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   Articles with "missing values" as a keyword



Dealing with missing values in proteomics data

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recommendations! 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

Imputation of missing values in lipidomic datasets

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recommendations! Published in 2024 at "PROTEOMICS"

DOI: 10.1002/pmic.202300606

Abstract: Lipidomic data often exhibit missing data points, which can be categorized as missing completely at random (MCAR), missing at random, or missing not at random (MNAR). In order to utilize statistical methods that require complete… read more here.

Keywords: random; imputation missing; lipidomic datasets; imputation ... See more keywords

Computational Methods for Data Integration and Imputation of Missing Values in Omics Datasets

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recommendations! Published in 2024 at "PROTEOMICS"

DOI: 10.1002/pmic.202400100

Abstract: Molecular profiling of different omic‐modalities (e.g., DNA methylomics, transcriptomics, proteomics) in biological systems represents the basis for research and clinical decision‐making. Measurement‐specific biases, so‐called batch effects, often hinder the integration of independently acquired datasets, and… read more here.

Keywords: integration; methods data; computational methods; imputation ... See more keywords
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Missing Value Monitoring to Address Missing Values in Quantitative Proteomics.

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recommendations! 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

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recommendations! 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

Ratai: recurrent autoencoder with imputation units and temporal attention for multivariate time series imputation

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recommendations! Published in 2024 at "Artificial Intelligence Review"

DOI: 10.1007/s10462-024-11039-z

Abstract: Multivariate time series is ubiquitous in real-world applications, yet it often suffers from missing values that impede downstream analytical tasks. In this paper, we introduce the Long Short-Term Memory Network based Recurrent Autoencoder with Imputation… read more here.

Keywords: multivariate time; time; imputation; time series ... See more keywords

The effect of simple imputations based on four variants of PCA methods on the quantiles of annual rainfall data

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recommendations! 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

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recommendations! 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

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recommendations! 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.

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recommendations! 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

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recommendations! 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