Missing value estimation for microarray data through cluster analysis

Pati, Soumen Kumar; Das, Asit Kumar

doi:10.1007/s10115-017-1025-5

Cited by 25 publications

(6 citation statements)

References 34 publications

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“…where trðG k Þ denotes the trace of the matrix G, and η = 10 −0 10 −3 is the regularization parameter. Finally, the weights w of KNNs are determined by solving the linear equation in Formula (12). For the kernel version KDF-WKNN, the method for solving the weights w is similar to that of the DF-WKNN described above.…”

Section: Rpca Based Methodmentioning

confidence: 99%

“…For missing values, some studies apply the “case deletion” method directly, i.e., simply removing those instances with missing values and only using the observed instances to establish the classification models, which may lose some information especially for small sample datasets [ 11 ]. To tackle these shortcomings, in the past decades, several missing value imputation methods have been proposed in some fields like DNA microarrays [ 12 – 16 ] and traffic data problems [ 17 , 18 ]. For example, Troyanskaya et al present a prevalent imputation method based on KNN, i.e., KNN impute for DNA microarrays [ 13 ].…”

Section: Problem Statementmentioning

confidence: 99%

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Missing Value Estimation Methods Research for Arrhythmia Classification Using the Modified Kernel Difference-Weighted KNN Algorithms

Yang

Lang

et al. 2020

BioMed Research International

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Electrocardiogram (ECG) signal is critical to the classification of cardiac arrhythmia using some machine learning methods. In practice, the ECG datasets are usually with multiple missing values due to faults or distortion. Unfortunately, many established algorithms for classification require a fully complete matrix as input. Thus it is necessary to impute the missing data to increase the effectiveness of classification for datasets with a few missing values. In this paper, we compare the main methods for estimating the missing values in electrocardiogram data, e.g., the “Zero method”, “Mean method”, “PCA-based method”, and “RPCA-based method” and then propose a novel KNN-based classification algorithm, i.e., a modified kernel Difference-Weighted KNN classifier (MKDF-WKNN), which is fit for the classification of imbalance datasets. The experimental results on the UCI database indicate that the “RPCA-based method” can successfully handle missing values in arrhythmia dataset no matter how many values in it are missing and our proposed classification algorithm, MKDF-WKNN, is superior to other state-of-the-art algorithms like KNN, DS-WKNN, DF-WKNN, and KDF-WKNN for uneven datasets which impacts the accuracy of classification.

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Section: Rpca Based Methodmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Missing Value Estimation Methods Research for Arrhythmia Classification Using the Modified Kernel Difference-Weighted KNN Algorithms

Yang

Lang

et al. 2020

BioMed Research International

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“…It yields distinct groups (or clusters) that contain objects that are similar based on some measurement. Cluster analysis has been applied in several domains such as in market segmentation (Tkaczynski, 2017), genetic analysis (Pati and Das, 2017), health-care analysis (Liao et al, 2016), sports analysis (Coughlan et al, 2019) and anomaly detection (Ghezelbash et al, 2020) An in-depth discussion on these types is provided by Han et al (2011) and Barbakh et al (2009). For brevity, we will focus our attention on type (i), in particular, K-means clustering.…”

Section: K-means Clustering Algorithmmentioning

confidence: 99%

A novel hybrid DEMATEL-K-means clustering algorithm for modeling the barriers of green computing adoption in the Philippines

Abellana

Mayol

2021

JM2

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Purpose This paper aims to propose a novel hybrid-decision-making trial and evaluation laboratory-K means clustering algorithm as a decision-making framework for analyzing the barriers of green computing adoption. Design/methodology/approach A literature review is conducted to extract relevant green computing barriers. An expert elicitation process is performed to finalize the barriers and to establish their corresponding interrelationships. Findings The proposed approach offers a comprehensive framework for modeling the barriers of green computing adoption. Research limitations/implications The results of this paper provide insights on how the barriers of green computing adoption facilitate the adoption of stakeholders. Moreover, the paper provides a framework for analyzing the structural relationships that exist between factors in a tractable manner. Originality/value The paper is one of the very first attempts to analyze the barriers of green computing adoption. Furthermore, it is the first to offer lenses in a Philippine perspective. The paper offers a novel algorithm that can be useful in modeling the barriers of innovation, particularly, in green computing adoption.

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“…Unlike global learning-based methods, local learningbased methods, rather than rely on a covariate structure, utilize the similar genes of a target gene to estimate missing values [5,18,25], where how to identify neighbors of the target genes and further establish their relationships largely determines the imputation results of an algorithm. There are studies that divide genes into multiple clusters and use the within-group genes of the target gene to estimate missing values.…”

Section: Related Workmentioning

confidence: 99%

Regularized Sparse Modelling for Microarray Missing Value Estimation

Wang

Yang

2021

IEEE Access

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The existence of missing values in microarray data inevitably hinders downstream biological analyses that expect complete data as input, therefore how to effectively explore the underlying structure of data to accurately estimate missing entries remains crucial and meaningful. In this study, we formalize the problem under a regularized sparse framework and accordingly propose local learning-based imputation models to capture the relationships that are hidden in gene expression profiles towards better imputation. Specifically, in view of the simultaneous variable selection and grouping effect of the elastic net penalty, we present an elastic net regularized local least squares-based imputation method to estimate the missing entries of a target gene with its neighbors. Besides, we investigate different similarity filtering metrics to select neighbor genes and develop another four imputation methods under the framework. Furthermore, the proposed methods process the target genes in ascending order of their associated missing rates. Finally, extensive comparative experiments against other eight commonly-used methods are conducted on multiple microarray datasets having varying missing rates. Results indicate the power of sparse regularization techniques and the superiority of elastic net over its competitors in terms of statistical analysis metrics.

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Missing value estimation for microarray data through cluster analysis

Cited by 25 publications

References 34 publications

Missing Value Estimation Methods Research for Arrhythmia Classification Using the Modified Kernel Difference-Weighted KNN Algorithms

Missing Value Estimation Methods Research for Arrhythmia Classification Using the Modified Kernel Difference-Weighted KNN Algorithms

A novel hybrid DEMATEL-K-means clustering algorithm for modeling the barriers of green computing adoption in the Philippines

Regularized Sparse Modelling for Microarray Missing Value Estimation

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