Knockdown of platinum-induced growth differentiation factor 15 abrogates p27-mediated tumor growth delay in the chemoresistant ovarian cancer model A2780cis.

Molecular mechanisms underlying the development of resistance to platinum-based treatment in patients with ovarian cancer remain poorly understood. This is mainly due to the lack of appropriate in vivo models allowing the identification of resistance-related factors. In this study, we used human whole-genome microarrays and linear model analysis to identify potential resistance-related genes by comparing the expression profiles of the parental human ovarian cancer model A2780 and its platinum-resistant variant A2780cis before and after carboplatin treatment in vivo.

Prediction errors in learning drug response from gene expression data - influence of labeling, sample size, and machine learning algorithm.

Model-based prediction is dependent on many choices ranging from the sample collection and prediction endpoint to the choice of algorithm and its parameters. Here we studied the effects of such choices, exemplified by predicting sensitivity (as IC50) of cancer cell lines towards a variety of compounds. For this, we used three independent sample collections and applied several machine learning algorithms for predicting a variety of endpoints for drug response.

Genome Fusion Detection: a novel method to detect fusion genes from SNP-array data.

Motivation: Fusion genes result from genomic rearrangements, such as deletions, amplifications and translocations. Such rearrangements can also frequently be observed in cancer and have been postulated as driving event in cancer development. to detect them, one needs to analyze the transition region of two segments with different copy number, the location where fusions are known to occur.

Phenotype mining for functional genomics and gene discovery.

In gene prediction, studying phenotypes is highly valuable for reducing the number of locus candidates in association studies and to aid disease gene candidate prioritization. This is due to the intrinsic nature of phenotypes to visibly reflect genetic activity, making them potentially one of the most useful data types for functional studies. However, systematic use of these data has begun only recently.

Influence of tropospheric ozone control on exposure to ultraviolet radiation at the surface.

Improving air quality by reducing ambient ozone (O(3)) will likely lower O(3) concentrations throughout the troposphere and increase the transmission of solar ultraviolet (UV) radiation to the surface. The changes in surface UV radiation between two control scenarios (nominally 84 and 70 ppb O(3) for summer 2020) in the Eastern two-thirds of the contiguous U.S.

National housing and impervious surface scenarios for integrated climate impact assessments.

Understanding the impacts of climate change on people and the environment requires an understanding of the dynamics of both climate and land use/land cover changes. A range of future climate scenarios is available for the conterminous United States that have been developed based on widely used international greenhouse gas emissions storylines. Climate scenarios derived from these emissions storylines have not been matched with logically consistent land use/cover maps for the United States.

Phenoclustering: online mining of cross-species phenotypes.

Summary: Recently, several methods for analyzing phenotype data have been published, but only few are able to cope with data sets generated in different studies, with different methods, or for different species. We developed an online system in which more than 300 000 phenotypes from a wide variety of sources and screening methods can be analyzed together. Clusters of similar phenotypes are visualized as networks of highly similar phenotypes, inducing gene groups useful for functional analysis.

Mining phenotypes for gene function prediction.

Background: Health and disease of organisms are reflected in their phenotypes. Often, a genetic component to a disease is discovered only after clearly defining its phenotype. In the past years, many technologies to systematically generate phenotypes in a high-throughput manner, such as RNA interference or gene knock-out, have been developed and used to decipher functions for genes.

PhenomicDB: a new cross-species genotype/phenotype resource.

Phenotypes are an important subject of biomedical research for which many repositories have already been created. Most of these databases are either dedicated to a single species or to a single disease of interest. With the advent of technologies to generate phenotypes in a high-throughput manner, not only is the volume of phenotype data growing fast but also the need to organize these data in more useful ways.