Improved KNN Imputation for Missing Values in Gene Expression Data

DNA microarray technology produces gene expression matrix that consists of an inexorably missing entries due to poor experimental procedures. The missing values are predicted in the matrix for gene expression data are considered to be essential, since most algorithms analyse the gene expression that usually needs a matrix without missing values. In order to address this issue, the present study biclustering Genetic based Simulated Annealing (Genetic SA) algorithm to predict the items that are missing in the gene expression data. The present study uses biclustering method that is considered to be essential for clustering the gene expression data. The performance evaluation shows that the proposed Genetic SA for gene data expression predicts the missing items in an accurate manner than the existing methods.

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Maximizing the Reusability of Public Gene Expression Data by Predicting Missing Metadata

10.1101/792382 ◽

2019 ◽

Author(s):

Pei-Yau Lung ◽

Xiaodong Pang ◽

Yan Li ◽

Jinfeng Zhang

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Gene Expression Data ◽

Missing Values ◽

Expression Data ◽

New Approach ◽

Machine Learning Methods ◽

Differential Gene ◽

Missing Variables ◽

Better Than

AbstractReusability is part of the FAIR data principle, which aims to make data Findable, Accessible, Interoperable, and Reusable. One of the current efforts to increase the reusability of public genomics data has been to focus on the inclusion of quality metadata associated with the data. When necessary metadata are missing, most researchers will consider the data useless. In this study, we develop a framework to predict the missing metadata of gene expression datasets to maximize their reusability. We propose a new metric called Proportion of Cases Accurately Predicted (PCAP), which is optimized in our specifically-designed machine learning pipeline. The new approach performed better than pipelines using commonly used metrics such as F1-score in terms of maximizing the reusability of data with missing values. We also found that different variables might need to be predicted using different machine learning methods and/or different data processing protocols. Using differential gene expression analysis as an example, we show that when missing variables are accurately predicted, the corresponding gene expression data can be reliably used in downstream analyses.

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Microarray missing values imputation methods: Critical analysis review

Computer Science and Information Systems ◽

10.2298/csis0902165h ◽

2009 ◽

Vol 6 (2) ◽

pp. 165-190 ◽

Cited By ~ 6

Author(s):

Mou'ath Hourani ◽

Emary El

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Missing Values ◽

Gene Array ◽

Estimation Method ◽

Least Square ◽

Support Vector ◽

Expression Data ◽

Imputation Methods ◽

Value Estimation

Gene expression data often contain missing expression values. For the purpose of conducting an effective clustering analysis and since many algorithms for gene expression data analysis require a complete matrix of gene array values, choosing the most effective missing value estimation method is necessary. In this paper, the most commonly used imputation methods from literature are critically reviewed and analyzed to explain the proper use, weakness and point the observations on each published method. From the conducted analysis, we conclude that the Local Least Square (LLS) and Support Vector Regression (SVR) algorithms have achieved the best performances. SVR can be considered as a complement algorithm for LLS especially when applied to noisy data. However, both algorithms suffer from some deficiencies presented in choosing the value of Number of Selected Genes (K) and the appropriate kernel function. To overcome these drawbacks, the need for new method that automatically chooses the parameters of the function and it also has an appropriate computational complexity is imperative.

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A Bicluster-Based Sequential Interpolation Imputation Method for Estimation of Missing Values in Microarray Gene Expression Data

Current Bioinformatics ◽

10.2174/1574893612666170106102019 ◽

2017 ◽

Vol 12 (2) ◽

pp. 118-130 ◽

Cited By ~ 2

Author(s):

Alamgir Hossain ◽

Matangini Chattopadhyay ◽

Samiran Chattopadhyay ◽

Shilpi Bose ◽

Chandra Das

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Missing Values ◽

Microarray Gene Expression Data ◽

Imputation Method ◽

Expression Data ◽

Microarray Gene Expression ◽

Microarray Gene

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A Joint Optimization Framework Integrated with Biological Knowledge for Clustering Incomplete Gene Expression Data

10.21203/rs.3.rs-1087790/v1 ◽

2021 ◽

Author(s):

Dan Li ◽

Hong Gu ◽

Qiaozhen Chang ◽

Jia Wang ◽

Pan Qin

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Missing Values ◽

Clustering Algorithms ◽

Joint Optimization ◽

Gene Clustering ◽

Biological Knowledge ◽

Data Sets ◽

Expression Data ◽

Optimization Framework

Abstract Clustering algorithms have been successfully applied to identify co-expressed gene groups from gene expression data. Missing values often occur in gene expression data, which presents a challenge for gene clustering. When partitioning incomplete gene expression data into co-expressed gene groups, missing value imputation and clustering are generally performed as two separate processes. These two-stage methods are likely to result in unsuitable imputation values for clustering task and unsatisfying clustering performance. This paper proposes a multi-objective joint optimization framework for clustering incomplete gene expression data that addresses this problem. The proposed framework can impute the missing expression values under the guidance of clustering, and therefore realize the synergistic improvement of imputation and clustering. In addition, gene expression similarity and gene semantic similarity extracted from the Gene Ontology are combined, as the form of functional neighbor interval for each missing expression value, to provide reasonable constraints for the joint optimization framework. Experiments on several benchmark data sets confirm the effectiveness of the proposed framework.

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