality genome annotations.
doi:10.1186/gb-2002-3-12-research0083
PMCID: PMC151185  PMID: 12537572

Results 1-3 (3)