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11.
Smit S Hoefsloot HC Smilde AK 《Journal of chromatography. B, Analytical technologies in the biomedical and life sciences》2008,866(1-2):77-88
This review discusses data analysis strategies for the discovery of biomarkers in clinical proteomics. Proteomics studies produce large amounts of data, characterized by few samples of which many variables are measured. A wealth of classification methods exists for extracting information from the data. Feature selection plays an important role in reducing the dimensionality of the data prior to classification and in discovering biomarker leads. The question which classification strategy works best is yet unanswered. Validation is a crucial step for biomarker leads towards clinical use. Here we only discuss statistical validation, recognizing that biological and clinical validation is of utmost importance. First, there is the need for validated model selection to develop a generalized classifier that predicts new samples correctly. A cross-validation loop that is wrapped around the model development procedure assesses the performance using unseen data. The significance of the model should be tested; we use permutations of the data for comparison with uninformative data. This procedure also tests the correctness of the performance validation. Preferably, a new set of samples is measured to test the classifier and rule out results specific for a machine, analyst, laboratory or the first set of samples. This is not yet standard practice. We present a modular framework that combines feature selection, classification, biomarker discovery and statistical validation; these data analysis aspects are all discussed in this review. The feature selection, classification and biomarker discovery modules can be incorporated or omitted to the preference of the researcher. The validation modules, however, should not be optional. In each module, the researcher can select from a wide range of methods, since there is not one unique way that leads to the correct model and proper validation. We discuss many possibilities for feature selection, classification and biomarker discovery. For validation we advice a combination of cross-validation and permutation testing, a validation strategy supported in the literature. 相似文献
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13.
Shi Yu Tillmann Falck Anneleen Daemen Leon-Charles Tranchevent Johan AK Suykens Bart De Moor Yves Moreau 《BMC bioinformatics》2010,11(1):309
Background
This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields different extensions of multiple kernel learning (MKL) such as L ∞, L 1, and L 2 MKL. In particular, L 2 MKL is a novel method that leads to non-sparse optimal kernel coefficients, which is different from the sparse kernel coefficients optimized by the existing L ∞ MKL method. In real biomedical applications, L 2 MKL may have more advantages over sparse integration method for thoroughly combining complementary information in heterogeneous data sources. 相似文献14.
The Indian black berry (Syzygium cumini Skeels) has a great nutraceutical and medicinal properties. As in other fruit crops, the
fruit characteristics are important attributes for differentiation were also determined for different accessions of S. cumini. The fruit
weight, length, breadth, length: breadth ratio, pulp weight, pulp content, seed weight and pulp: seed ratio significantly varied in
different accessions. Molecular characterization was carried out using PCR based RAPD technique. Out of 80 RAPD primers, only
18 primers produced stable polymorphisms that were used to examine the phylogenetic relationship. A sum of 207 loci were
generated out of which 201 loci found polymorphic. The average genetic dissimilarity was 97 per cent among jamun accessions.
The phylogenetic relationship was also determined by principal coordinates analysis (PCoA) that explained 46.95 per cent
cumulative variance. The two-dimensional PCoA analysis showed grouping of the different accessions that were plotted into four
sub-plots, representing clustering of accessions. The UPGMA (r = 0.967) and NJ (r = 0.987) dendrogram constructed based on the
dissimilarity matrix revealed a good degree of fit with the cophenetic correlation value. The dendrogram grouped the accessions
into three main clusters according to their eco-geographical regions which given useful insight into their phylogenetic
relationships. 相似文献
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17.
K Michałek M Laszczyńska AK Ciechanowicz A Herosimczyk I Rotter M Oganowska 《Biotechnic & histochemistry》2014,89(5):342-347
Aquaporin 2 (AQP2) is a small, integral tetrameric plasma membrane protein that is expressed in mammalian kidneys. The specific constitution of this protein and its selective permeability to water means that AQP2 plays an important role in hypertonic urine production. Immunolocalization of AQP2 has been studied in humans, monkeys, sheep, dogs, rabbits, rats, mice and adult cattle. We analyzed the expression of AQP2 in kidneys of 7-month-old Polish-Friesian var. black and white male calves. AQP2 was localized in the principal cells of collecting ducts in medullary rays penetrating the renal cortex and in the collecting ducts of renal medulla. AQP2 was expressed most strongly in the apical plasma membrane, but expression was observed also in the intracellular vesicles and basolateral plasma membrane. Our study provides new information concerning the immunolocalization of AQP2 in calf kidneys. 相似文献
18.
The sequences of the 5' long-terminal repeat (LTR) and adjacent leader
regions of 27 full-length copia elements isolated from natural populations
of Drosophila melanogaster, D. simulans, and D. mauritiana are presented.
Phylogenetic analyses indicate that although D. melanogaster copia elements
are distinct from those of D. simulans and D. mauritiana, the elements of
these latter two species are not distinguishable from one another. LTRs and
adjacent 5' leader regions of elements isolated from D. simulans and D.
mauritiana are structurally similar to one another and carry substantial
deletional variation mapping to regions previously identified as being of
potential importance for copia expression.
相似文献
19.
Alžběta Gardlo Age K. Smilde Karel Hron Marcela Hrdá Radana Karlíková David Friedecký Tomáš Adam 《Metabolomics : Official journal of the Metabolomic Society》2016,12(7):117
Introduction
One of the body fluids often used in metabolomics studies is urine. The concentrations of metabolites in urine are affected by hydration status of an individual, resulting in dilution differences. This requires therefore normalization of the data to correct for such differences. Two normalization techniques are commonly applied to urine samples prior to their further statistical analysis. First, AUC normalization aims to normalize a group of signals with peaks by standardizing the area under the curve (AUC) within a sample to the median, mean or any other proper representation of the amount of dilution. The second approach uses specific end-product metabolites such as creatinine and all intensities within a sample are expressed relative to the creatinine intensity.Objectives
Another way of looking at urine metabolomics data is by realizing that the ratios between peak intensities are the information-carrying features. This opens up possibilities to use another class of data analysis techniques designed to deal with such ratios: compositional data analysis. The aim of this paper is to develop PARAFAC modeling of three-way urine metabolomics data in the context of compositional data analysis and compare this with standard normalization techniques.Methods
In the compositional data analysis approach, special coordinate systems are defined to deal with the ratio problem. In essence, it comes down to using other distance measures than the Euclidian Distance that is used in the conventional analysis of metabolomic data.Results
We illustrate using this type of approach in combination with three-way methods (i.e. PARAFAC) of a longitudinal urine metabolomics study and two simulations. In both cases, the advantage of the compositional approach is established in terms of improved interpretability of the scores and loadings of the PARAFAC model.Conclusion
For urine metabolomics studies, we advocate the use of compositional data analysis approaches. They are easy to use, well established and proof to give reliable results.20.