PRIME: Block-Wise Missingness Handling for Multi-modalities in Intelligent Tutoring Systems

Yang, Xi; Kim, Yeo-Jin; Taub, Michelle; Azevedo, Roger; Chi, Min

doi:10.1007/978-3-030-37734-2_6

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…estimate a factor model that is then used to impute missing values. Yang et al (2020) first learn the pairwise mappings between each data type A and the remaining data types using autoencoders. Second, these pairwise mappings are regularised to be consistent with each other, each pairwise mapping is applied to A imputing the missing values, and finally the imputations obtained using the different pairwise mappings are averaged.…”

Section: Imputation-based Methodsmentioning

confidence: 99%

Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study

Hornung¹,

Ludwigs²,

Hagenberg³

et al. 2023

Preprint

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As the availability of omics data has increased in the last few years, more multi-omics data have been generated, that is, high-dimensional molecular data consisting of several types such as genomic, transcriptomic, or proteomic data, all obtained from the same patients. Such data lend themselves to being used as covariates in automatic outcome prediction because each omics type may contribute unique information, possibly improving predictions compared to using only one omics data type. Frequently, however, in the training data and the data to which automatic prediction rules should be applied, the test data, the different omics data types are not available for all patients. We refer to this type of data as block-wise missing multi-omics data. First, we provide a literature review on existing prediction methods applicable to such data. Subsequently, using a collection of 13 publicly available multi-omics data sets, we compare the predictive performances of several of these approaches for different block-wise missingness patterns. Finally, we discuss the results of this empirical comparison study and draw some tentative conclusions.

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Section: Imputation-based Methodsmentioning

confidence: 99%

Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study

Hornung¹,

Ludwigs²,

Hagenberg³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Zhang et al (2020) estimate a factor model that is then used to impute missing values. Yang et al (2020) first learn the pairwise mappings between each data type

A

and the remaining data types using autoencoders. Second, these pairwise mappings are regularized to be consistent with each other, each pairwise mapping is applied to

A

imputing the missing values, and finally the imputations obtained using the different pairwise mappings are averaged.…”

Section: Existing Prediction Approaches For Partly Missing Multi‐omic...mentioning

confidence: 99%

Prediction approaches for partly missing multi‐omics covariate data: A literature review and an empirical comparison study

Hornung

Ludwigs

Hagenberg

et al. 2023

WIREs Computational Stats

View full text Add to dashboard Cite

As the availability of omics data has increased in the last few years, more multi‐omics data have been generated, that is, high‐dimensional molecular data consisting of several types such as genomic, transcriptomic, or proteomic data, all obtained from the same patients. Such data lend themselves to being used as covariates in automatic outcome prediction because each omics type may contribute unique information, possibly improving predictions compared to using only one omics data type. Frequently, however, in the training data and the data to which automatic prediction rules should be applied, the test data, the different omics data types are not available for all patients. We refer to this type of data as block‐wise missing multi‐omics data. First, we provide a literature review on existing prediction methods applicable to such data. Subsequently, using a collection of 13 publicly available multi‐omics data sets, we compare the predictive performances of several of these approaches for different block‐wise missingness patterns. Finally, we discuss the results of this empirical comparison study and draw some tentative conclusions.This article is categorized under: Applications of Computational Statistics > Genomics/Proteomics/Genetics Applications of Computational Statistics > Health and Medical Data/Informatics Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data

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Classroom Attention Estimation Method Based on Mining Facial Landmarks of Students

Chen

Yang

Liu

2022

MultiMedia Modeling

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PRIME: Block-Wise Missingness Handling for Multi-modalities in Intelligent Tutoring Systems

Cited by 5 publications

References 17 publications

Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study

Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study

Prediction approaches for partly missing multi‐omics covariate data: A literature review and an empirical comparison study

Classroom Attention Estimation Method Based on Mining Facial Landmarks of Students

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