Dealing with missing values in proteomics data
Sign Up to like & getrecommendations! Published in 2022 at "PROTEOMICS"
DOI: 10.1002/pmic.202200092
Abstract: Proteomics data are often plagued with missingness issues. These missing values (MVs) threaten the integrity of subsequent statistical analyses by reduction of statistical power, introduction of bias, and failure to represent the true sample. Over… read more here.
Keywords: proteomics data; dealing missing; mvi; missing values ... See more keywords
Missing Value Monitoring to Address Missing Values in Quantitative Proteomics.
Sign Up to like & getrecommendations! Published in 2021 at "Methods in molecular biology"
DOI: 10.1007/978-1-0716-1024-4_27
Abstract: Many classes of key functional proteins such as transcription factors or cell cycle proteins are present in the proteome at a very low concentration. These low-abundance proteins are almost entirely invisible to systematic quantitative analysis… read more here.
Keywords: missing values; value monitoring; monitoring address; missing value ... See more keywords
Estimation of incomplete values in heterogeneous attribute large datasets using discretized Bayesian max–min ant colony optimization
Sign Up to like & getrecommendations! Published in 2017 at "Knowledge and Information Systems"
DOI: 10.1007/s10115-017-1123-4
Abstract: The size of datasets is becoming larger nowadays and missing values in such datasets pose serious threat to data analysts. Although various techniques have been developed by researchers to handle missing values in different kinds… read more here.
Keywords: missing values; methodology; max min; min ant ... See more keywords
The effect of simple imputations based on four variants of PCA methods on the quantiles of annual rainfall data
Sign Up to like & getrecommendations! Published in 2018 at "Environmental Monitoring and Assessment"
DOI: 10.1007/s10661-018-6913-y
Abstract: Hydrology-related studies often require complete datasets. However, missing data is an unavoidable reality. In this regard, the imputed data could fulfill the same role as the observed ones, while they are uncertain and just estimated.… read more here.
Keywords: annual rainfall; missing values; quantiles annual; rainfall data ... See more keywords
Hollow-tree: a metric access method for data with missing values
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Intelligent Information Systems"
DOI: 10.1007/s10844-019-00567-8
Abstract: Similarity search is fundamental to store and retrieve large volumes of complex data required by many real world applications. A useful mechanism for such concept is the query-by-similarity. Based on their topological properties, metric similarity… read more here.
Keywords: missing values; metric access; access method; hollow tree ... See more keywords
Mixture of multivariate t nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values
Sign Up to like & getrecommendations! Published in 2019 at "TEST"
DOI: 10.1007/s11749-018-0612-4
Abstract: The multivariate t nonlinear mixed-effects model (MtNLMM) has been shown to be effective for analyzing multi-outcome longitudinal data following nonlinear growth patterns with fat-tailed noises or potential outliers. This paper considers the problem of clustering… read more here.
Keywords: longitudinal data; missing values; mixture multivariate; multivariate nonlinear ... See more keywords
Cardio-ML: Detection of malicious clinical programmings aimed at cardiac implantable electronic devices based on machine learning and a missing values resemblance framework.
Sign Up to like & getrecommendations! Published in 2021 at "Artificial intelligence in medicine"
DOI: 10.1016/j.artmed.2021.102200
Abstract: Patients with life-threatening arrhythmias are often treated with cardiac implantable electronic devices (CIEDs), such as pacemakers and implantable cardioverter defibrillators (ICDs). Recent advancements in CIEDs have enabled advanced functionality and connectivity that make such devices… read more here.
Keywords: missing values; detection; resemblance framework; malicious clinical ... See more keywords
A statistical emulator for multivariate model outputs with missing values
Sign Up to like & getrecommendations! Published in 2019 at "Atmospheric Environment"
DOI: 10.1016/j.atmosenv.2018.11.025
Abstract: Abstract Statistical emulators are used to approximate the output of complex physical models when their computational burden limits any sensitivity and uncertainty analysis of model output to variation in the model inputs. In this paper,… read more here.
Keywords: emulator; multivariate model; missing values; outputs missing ... See more keywords
A deep learning-based, unsupervised method to impute missing values in electronic health records for improved patient management
Sign Up to like & getrecommendations! Published in 2020 at "Journal of biomedical informatics"
DOI: 10.1016/j.jbi.2020.103576
Abstract: Electronic health records (EHRs) often suffer missing values, for which recent advances in deep learning offer a promising remedy. We develop a deep learning-based, unsupervised method to impute missing values in patient records, then examine… read more here.
Keywords: missing values; imputation; patient management; deep learning ... See more keywords
Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data
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DOI: 10.1038/s41598-017-19120-0
Abstract: Missing values exist widely in mass-spectrometry (MS) based metabolomics data. Various methods have been applied for handling missing values, but the selection can significantly affect following data analyses. Typically, there are three types of missing… read more here.
Keywords: missing values; imputation; mass spectrometry; value ... See more keywords
Estimating missing values in China’s official socioeconomic statistics using progressive spatiotemporal Bayesian hierarchical modeling
Sign Up to like & getrecommendations! Published in 2018 at "Scientific Reports"
DOI: 10.1038/s41598-018-28322-z
Abstract: Due to a large number of missing values, both spatially and temporally, China has not published a complete official socioeconomic statistics dataset at the county level, which is the country’s basic scale of official statistics… read more here.
Keywords: missing values; official socioeconomic; hierarchical modeling; bayesian hierarchical ... See more keywords