1,721,042 research outputs found
A multicategory logit model detecting temporal changes in antimicrobial resistance
No full textMonitoring and investigating temporal trends in antimicrobial data is a high priority for human and animal health authorities. Timely detection of temporal changes in antimicrobial resistance (AMR) can rely not only on monitoring and analyzing the proportion of resistant isolates based on the use of a clinical or epidemiological cut-off value, but also on more subtle changes and trends in the full distribution of minimum inhibitory concentration (MIC) values. The nature of the MIC distribution is categorical and ordinal (discrete). In this contribution, we developed a particular family of multicategory logit models for estimating and modelling MIC distributions over time. It allows the detection of a multitude of temporal trends in the full discrete distribution, without any assumption on the underlying continuous distribution for the MIC values. The experimental ranges of the serial dilution experiments may vary across laboratories and over time. The proposed categorical model allows to estimate the MIC distribution over the maximal range of the observed experiments, and allows the observed ranges to vary across labs and over time. The use and performance of the model is illustrated with two datasets on AMR in Salmonella.The authors thank the official veterinary services from the Spanish Ministry of Agriculture, Fisheries and Food for providing the CIPR data. The authors also thank the editors and reviewers for their constructive comments
Optimising machine learning prediction of minimum inhibitory concentrations in Klebsiella pneumoniae
No full textMinimum Inhibitory Concentrations (MICs) are the gold standard for quantitatively measuring antibiotic resistance.
However, lab-based MIC determination can be time-consuming and suffers from low reproducibility, and interpretation as sensitive or resistant relies on guidelines which change over time. Genome sequencing and machine learning
promise to allow in silico MIC prediction as an alternative approach which overcomes some of these difficulties, albeit
the interpretation of MIC is still needed. Nevertheless, precisely how we should handle MIC data when dealing with
predictive models remains unclear, since they are measured semi-quantitatively, with varying resolution, and are
typically also left- and right-censored within varying ranges. We therefore investigated genome-based prediction of
MICs in the pathogen Klebsiella pneumoniae using 4367 genomes with both simulated semi-quantitative traits and real
MICs. As we were focused on clinical interpretation, we used interpretable rather than black-box machine learning
models, namely, Elastic Net, Random Forests, and linear mixed models. Simulated traits were generated accounting
for oligogenic, polygenic, and homoplastic genetic effects with different levels of heritability. Then we assessed how
model prediction accuracy was affected when MICs were framed as regression and classification. Our results showed
that treating the MICs differently depending on the number of concentration levels of antibiotic available was the most
promising learning strategy. Specifically, to optimise both prediction accuracy and inference of the correct causal variants, we recommend considering the MICs as continuous and framing the learning problem as a regression when the
number of observed antibiotic concentration levels is large, whereas with a smaller number of concentration levels
they should be treated as a categorical variable and the learning problem should be framed as a classification. Our
findings also underline how predictive models can be improved when prior biological knowledge is taken into account,
due to the varying genetic architecture of each antibiotic resistance trait. Finally, we emphasise that incrementing
the population database is pivotal for the future clinical implementation of these models to support routine machinelearning based diagnostic
Metabolic Network Analysis Demystified
Full text link15th Annual International Conference, RECOMB 2011, Vancouver, BC, Canada, March 28-31, 2011. ProceedingsMetabolic networks are a representation of current knowledge about the metabolic reactions available to a given organism. These networks can be placed into various mathematical frameworks, of which the constraintbased framework [1] has received the most attention over the past 15 years. This results in a predictive model of metabolism. Metabolic models can yield predictions of two types: quantitative, such as the growth rate of an organism under given experimental conditions [2], and qualitative, such as the viability of a mutant [3] or minimal media required for growth [4]. Qualitative predictions, on which we focus, tend to be more robust and reliable than quantitative ones, while remaining experimentally testable and biologically relevant
An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models
Full text linkConstraint-based models are currently the only methodology that allows the study of metabolism at the whole-genome scale. Flux balance analysis is commonly used to analyse constraint-based models. Curiously, the results of this analysis vary with the software being run, a situation that we show can be remedied by using exact rather than floating-point arithmetic. Here we introduce MONGOOSE, a toolbox for analysing the structure of constraint-based metabolic models in exact arithmetic. We apply MONGOOSE to the analysis of 98 existing metabolic network models and find that the biomass reaction is surprisingly blocked (unable to sustain non-zero flux) in nearly half of them. We propose a principled approach for unblocking these reactions and extend it to the problems of identifying essential and synthetic lethal reactions and minimal media. Our structural insights enable a systematic study of constraint-based metabolic models, yielding a deeper understanding of their possibilities and limitations.National Institutes of Health (U.S.) (Grant GM108348)Howard Hughes Medical Institut
MetaMerge: scaling up genome-scale metabolic reconstructions, with application to Mycobacterium tuberculosis
Full text linkReconstructed models of metabolic networks are widely used for studying metabolism in various organisms. Many different reconstructions of the same organism often exist concurrently, forcing researchers to choose one of them at the exclusion of the others. We describe MetaMerge, an algorithm for semi-automatically reconciling a pair of existing metabolic network reconstructions into a single metabolic network model. We use MetaMerge to combine two published metabolic networks for Mycobacterium tuberculosis into a single network, which allows many reactions that could not be active in the individual models to become active, and predicts essential genes with a higher positive predictive value.Natural Sciences and Engineering Research Council of Canada (NSERC) (Postgraduate Award)Howard Hughes Medical InstituteBurroughs Wellcome Fund (Career Award at the Scientific Interface)National Institutes of Health (U.S.) (PIONEER award)Alfred P. Sloan Foundation (Research Fellowship
Optimizing a global alignment of protein interaction networks
Full text linkMotivation: The global alignment of protein interaction networks is a widely studied problem. It is an important first step in understanding the relationship between the proteins in different species and identifying functional orthologs. Furthermore, it can provide useful insights into the species’ evolution.
Results: We propose a novel algorithm, PISwap, for optimizing global pairwise alignments of protein interaction networks, based on a local optimization heuristic that has previously demonstrated its effectiveness for a variety of other intractable problems. PISwap can begin with different types of network alignment approaches and then iteratively adjust the initial alignments by incorporating network topology information, trading it off for sequence information. In practice, our algorithm efficiently refines other well-studied alignment techniques with almost no additional time cost. We also show the robustness of the algorithm to noise in protein interaction data. In addition, the flexible nature of this algorithm makes it suitable for different applications of network alignment. This algorithm can yield interesting insights into the evolutionary dynamics of related species.
Availability: Our software is freely available for non-commercial purposes from our Web site, http://piswap.csail.mit.edu/.National Institutes of Health (U.S.) (Grant GM081871
Expanding, evaluating and combining tree shape statistics
No full textNon UBCUnreviewedAuthor affiliation: Simon Fraser UniversityFacult
Interpretable machine learning methods for predicting drug resistance
No full textNon UBCUnreviewedAuthor affiliation: Imperial College, LondonResearche
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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