scholarly journals A Siamese neural network model for the prioritization of metabolic disorders by integrating real and simulated data

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i787-i794
Author(s):  
Gian Marco Messa ◽  
Francesco Napolitano ◽  
Sarah H. Elsea ◽  
Diego di Bernardo ◽  
Xin Gao
Keyword(s):  
Predictive Accuracy ◽  
Hybrid Approach ◽  
Simulated Data ◽  
Original Data ◽  
Diagnostic Process ◽  
Great Promise ◽  
Metabolic Profiles ◽  
Inborn Errors ◽  
Metabolomic Data ◽  
Near Future

Abstract Motivation Untargeted metabolomic approaches hold a great promise as a diagnostic tool for inborn errors of metabolisms (IEMs) in the near future. However, the complexity of the involved data makes its application difficult and time consuming. Computational approaches, such as metabolic network simulations and machine learning, could significantly help to exploit metabolomic data to aid the diagnostic process. While the former suffers from limited predictive accuracy, the latter is normally able to generalize only to IEMs for which sufficient data are available. Here, we propose a hybrid approach that exploits the best of both worlds by building a mapping between simulated and real metabolic data through a novel method based on Siamese neural networks (SNN). Results The proposed SNN model is able to perform disease prioritization for the metabolic profiles of IEM patients even for diseases that it was not trained to identify. To the best of our knowledge, this has not been attempted before. The developed model is able to significantly outperform a baseline model that relies on metabolic simulations only. The prioritization performances demonstrate the feasibility of the method, suggesting that the integration of metabolic models and data could significantly aid the IEM diagnosis process in the near future. Availability and implementation Metabolic datasets used in this study are publicly available from the cited sources. The original data produced in this study, including the trained models and the simulated metabolic profiles, are also publicly available (Messa et al., 2020).

Potential Interventions Against BMPR2-Related Pulmonary Hypertension

2012 ◽  
Vol 11 (1) ◽  
pp. 25-32 ◽  
Author(s):  
James West ◽  
James E. Loyd ◽  
Rizwan Hamid
Keyword(s):  
Therapeutic Interventions ◽  
Great Promise ◽  
Patient Registries ◽  
Metabolic Defects ◽  
Pulmonary Arterial ◽  
Alternative Approaches ◽  
Splicing Defects ◽  
Trafficking Defects ◽  
Molecular Bases ◽  
Near Future

For more than 60 years, researchers have sought to understand the molecular basis of idiopathic pulmonary arterial hypertension (PAH). Recognition of the heritable form of the disease led to the creation of patient registries in the 1980s and 1990s, and discovery of BMPR2 as the cause of roughly 80% of heritable PAH in 2000. With discovery of the disease gene came opportunity for intervention, with focus on 2 alternative approaches. First, it may be possible to correct the effects of BMPR2 mutation directly through interventions targeted at correction of trafficking defects, increasing expression of the unmutated allele, and correction of splicing defects. Second, therapeutic interventions are being targeted at the signaling consequences of BMPR2 mutation. In particular, therapies targeting cytoskeletal and metabolic defects caused by BMPR2 mutation are currently in trials, or will be ready for human trials in the near future. Translation of these findings into therapies is the culmination of decades of research, and holds great promise for treatment of the underlying molecular bases of disease.

Applying the risk principle to optimize accuracy and equity in correctional risk assessment: Results From a Simulation

2021 ◽  
pp. 0306624X2098652
Author(s):  
Grant Duwe
Keyword(s):  
Risk Assessment ◽  
Predictive Accuracy ◽  
Ethnic Disparities ◽  
Simulated Data ◽  
Risk Assessments ◽  
Risk Level ◽  
Risk Levels ◽  
Risk Principle ◽  
Changes In Risk ◽  
The Impact

As the use of risk assessments for correctional populations has grown, so has concern that these instruments exacerbate existing racial and ethnic disparities. While much of the attention arising from this concern has focused on how algorithms are designed, relatively little consideration has been given to how risk assessments are used. To this end, the present study tests whether application of the risk principle would help preserve predictive accuracy while, at the same time, mitigate disparities. Using a sample of 9,529 inmates released from Minnesota prisons who had been assessed multiple times during their confinement on a fully-automated risk assessment, this study relies on both actual and simulated data to examine the impact of program assignment decisions on changes in risk level from intake to release. The findings showed that while the risk principle was used in practice to some extent, the simulated results showed that greater adherence to the risk principle would increase reductions in risk levels and minimize the disparities observed at intake. The simulated data further revealed the most favorable outcomes would be achieved by not only applying the risk principle, but also by expanding program capacity for the higher-risk inmates in order to adequately reduce their risk.

scholarly journals Mapping EQ-5D-3L from the Knee Injury and Osteoarthritis Outcome Score (KOOS)

2019 ◽  
Vol 29 (1) ◽  
pp. 265-274
Author(s):  
Ali Kiadaliri ◽  
Monica Hernández Alava ◽  
Ewa M. Roos ◽  
Martin Englund
Keyword(s):  
Disease Severity ◽  
Knee Injury ◽  
Cruciate Ligament ◽  
Simulated Data ◽  
Probit Model ◽  
Original Data ◽  
Cost Utility ◽  
Superior Performance ◽  
Outcome Score ◽  
Mapping Models

Abstract Purpose To develop a mapping model to estimate EQ-5D-3L from the Knee Injury and Osteoarthritis Outcome Score (KOOS). Methods The responses to EQ-5D-3L and KOOS questionnaires (n = 40,459 observations) were obtained from the Swedish National anterior cruciate ligament (ACL) Register for patients ≥ 18 years with the knee ACL injury. We used linear regression (LR) and beta-mixture (BM) for direct mapping and the generalized ordered probit model for response mapping (RM). We compared the distribution of the original data to the distributions of the data generated using the estimated models. Results Models with individual KOOS subscales performed better than those with the average of KOOS subscale scores (KOOS5, KOOS4). LR had the poorest performance overall and across the range of disease severity particularly at the extremes of the distribution of severity. Compared with the RM, the BM performed better across the entire range of disease severity except the most severe range (KOOS5 < 25). Moving from the most to the least disease severity was associated with 0.785 gain in the observed EQ-5D-3L. The corresponding value was 0.743, 0.772 and 0.782 for LR, BM and RM, respectively. LR generated simulated EQ-5D-3L values outside the feasible range. The distribution of simulated data generated from the BM model was almost identical to the original data. Conclusions We developed mapping models to estimate EQ-5D-3L from KOOS facilitating application of KOOS in cost-utility analyses. The BM showed superior performance for estimating EQ-5D-3L from KOOS. Further validation of the estimated models in different independent samples is warranted.

scholarly journals Risk prediction in multicentre studies when there is confounding by cluster or informative cluster size

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Menelaos Pavlou ◽  
Gareth Ambler ◽  
Rumana Z. Omar
Keyword(s):  
Risk Prediction ◽  
Cluster Size ◽  
Linear Models ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Clustered Data ◽  
Predictor Variable ◽  
Simulated Data ◽  
Predictive Ability ◽  
Informative Cluster Size

Abstract Background Clustered data arise in research when patients are clustered within larger units. Generalised Estimating Equations (GEE) and Generalised Linear Models (GLMM) can be used to provide marginal and cluster-specific inference and predictions, respectively. Methods Confounding by Cluster (CBC) and Informative cluster size (ICS) are two complications that may arise when modelling clustered data. CBC can arise when the distribution of a predictor variable (termed ‘exposure’), varies between clusters causing confounding of the exposure-outcome relationship. ICS means that the cluster size conditional on covariates is not independent of the outcome. In both situations, standard GEE and GLMM may provide biased or misleading inference, and modifications have been proposed. However, both CBC and ICS are routinely overlooked in the context of risk prediction, and their impact on the predictive ability of the models has been little explored. We study the effect of CBC and ICS on the predictive ability of risk models for binary outcomes when GEE and GLMM are used. We examine whether two simple approaches to handle CBC and ICS, which involve adjusting for the cluster mean of the exposure and the cluster size, respectively, can improve the accuracy of predictions. Results Both CBC and ICS can be viewed as violations of the assumptions in the standard GLMM; the random effects are correlated with exposure for CBC and cluster size for ICS. Based on these principles, we simulated data subject to CBC/ICS. The simulation studies suggested that the predictive ability of models derived from using standard GLMM and GEE ignoring CBC/ICS was affected. Marginal predictions were found to be mis-calibrated. Adjusting for the cluster-mean of the exposure or the cluster size improved calibration, discrimination and the overall predictive accuracy of marginal predictions, by explaining part of the between cluster variability. The presence of CBC/ICS did not affect the accuracy of conditional predictions. We illustrate these concepts using real data from a multicentre study with potential CBC. Conclusion Ignoring CBC and ICS when developing prediction models for clustered data can affect the accuracy of marginal predictions. Adjusting for the cluster mean of the exposure or the cluster size can improve the predictive accuracy of marginal predictions.

scholarly journals Prospects for p53-based cancer therapy.

2005 ◽  
Vol 52 (2) ◽  
pp. 321-328 ◽  
Author(s):  
Tomasz Stokłosa ◽  
Jakub Gołab
Keyword(s):  
Human Tumor ◽  
P53 Gene ◽  
Great Promise ◽  
Mutant Form ◽  
Wild Type ◽  
P53 Pathway ◽  
Cycle Arrest ◽  
Stress Signals ◽  
Near Future

The p53 tumor suppressor plays the role of a cellular hub which gathers stress signals such as damage to DNA or hypoxia and translates them into a complex response. p53 exerts its action mainly as a potent transcription factor. The two major outcomes of p53 activity are highlighted: cell cycle arrest and apoptosis. During malignant transformation p53 or p53-pathway related molecules are disabled extremely often. Mutations in p53 gene are present in every second human tumor. A mutant form of p53 may not only negate the wild type p53 function but may play additional role in tumor progression. Therefore p53 represents a relatively unique and specific target for anticancer drug design. Current approaches include several different molecules able to restore p53 wild-type conformation and activity. Such small molecule drugs hold great promise in treating human tumors with dysfunction of p53 pathway in the near future.

scholarly journals Extensive Metabolic Profiles of Leaves and Stems from the Medicinal Plant Dendrobium officinale Kimura et Migo

Metabolites ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 215 ◽  
Author(s):  
Cao ◽  
Ji ◽  
Li ◽  
Lu ◽  
Tian ◽  
...  
Keyword(s):  
Genomic Research ◽  
Dendrobium Officinale ◽  
Metabolic Profiles ◽  
Asian Countries ◽  
Targeted Metabolomics ◽  
Medicinal Species ◽  
Metabolomic Data ◽  
Metabolite Profiles ◽  
Widely Targeted Metabolomics ◽  
Metabolic Data

Dendrobium officinale Kimura et Migo is a commercially and pharmacologically highly prized species widely used in Western Asian countries. In contrast to the extensive genomic and transcriptomic resources generated in this medicinal species, detailed metabolomic data are still missing. Herein, using the widely targeted metabolomics approach, we detect 649 diverse metabolites in leaf and stem samples of D. officinale. The majority of these metabolites were organic acids, amino acids and their derivatives, nucleotides and their derivatives, and flavones. Though both organs contain similar metabolites, the metabolite profiles were quantitatively different. Stems, the organs preferentially exploited for herbal medicine, contained larger concentrations of many more metabolites than leaves. However, leaves contained higher levels of polyphenols and lipids. Overall, this study reports extensive metabolic data from leaves and stems of D. officinale, providing useful information that supports ongoing genomic research and discovery of bioactive compounds.

scholarly journals On the Use of Correlation and MI as a Measure of Metabolite—Metabolite Association for Network Differential Connectivity Analysis

Metabolites ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 171
Author(s):  
Sanjeevan Jahagirdar ◽  
Edoardo Saccenti
Keyword(s):  
Molecular Mechanisms ◽  
Real Life ◽  
Simulated Data ◽  
Metabolic Model ◽  
Data Sets ◽  
Connectivity Analysis ◽  
Estimation Algorithms ◽  
Metabolomic Data ◽  
Spearman’S Correlation ◽  
Differential Connectivity

Metabolite differential connectivity analysis has been successful in investigating potential molecular mechanisms underlying different conditions in biological systems. Correlation and Mutual Information (MI) are two of the most common measures to quantify association and for building metabolite—metabolite association networks and to calculate differential connectivity. In this study, we investigated the performance of correlation and MI to identify significantly differentially connected metabolites. These association measures were compared on (i) 23 publicly available metabolomic data sets and 7 data sets from other fields, (ii) simulated data with known correlation structures, and (iii) data generated using a dynamic metabolic model to simulate real-life observed metabolite concentration profiles. In all cases, we found more differentially connected metabolites when using correlation indices as a measure for association than MI. We also observed that different MI estimation algorithms resulted in difference in performance when applied to data generated using a dynamic model. We concluded that there is no significant benefit in using MI as a replacement for standard Pearson’s or Spearman’s correlation when the application is to quantify and detect differentially connected metabolites.

scholarly journals X-ray extinction from interstellar dust

2019 ◽  
Vol 629 ◽  
pp. A78 ◽  
Author(s):  
E. Costantini ◽  
S. T. Zeegers ◽  
D. Rogantini ◽  
C. P. de Vries ◽  
A. G. G. M. Tielens ◽  
...  
Keyword(s):  
Interstellar Medium ◽  
Interstellar Dust ◽  
Simulated Data ◽  
X Ray ◽  
Cosmic Abundance ◽  
Grain Size Distributions ◽  
Edge Features ◽  
Different Grain Size ◽  
Near Future

Aims. We present a study on the prospects of observing carbon, sulfur, and other lower abundance elements (namely Al, Ca, Ti, and Ni) present in the interstellar medium using future X-ray instruments. We focus in particular on the detection and characterization of interstellar dust along the lines of sight. Methods. We compared the simulated data with different sets of dust aggregates, either obtained from past literature or measured by us using the SOLEIL-LUCIA synchrotron beamline. Extinction by interstellar grains induces modulations of a given photolelectric edge, which can be in principle traced back to the chemistry of the absorbing grains. We simulated data of instruments with characteristics of resolution and sensitivity of the current Athena, XRISM, and Arcus concepts. Results. In the relatively near future, the depletion and abundances of the elements under study will be determined with confidence. In the case of carbon and sulfur, the characterization of the chemistry of the absorbing dust will be also determined, depending on the dominant compound. For aluminum and calcium, despite the large depletion in the interstellar medium and the prominent dust absorption, in many cases the edge feature may not be changing significantly with the change of chemistry in the Al- or Ca-bearing compounds. The exinction signature of large grains may be detected and modeled, allowing a test on different grain size distributions for these elements. The low cosmic abundance of Ti and Ni will not allow us a detailed study of the edge features.

scholarly journals 330 A hybrid model for genomic selection using prioritized SNPs based on FST scores in the presence of non-genotyped animals

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 51-51
Author(s):  
Sajjad Toghiani ◽  
Ling-Yun Chang ◽  
El H Hay ◽  
Andrew J Roberts ◽  
Samuel E Aggrey ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Hybrid Approach ◽  
Computational Cost ◽  
Simulated Data ◽  
Snp Markers ◽  
Genomic Relationship Matrix ◽  
Polygenic Effect ◽  
Relationship Matrix ◽  
Continuous Increase ◽  
Missing Genotypes

Abstract The dramatic advancement in genotyping technology has greatly reduced the complexity and cost of genotyping. The continuous increase in the density of marker panels is resulting in little to no improvement in the accuracy of genomic selection. Direct inversion of the genomic relationship matrix is infeasible for some livestock populations due to the excessive computational cost. In addition, most animals in genetic evaluation programs are non-genotyped. Including these animals in a genomic evaluation requires the imputation of the missing genotypes when using regression methods. To overcome these challenges, a hybrid approach is proposed. This approach fits a subset of SNP markers selected based on FST scores and a classical polygenic effect. The method was first tested using only genotyped animals and then extended to accommodate non-genotyped animals. The proposed approach was evaluated using simulated data for a trait with heritability of 0.1 and 0.4 and weaning weight in a crossbred beef cattle population. When all animals were genotyped, the hybrid approach using only 2.5% of prioritized SNPs exceeded the prediction accuracies of BayesB, BayesC, and GBLUP by more than 7%. When non-genotyped animals were incorporated, the proposed approach significantly outperformed ss-GBLUP method in terms of prediction accuracy under both simulated heritability scenarios. Although the results seem to depend on the genetic complexity of the trait, the proposed approach resulted in higher prediction accuracies than current methods. Furthermore, its computational costs in terms of CPU time and peak memory are substantially lower than the current methods.

Building better rechargeable Zn–Mn batteries with a highly active Mn3O4/carbon nanowire cathode and neutral Na2SO4/MnSO4 electrolyte

2018 ◽  
Vol 54 (77) ◽  
pp. 10835-10838 ◽  
Author(s):  
Lai Ma ◽  
Linpo Li ◽  
Yani Liu ◽  
Jianhui Zhu ◽  
Ting Meng ◽  
...  
Keyword(s):  
Power Supply ◽  
Great Promise ◽  
Battery System ◽  
Highly Active ◽  
Carbon Nanowire ◽  
Near Future ◽  
Carbon Nanowires

A more reliable/eco-friendly secondary Zn–Mn battery system is built with highly active Mn3O4@carbon nanowires and near-neutral electrolytes. Such configured batteries show high reversibility and superior behavior in terms of both stored capacity and cycling durability, holding great promise in near-future power-supply applications.

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