CMDA: a tool for Continuous Monitoring Data Analysis

Over the last few years, with the growth of time-series collecting and storing, there has been a great demand for tools and software for temporal data engineering and modeling. This paper presents a generic workflow for time series data research, including temporal data importing, preprocessing, and feature extraction. This framework is developed and built as a robust and easy-to-use Python package, called CMDA, with a modular structure that offers tools to prepare raw data, allowing both scientists and non-experts to analyze various temporal data structures

Quantifying stability in gene list ranking across microarray derived clinical biomarkers

<p>Abstract</p> <p>Background</p> <p>Identifying stable gene lists for diagnosis, prognosis prediction, and treatment guidance of tumors remains a major challenge in cancer research. Microarrays measuring differential gene expression are widely used and should be versatile predictors of disease and other phenotypic data. However, gene expression profile studies and predictive biomarkers are often of low power, requiring numerous samples for a sound statistic, or vary between studies. Given the inconsistency of results across similar studies, methods that identify robust biomarkers from microarray data are needed to relay true biological information. Here we present a method to demonstrate that gene list stability and predictive power depends not only on the size of studies, but also on the clinical phenotype.</p> <p>Results</p> <p>Our method projects genomic tumor expression data to a lower dimensional space representing the main variation in the data. Some information regarding the phenotype resides in this low dimensional space, while some information resides in the residuum. We then introduce an information ratio (IR) as a metric defined by the partition between projected and residual space. Upon grouping phenotypes such as tumor tissue, histological grades, relapse, or aging, we show that higher IR values correlated with phenotypes that yield less robust biomarkers whereas lower IR values showed higher transferability across studies. Our results indicate that the IR is correlated with predictive accuracy. When tested across different published datasets, the IR can identify information-rich data characterizing clinical phenotypes and stable biomarkers.</p> <p>Conclusions</p> <p>The IR presents a quantitative metric to estimate the information content of gene expression data with respect to particular phenotypes.</p

Combined population dynamics and entropy modelling supports patient stratification in chronic myeloid leukemia

Modelling the parameters of multistep carcinogenesis is key for a better understanding of cancer progression, biomarker identification and the design of individualized therapies. Using chronic myeloid leukemia (CML) as a paradigm for hierarchical disease evolution we show that combined population dynamic modelling and CML patient biopsy genomic analysis enables patient stratification at unprecedented resolution. Linking CD34+ similarity as a disease progression marker to patientderived gene expression entropy separated established CML progression stages and uncovered additional heterogeneity within disease stages. Importantly, our patient data informed model enables quantitative approximation of individual patients’ disease history within chronic phase (CP) and significantly separates “early” from “late” CP. Our findings provide a novel rationale for personalized and genome-informed disease progression risk assessment that is independent and complementary to conventional measures of CML disease burden and prognosis

Принцип диалогичности извлечения экспертных знаний при оценке инноваций

В статье предлагается использовать принцип диалогичности при прогнозе эффективности инновационных продуктов на основе экспертных данных. Извлечение знаний экспертов в процессе диалога осуществляется с учетом их общественной роли и психофизиологических возможностей с пользованием компьютерных систем поддержки решений

Jumping behavior in singularly perturbed systems modelling bimolecular reactions

Singular singularly perturbed systems of ordinary differential equations modelling the dynamics of fast bimolecular reactions are considered. The asymptotic behavior of the solution of the initial value problem on a finite time interval is studied under conditions (change of stability) which are not treated in the usual standard theory. The application of the obtained results to the model under consideration yields conditions under which the reaction rate jumps. This behavior has to. be taken into account for identification problems in chemical process modelling

İşret, kumar, nisvan belası

Paul de Kock'un Sabah'ta yayımlanan İşret, Kumar, Nisvan Belası adlı romanının ilk ve son tefrikalar

The SYMBIONT Project: Symbolic Methods for Biological Networks

F1000Research 7:1341 (poster)SYMBIONT ranges from mathematics via computer science to systems biology, with a balanced team of researchers from those fields. At the present stage the project has a clear focus on fundamental research on mathematical methods and prototypes in software. Results are systematically bench-marked against models from computational biology databases. We summarize the motivation and aims for the project, and report on some existing results

Enabling multiscale modeling in systems medicine

CITATION: Wolkenhauer, O. et al. 2014. Enabling multiscale modeling in systems medicine. Genome Medicine, 6:21, doi:10.1186/gm538.The original publication is available at http://genomemedicine.biomedcentral.com[See article for abstract].Publisher's versio