Multiple Imputation by Generative Adversarial Networks for Classification with Incomplete Data

Ngoc, Bao; Nguyen, Dinh Tam; Tran, Cao Truong; Ngo, Huu Phuc; Nguyễn, Chí Công; Phan, Hai-Hong

doi:10.1109/rivf51545.2021.9642138

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Vi et al. [50] proposed an integrated GAN model (MIGAN) to estimate missing values for classification. The framework of MIGAN consisted of a GAN and an ensemble classifier.…”

Section: Methodsmentioning

confidence: 99%

Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective

Smith

Feltus

2023

Comp Sys Onco

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The remarkable flexibility and adaptability of generative adversarial networks (GANs) have led to the proliferation of its models in bioinformatics research. Proteomic and transcriptomic profiles have been shown to be promising methods for discovering and identifying disease biomarkers. However, those analyses were performed by trained human examiners making the process tedious, time consuming, and hard to standardize. With the development of GANs, it is now possible to reduce computational costs and human time for bioinformatics analysis to produce effective biomarkers. Moreover, GANs help address the lack of phenotypic state transitional gene expression data as well as avoid protected human data constraints by generating RNA sequencing (RNA‐seq) data from random vectors. The purpose of this review is to summarize the use of GAN approaches and techniques to augment RNA‐seq expression data and identify clinically useful biomarkers. We compare different studies that use different types of GAN models to examine the biomarkers. Also, we identify research gaps and challenges that apply GANs to bio‐informatics. Finally, we propose potential directions for future research.

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“…Vi et al. [50] proposed an integrated GAN model (MIGAN) to estimate missing values for classification. The framework of MIGAN consisted of a GAN and an ensemble classifier.…”

Section: Methodsmentioning

confidence: 99%

Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective

Smith

Feltus

2023

Comp Sys Onco

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A Comprehensive Bibliometric Analysis of Missing Value Imputation

Nugroho,

Surendro

2024

IEEE Access

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Data quality plays a crucial role in tasks, such as enhancing the accuracy of data analytics and avoiding the accumulation of redundant data. One of the significant challenges in data quality is dealing with missing data, which has been extensively explored by the scholarly community and has resulted in a significant increase in related publications. It is important to recognize that the landscape of missing data in computer science offers numerous opportunities for further research. However, upon closer examination of existing studies, it becomes evident that many have not fully utilized bibliometric analysis tools and software for comprehensive literature reviews. Therefore, this study aims to explore the essential characteristics, trends, and prevailing themes in the field of missing data imputation. Through a thorough bibliometric analysis, this study demonstrated the evolution of knowledge and key focal points in the field of missing data imputation. The analysis consisted of 352 journal papers in social sciences published between 2012 and 2023, all centered on missing data imputation. Among these publications, "IEEE Access" has become a highly respected source. To systematically explore various aspects of missing data imputation, a conceptual framework was used to uncover potential research directions and underlying themes. Ultimately, a thematic map serves as a valuable tool for providing a comprehensive understanding, categorizing significant concepts into basic or overarching, developing, or declining, central, highly developed, and isolated themes. These overarching and underlying themes offer valuable insights and pave the way for prospective directions and critical areas of study.

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Forecasting a Journal Impact Factor Under Missing Values Based on Machine Learning

Hua,

Huynh

2024

IEEE Access

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Multiple Imputation by Generative Adversarial Networks for Classification with Incomplete Data

Cited by 4 publications

References 11 publications

Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective

Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective

A Comprehensive Bibliometric Analysis of Missing Value Imputation

Forecasting a Journal Impact Factor Under Missing Values Based on Machine Learning

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