Approximation properties of haplotype tagging

BACKGROUND: Single nucleotide polymorphisms (SNPs) are locations at which the genomic sequences of population members differ. Since these differences are known to follow patterns, disease association studies are facilitated by identifying SNPs that allow the unique identification of such patterns. This process, known as haplotype tagging, is formulated as a combinatorial optimization problem and analyzed in terms of complexity and approximation properties. RESULTS: It is shown that the tagging problem is NP-hard but approximable within 1 + ln((n(2 )- n)/2) for n haplotypes but not approximable within (1 - ε) ln(n/2) for any ε > 0 unless NP ⊂ DTIME(n(log log n)). A simple, very easily implementable algorithm that exhibits the above upper bound on solution quality is presented. This algorithm has running time O([Image: see text] (2m - p + 1)) ≤ O(m(n(2 )- n)/2) where p ≤ min(n, m) for n haplotypes of size m. As we show that the approximation bound is asymptotically tight, the algorithm presented is optimal with respect to this asymptotic bound. CONCLUSION: The haplotype tagging problem is hard, but approachable with a fast, practical, and surprisingly simple algorithm that cannot be significantly improved upon on a single processor machine. Hence, significant improvement in computatational efforts expended can only be expected if the computational effort is distributed and done in parallel

Applying a decision support system in clinical practice: Results from melanoma diagnosis

Abstract The work reported in this paper investigates the use of a decision-support tool for the diagnosis of pigmented skin lesions in a real-world clinical trial with 511 patients and 3827 lesion evaluations. We analyzed a number of outcomes of the trial, such as direct comparison of system performance in laboratory and clinical setting, the performance of physicians using the system compared to a control dermatologist without the system, and repeatability of system recommendations. The results show that system performance was significantly less in the real-world setting compared to the laboratory setting (c-index of 0.87 vs. 0.94, p = 0.01). Dermatologists using the system achieved a combined sensitivity of 85% and combined specificity of 95%. We also show that the process of acquiring lesion images using digital dermoscopy devices needs to be standardized before sufficiently high repeatability of measurements can be assured

Hip fracture risk assessment: Artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study

Copyright @ 2013 Tseng et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background - Osteoporotic hip fractures with a significant morbidity and excess mortality among the elderly have imposed huge health and economic burdens on societies worldwide. In this age- and sex-matched case control study, we examined the risk factors of hip fractures and assessed the fracture risk by conditional logistic regression (CLR) and ensemble artificial neural network (ANN). The performances of these two classifiers were compared. Methods - The study population consisted of 217 pairs (149 women and 68 men) of fractures and controls with an age older than 60 years. All the participants were interviewed with the same standardized questionnaire including questions on 66 risk factors in 12 categories. Univariate CLR analysis was initially conducted to examine the unadjusted odds ratio of all potential risk factors. The significant risk factors were then tested by multivariate analyses. For fracture risk assessment, the participants were randomly divided into modeling and testing datasets for 10-fold cross validation analyses. The predicting models built by CLR and ANN in modeling datasets were applied to testing datasets for generalization study. The performances, including discrimination and calibration, were compared with non-parametric Wilcoxon tests. Results - In univariate CLR analyses, 16 variables achieved significant level, and six of them remained significant in multivariate analyses, including low T score, low BMI, low MMSE score, milk intake, walking difficulty, and significant fall at home. For discrimination, ANN outperformed CLR in both 16- and 6-variable analyses in modeling and testing datasets (p?<?0.005). For calibration, ANN outperformed CLR only in 16-variable analyses in modeling and testing datasets (p?=?0.013 and 0.047, respectively). Conclusions - The risk factors of hip fracture are more personal than environmental. With adequate model construction, ANN may outperform CLR in both discrimination and calibration. ANN seems to have not been developed to its full potential and efforts should be made to improve its performance.National Health Research Institutes in Taiwa

The Red Queen and the seed bank: pathogen resistance of ex situ and in situ conserved barley

Plant geneticists have proposed that the dynamic conservation of crop plants in farm environments (in situ conservation) is complementary to static conservation in seed banks (ex situ conservation) because it may help to ensure adaptation to changing conditions. Here, we test whether collections of a traditional variety of Moroccan barley (Hordeum vulgare ssp. vulgare) conserved ex situ showed differences in qualitative and quantitative resistance to the endemic fungal pathogen, Blumeria graminis f.sp. hordei, compared to collections that were continuously cultivated in situ. In detached-leaf assays for qualitative resistance, there were some significant differences between in situ and ex situ conserved collections from the same localities. Some ex situ conserved collections showed lower resistance levels, while others showed higher resistance levels than their in situ conserved counterparts. In field trials for quantitative resistance, similar results were observed, with the highest resistance observed in situ. Overall, this study identifies some cases where the Red Queen appears to drive the evolution of increased resistance in situ. However, in situ conservation does not always result in improved adaptation to pathogen virulence, suggesting a more complex evolutionary scenario, consistent with several published examples of plant–pathogen co-evolution in wild systems

Pathotype variation of barley powdery mildew in Western Australia

Barley powdery mildew caused by the fungus Blumeria graminis f. sp. hordei (Bgh) has emerged as the most damaging disease of barley in Western Australia (WA). Many of the available cultivars display high levels of disease in the field when climatic conditions are conducive. As a result, fungicides have become the main method of disease control in the last 10 years. Different types and sources of genetic disease resistance are available but to optimise their deployment it is necessary to evaluate the spectrum of pathotypes present in the pathogen population. Sixty isolates of Bgh were collected in the 2009 season from 9 locations, single spored and characterised by infection on reference barley lines and cultivars. Eighteen unique pathotypes were resolved. Virulence against many of the R-genes in the reference lines was present in at least one pathotype. Isolates were virulent against 16 out of a total of 23 resistance gene combinations. Undefeated resistance genes included the major R-genes Mla-6, Mla-9, Ml-ra and the combinations of Mla-1 plus Mla-A12 and Mla-6 plus Mla-14 and Mla-13 plus Ml-Ru3 together with the recessive resistance gene mlo-5. There was significant pathotype spatial differentiation suggesting limited gene flow between different regions with WA or localised selection pressures and proliferation. On the basis of the results we recommend a number of strategies to manage powdery mildew disease levels within WA

Characterization of digital medical images utilizing support vector machines

BACKGROUND: In this paper we discuss an efficient methodology for the image analysis and characterization of digital images containing skin lesions using Support Vector Machines and present the results of a preliminary study. METHODS: The methodology is based on the support vector machines algorithm for data classification and it has been applied to the problem of the recognition of malignant melanoma versus dysplastic naevus. Border and colour based features were extracted from digital images of skin lesions acquired under reproducible conditions, using basic image processing techniques. Two alternative classification methods, the statistical discriminant analysis and the application of neural networks were also applied to the same problem and the results are compared. RESULTS: The SVM (Support Vector Machines) algorithm performed quite well achieving 94.1% correct classification, which is better than the performance of the other two classification methodologies. The method of discriminant analysis classified correctly 88% of cases (71% of Malignant Melanoma and 100% of Dysplastic Naevi), while the neural networks performed approximately the same. CONCLUSION: The use of a computer-based system, like the one described in this paper, is intended to avoid human subjectivity and to perform specific tasks according to a number of criteria. However the presence of an expert dermatologist is considered necessary for the overall visual assessment of the skin lesion and the final diagnosis