Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data?

A large number of prognostic and predictive signatures have been proposed for breast cancer and a few of these are now available in the clinic as new molecular diagnostic tests. However, several other signatures have not fared well in validation studies. Some investigators continue to be puzzled by the diversity of signatures that are being developed for the same purpose but that share few or no common genes. The history of empirical development of prognostic gene signatures and the unique association between molecular subsets and clinical phenotypes of breast cancer explain many of these apparent contradictions in the literature. Three features of breast cancer gene expression contribute to this: the large number of individually prognostic genes (differentially expressed between good and bad prognosis cases); the unstable rankings of differentially expressed genes between datasets; and the highly correlated expression of informative genes

Analysis of activity times in the process of a wooden box manufacturing

The main target of companies is to earn money and achieve profit. In order to fulfil these needs, companies have to reduce their costs. Cost reduction is often associated with bad quality products, but it could be done in a different way. Sometimes it is enough to examine only their own processes and then benefit from the process optimization, process improvement or process scheduling. In this article a case study is presented, in which the differences in a production scheduling are evaluated with the application of Monte-Carlo simulation and descriptive statistics. At the end of the paper the most efficient material sequence is selected at the manufacturing company by using weighted sum mode

Genomic determinants of homologous recombination deficiency across human cancers

Germlin

Lack of sufficiently strong informative features limits the potential of gene expression analysis as predictive tool for many clinical classification problems

<p>Abstract</p> <p>Background</p> <p>Our goal was to examine how various aspects of a gene signature influence the success of developing multi-gene prediction models. We inserted gene signatures into three real data sets by altering the expression level of existing probe sets. We varied the number of probe sets perturbed (signature size), the fold increase of mean probe set expression in perturbed compared to unperturbed data (signature strength) and the number of samples perturbed. Prediction models were trained to identify which cases had been perturbed. Performance was estimated using Monte-Carlo cross validation.</p> <p>Results</p> <p>Signature strength had the greatest influence on predictor performance. It was possible to develop almost perfect predictors with as few as 10 features if the fold difference in mean expression values were > 2 even when the spiked samples represented 10% of all samples. We also assessed the gene signature set size and strength for 9 real clinical prediction problems in six different breast cancer data sets.</p> <p>Conclusions</p> <p>We found sufficiently large and strong predictive signatures only for distinguishing ER-positive from ER-negative cancers, there were no strong signatures for more subtle prediction problems. Current statistical methods efficiently identify highly informative features in gene expression data if such features exist and accurate models can be built with as few as 10 highly informative features. Features can be considered highly informative if at least 2-fold expression difference exists between comparison groups but such features do not appear to be common for many clinically relevant prediction problems in human data sets.</p

Investigation of a Production Process Under Uncertainty

L

Industrial process modelling with operations research method

The operation of a business process is not as easy as it seems for the first time. A lot of complex connections can be identified in a production process, while the performance of a machine can be either uncertain or unknown. These factors could result in inappropriate conclusions and decisions. Collecting data is an essential part of business process modelling, while operations research methods can provide a good evaluation tool for making the right decisions. The article aims at presenting a process rationalization while total process time optimization is carried out based on a minimal extra cost investment. The examined variables of the optimization were total process time and cost of human resource. The target value of total process time reduction was 10%

Alapanyag áramlásának optimalizálása általánosított hálózati folyam modellel

Minden vállalat számára fontos az üzleti folyamataik pontos ismerete, ugyanis a versenyképesség fenntartása érdekében a vállalatoknak hatékony módon kell az erőforrásaikat allokálniuk. Kutatásunk során egy faládákat műhelyrendszerben gyártó vállalat anyagáramlásának optimalizálását tűztük ki célul hálózati modell segítségével, hiszen ezt a módszert széles körben alkalmazzák termelési folyamat modellezésére. A vizsgálat első részében két célfüggvény szerint számoltuk a termelés optimális útvonalait, majd egy kompromisszumos megoldást kerestünk, és a 3 modell eredményét összevetettük