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ADEMA: An Algorithm to Determine Expected Metabolite Level Alterations Using Mutual Information
A. Ercument Cicek ,Ilya Bederman,Leigh Henderson,Mitchell L. Drumm,Gultekin Ozsoyoglu
PLOS Computational Biology , 2013, DOI: 10.1371/journal.pcbi.1002859
Abstract: Metabolomics is a relatively new “omics” platform, which analyzes a discrete set of metabolites detected in bio-fluids or tissue samples of organisms. It has been used in a diverse array of studies to detect biomarkers and to determine activity rates for pathways based on changes due to disease or drugs. Recent improvements in analytical methodology and large sample throughput allow for creation of large datasets of metabolites that reflect changes in metabolic dynamics due to disease or a perturbation in the metabolic network. However, current methods of comprehensive analyses of large metabolic datasets (metabolomics) are limited, unlike other “omics” approaches where complex techniques for analyzing coexpression/coregulation of multiple variables are applied. This paper discusses the shortcomings of current metabolomics data analysis techniques, and proposes a new multivariate technique (ADEMA) based on mutual information to identify expected metabolite level changes with respect to a specific condition. We show that ADEMA better predicts De Novo Lipogenesis pathway metabolite level changes in samples with Cystic Fibrosis (CF) than prediction based on the significance of individual metabolite level changes. We also applied ADEMA's classification scheme on three different cohorts of CF and wildtype mice. ADEMA was able to predict whether an unknown mouse has a CF or a wildtype genotype with 1.0, 0.84, and 0.9 accuracy for each respective dataset. ADEMA results had up to 31% higher accuracy as compared to other classification algorithms. In conclusion, ADEMA advances the state-of-the-art in metabolomics analysis, by providing accurate and interpretable classification results.
Modeling Multi-Echelon Multi-Supplier Repairable Inventory Systems with Backorders  [PDF]
Yael Perlman, Ilya Levner
Journal of Service Science and Management (JSSM) , 2010, DOI: 10.4236/jssm.2010.34050
Abstract: This paper considers an inventory system responsible for repairable equipments located at several operational sites, each in different area. When a failure occurs at the operational site, spare parts are required. We analyze a multiple-supplier inventory system that includes an internal repair shop that offers several modes of repair with different repair times and an external supplier of spare parts. The network model of the problem presented here efficiently solves the problem for deterministic demands that vary over time with backorders taken into account.
Perishable Inventory Management in Healthcare  [PDF]
Yael Perlman, Ilya Levner
Journal of Service Science and Management (JSSM) , 2014, DOI: 10.4236/jssm.2014.71002

This study addresses a problem encountered in a nation-wide, large-scale healthcare supply chain that comprises several hundred medical organizations (hospitals, clinics, pharmacies, etc.) and provides highly advanced medical care to several million people. The medical products in the system are perishable, meaning that they become unusable beyond a certain expiry date. It is necessary to track the ages of units in stock and to plan and control the inventory accordingly. The models developed herein represent a multi-echelon, multi-supplier inventory system and unite together aspects of perishability and outsourcing under deterministic demand for medical products, which include both perishable and deteriorating goods. The objective of the study is to determine the optimal number of products to be purchased from regular and outsource suppliers so as to meet the required demand at the minimum operating cost. The solution is a network-flow model that can be used to determine the trade-off between the quantities of items to be ordered from the two types of suppliers in each time period. In addition, the study analyzes different distribution policies to account for the perishable nature of the products. Further insights are obtained by applying the model to a case study of a real-life healthcare supply chain from which interesting results are drawn.

Atherogenesis, the oxidative LDL modification hypothesis revisited  [PDF]
Dov Lichtenberg, Ilya Pinchuk
Advances in Bioscience and Biotechnology (ABB) , 2013, DOI: 10.4236/abb.2013.411A2007

The commonly-accepted “oxidized LDL hypothesis of atherogenesis” is based on a large number of indirect evidence that shows that oxidatively-modified LDL plays a role in atherogenesis. Yet, the exact role is not clear. Some researchers think that oxidatively modified biomolecules initiate atherogenesis; others believe that they “only” promote this multifactorial process. Regardless of the exact mechanism responsible for the effect of peroxidation on atherogenesis, the “oxidative theory of AS” is apparently inconsistent with the results of meta-analysis, in which (the “expected”) significant correlation between CVD and oxidative stress (OS) was found only when the OS was evaluated on the basis of the plasma concentrations of malondialdehyde (MDA), often based on the concentration of thiobarbituric acid reactive substances (TBARS). Notably, even this association is questionable due to 1) poor reliability of the laboratory assay of MDA and 2) possible publication bias. Hence, it appears that the commonly accepted paradigm regarding the role of oxidative damage in the pathogenesis of CVD has been overestimated. Furthermore, the hypothesis is apparently inconsistent with the disappointing results of most of the clinical trials that were designed to reduce OS by means of supplementation of antioxidants, mostly vitamin E. These apparent inconsistencies do not contradict the oxidative modification hypothesis of AS. The source of the apparent contradictions is probably the oversimplified considerations on which the predictions have been based. Many reasonable arguments can be raised to explain the apparent contradictions, which means that our current knowledge is insufficient to test the relationship of oxidative stress to cardiovascular disease.

Cosmological Constant and Energy Density of Random Electromagnetic Field  [PDF]
Ilya A. Obukhov
Journal of High Energy Physics, Gravitation and Cosmology (JHEPGC) , 2016, DOI: 10.4236/jhepgc.2016.23028
Abstract: It is shown that the non-equilibrium electrically neutral and relativistically invariant vacuum-like state with the negative energy density and positive pressure may exist at the non-zero temperature in the system of spinor particles, antiparticles, and random electromagnetic field generated by particle-particle, particle-antiparticle, and antiparticle-antiparticle transitions. At the temperature of the order of 10 -5 K, the energy density of its state corresponds to the dark energy density in absolute magnitude. The cosmological constant for such material medium turns out to be negative.
Density of Vacuum-Like Plasma and Hubble Constant  [PDF]
Ilya A. Obukhov
Journal of High Energy Physics, Gravitation and Cosmology (JHEPGC) , 2017, DOI: 10.4236/jhepgc.2017.34044
Abstract: The model in which expansion of the Universe leads to a generation of non-equilibrium vacuum-like electron-positron plasma is proposed and researched. The formulas that relate the Hubble’s constant with the concentration of plasma particles and the cosmological constant are obtained. The collective properties of vacuum-like plasma are investigated. It is shown, that the coefficient of a two-photon annihilation in such plasma is nine times less than for the free particles. A simple formula for dark energy density as a function of electron mass and charge is obtained. It was demonstrated that acceleration of plasma’s chemical potential fluctuations flow proportional of dark energy density.
The Constructionist Learning Approach in the Digital Age  [PDF]
Ilya Levin, Dina Tsybulsky
Creative Education (CE) , 2017, DOI: 10.4236/ce.2017.815169
Abstract: The article deals with the evolution of the constructionist learning approach from the beginning of the era of information technologies through the digital age. The evolution of constructionism is demonstrated in connection with two associated processes: changes in the human worldview related to the digital shift, and the corresponding transformations in human society. The study examines the evolution of basic constructionist ideas: 1) “microworlds” as “incubators of knowledge”; 2) a child as “the architect of his [or her] intelligent structures”; 3) the computer as “a machine that brings back a natural character to learning”; 4) coding as a “universal learning activity” that enables the study of fundamental scientific ideas. The constructionist ideas are analyzed in the context of today’s digital reality. The main contribution of the study is formulating the changes in classical constructionism as transformations that correspond to worldview components: activating the perception of self; democratization of the mutual interactions with others; virtualization of the conception of reality; integration the subject and object in their interaction with reality.
Coincidences and Estimation of Entropies of Random Variables with Large Cardinalities
Ilya Nemenman
Entropy , 2011, DOI: 10.3390/e13122013
Abstract: We perform an asymptotic analysis of the NSB estimator of entropy of a discrete random variable. The analysis illuminates the dependence of the estimates on the number of coincidences in the sample and shows that the estimator has a well defined limit for a large cardinality of the studied variable. This allows estimation of entropy with no a priori assumptions about the cardinality. Software implementation of the algorithm is available.
Feature selection and nearest centroid classification for protein mass spectrometry
Ilya Levner
BMC Bioinformatics , 2005, DOI: 10.1186/1471-2105-6-68
Abstract: This study examines the performance of the nearest centroid classifier coupled with the following feature selection algorithms. Student-t test, Kolmogorov-Smirnov test, and the P-test are univariate statistics used for filter-based feature ranking. From the wrapper approaches we tested sequential forward selection and a modified version of sequential backward selection. Embedded approaches included shrunken nearest centroid and a novel version of boosting based feature selection we developed. In addition, we tested several dimensionality reduction approaches, namely principal component analysis and principal component analysis coupled with linear discriminant analysis. To fairly assess each algorithm, evaluation was done using stratified cross validation with an internal leave-one-out cross-validation loop for automated feature selection. Comprehensive experiments, conducted on five popular cancer data sets, revealed that the less advocated sequential forward selection and boosted feature selection algorithms produce the most consistent results across all data sets. In contrast, the state-of-the-art performance reported on isolated data sets for several of the studied algorithms, does not hold across all data sets.This study tested a number of popular feature selection methods using the nearest centroid classifier and found that several reportedly state-of-the-art algorithms in fact perform rather poorly when tested via stratified cross-validation. The revealed inconsistencies provide clear evidence that algorithm evaluation should be performed on several data sets using a consistent (i.e., non-randomized, stratified) cross-validation procedure in order for the conclusions to be statistically sound.Advances in protein mass spectrometry have have recently shown great potential for high-throughput disease classification and biomarker identification. In turn, fast and accurate detection of diseases, such as early cancer detection, can revolutionize the field of medical
Multinomial Least Angle Regression with Application to Web Personalization
Ilya Gluhovsky
Lecture Notes in Engineering and Computer Science , 2011,
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