CiteULike: jyuh Hastie

Penalized logistic regression for detecting gene interactions

Mee Park — 2007-11-15T12:57:35-00:00

Biostat (11 April 2007), kxm010.

We propose using a variant of logistic regression (LR) with [IMG]f1.gif" ALT="Formula" BORDER="0">-regularization to fit gene-gene and gene-environment interaction models. Studies have shown that many common diseases are influenced by interaction of certain genes. LR models with quadratic penalization not only correctly characterizes the influential genes along with their interaction structures but also yields additional benefits in handling high-dimensional, discrete factors with a binary response. We illustrate the advantages of using an [IMG]f1.gif" ALT="Formula" BORDER="0">-regularization scheme and compare its performance with that of "multifactor dimensionality reduction" and "FlexTree," 2 recent tools for identifying gene-gene interactions. Through simulated and real data sets, we demonstrate that our method outperforms other methods in the identification of the interaction structures as well as prediction accuracy. In addition, we validate the significance of the factors selected through bootstrap analyses. 10.1093/biostatistics/kxm010

A working guide to boosted regression trees.

J Elith — 2008-04-15T04:35:58-00:00

The Journal of animal ecology (7 April 2008)

1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion. 3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods. 4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel (Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.

SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout.

Veronique Vitart — 2008-03-23T14:08:32-00:00

Nat Genet (9 March 2008)

Uric acid is the end product of purine metabolism in humans and great apes, which have lost hepatic uricase activity, leading to uniquely high serum uric acid concentrations (200-500 muM) compared with other mammals (3-120 muM). About 70% of daily urate disposal occurs via the kidneys, and in 5-25% of the human population, impaired renal excretion leads to hyperuricemia. About 10% of people with hyperuricemia develop gout, an inflammatory arthritis that results from deposition of monosodium urate crystals in the joint. We have identified genetic variants within a transporter gene, SLC2A9, that explain 1.7-5.3% of the variance in serum uric acid concentrations, following a genome-wide association scan in a Croatian population sample. SLC2A9 variants were also associated with low fractional excretion of uric acid and/or gout in UK, Croatian and German population samples. SLC2A9 is a known fructose transporter, and we now show that it has strong uric acid transport activity in Xenopus laevis oocytes.

The selectivity of protein kinase inhibitors: a further update.

J Bain — 2007-12-20T14:37:59-00:00

Biochem J, Vol. 408, No. 3. (15 December 2007), pp. 297-315.

The specificities of 65 compounds reported to be relatively specific inhibitors of protein kinases have been profiled against a panel of 70-80 protein kinases. On the basis of this information, the effects of compounds that we have studied in cells and other data in the literature, we recommend the use of the following small-molecule inhibitors: SB 203580/SB202190 and BIRB 0796 to be used in parallel to assess the physiological roles of p38 MAPK (mitogen-activated protein kinase) isoforms, PI-103 and wortmannin to be used in parallel to inhibit phosphatidylinositol (phosphoinositide) 3-kinases, PP1 or PP2 to be used in parallel with Src-I1 (Src inhibitor-1) to inhibit Src family members; PD 184352 or PD 0325901 to inhibit MKK1 (MAPK kinase-1) or MKK1 plus MKK5, Akt-I-1/2 to inhibit the activation of PKB (protein kinase B/Akt), rapamycin to inhibit TORC1 [mTOR (mammalian target of rapamycin)-raptor (regulatory associated protein of mTOR) complex], CT 99021 to inhibit GSK3 (glycogen synthase kinase 3), BI-D1870 and SL0101 or FMK (fluoromethylketone) to be used in parallel to inhibit RSK (ribosomal S6 kinase), D4476 to inhibit CK1 (casein kinase 1), VX680 to inhibit Aurora kinases, and roscovitine as a pan-CDK (cyclin-dependent kinase) inhibitor. We have also identified harmine as a potent and specific inhibitor of DYRK1A (dual-specificity tyrosine-phosphorylated and -regulated kinase 1A) in vitro. The results have further emphasized the need for considerable caution in using small-molecule inhibitors of protein kinases to assess the physiological roles of these enzymes. Despite being used widely, many of the compounds that we analysed were too non-specific for useful conclusions to be made, other than to exclude the involvement of particular protein kinases in cellular processes.