Grouped Variable Model Selection for Heterogeneous Medical Signals

ABSTRACT We explore statistical regression techniques for use in medical monitoring and telehealth applications. Medical embedded systems of the present and future are recording vast sets of data related to medical conditions and physiology. In this paper, distributed time-lag linear models are proposed as a means to help explain relationships between two or more medical and physiological measurements. The issues associated with performing multiple regression with heterogeneous medical data are treated as problems in model selection. An automatic method of model selection is proposed to construct models for high sample rate data by grouping sets of predictor variables. The grouped predictor variable model optimization problem is formalized. Once an initial regression is performed on all available variables, our approximate algorithm for finding the grouped variable model with the greatest validity runs in O(n 2 ) time, where n is the number of available predictor variables. This is compared to the all subsets technique which requires O(2 n ) time for the same predictor set. In our experiments with medical signal data, we find that the method produces models with reasonable goodness of fit scores and high average confidence levels for grouped predictors

Internal combustion engine sensor network analysis using graph modeling

In recent years there has been a rapid development in technologies for smart monitoring applied to many different areas (e.g. building automation, photovoltaic systems, etc.). An intelligent monitoring system employs multiple sensors distributed within a network to extract useful information for decision-making. The management and the analysis of the raw data derived from the sensor network includes a number of specific challenges still unresolved, related to the different communication standards, the heterogeneous structure and the huge volume of data. In this paper we propose to apply a method based on complex network theory, to evaluate the performance of an Internal Combustion Engine. Data are gathered from the OBD sensor subset and from the emission analyzer. The method provides for the graph modeling of the sensor network, where the nodes are represented by the sensors and the edge are evaluated with non-linear statistical correlation functions applied to the time series pairs. The resulting functional graph is then analyzed with the topological metrics of the network, to define characteristic proprieties representing useful indicator for the maintenance and diagnosis

Trends of the major porin gene (ompF) evolution

OmpF is one of the major general porins of Enterobacteriaceae that belongs to the first line of bacterial defense and interactions with the biotic as well as abiotic environments. Porins are surface exposed and their structures strongly reflect the history of multiple interactions with the environmental challenges. Unfortunately, little is known on diversity of porin genes of Enterobacteriaceae and the genus Yersinia especially. We analyzed the sequences of the ompF gene from 73 Yersinia strains covering 14 known species. The phylogenetic analysis placed most of the Yersinia strains in the same line assigned by 16S rDNA-gyrB tree. Very high congruence in the tree topologies was observed for Y. enterocolitica, Y. kristensenii, Y. ruckeri, indicating that intragenic recombination in these species had no effect on the ompF gene. A significant level of intra- and interspecies recombination was found for Y. aleksiciae, Y. intermedia and Y. mollaretii. Our analysis shows that the ompF gene of Yersinia has evolved with nonrandom mutational rate under purifying selection. However, several surface loops in the OmpF porin contain positively selected sites, which very likely reflect adaptive diversification Yersinia to their ecological niches. To our knowledge, this is a first investigation of diversity of the porin gene covering the whole genus of the family Enterobacteriaceae. This study demonstrates that recombination and positive selection both contribute to evolution of ompF, but the relative contribution of these evolutionary forces are different among Yersinia species

TrAp: a Tree Approach for Fingerprinting Subclonal Tumor Composition

Revealing the clonal composition of a single tumor is essential for identifying cell subpopulations with metastatic potential in primary tumors or with resistance to therapies in metastatic tumors. Sequencing technologies provide an overview of an aggregate of numerous cells, rather than subclonal-specific quantification of aberrations such as single nucleotide variants (SNVs). Computational approaches to de-mix a single collective signal from the mixed cell population of a tumor sample into its individual components are currently not available. Herein we propose a framework for deconvolving data from a single genome-wide experiment to infer the composition, abundance and evolutionary paths of the underlying cell subpopulations of a tumor. The method is based on the plausible biological assumption that tumor progression is an evolutionary process where each individual aberration event stems from a unique subclone and is present in all its descendants subclones. We have developed an efficient algorithm (TrAp) for solving this mixture problem. In silico analyses show that TrAp correctly deconvolves mixed subpopulations when the number of subpopulations and the measurement errors are moderate. We demonstrate the applicability of the method using tumor karyotypes and somatic hypermutation datasets. We applied TrAp to SNV frequency profile from Exome-Seq experiment of a renal cell carcinoma tumor sample and compared the mutational profile of the inferred subpopulations to the mutational profiles of twenty single cells of the same tumor. Despite the large experimental noise, specific co-occurring mutations found in clones inferred by TrAp are also present in some of these single cells. Finally, we deconvolve Exome-Seq data from three distinct metastases from different body compartments of one melanoma patient and exhibit the evolutionary relationships of their subpopulations