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Missing value
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The paper discusses how the traditional method of association rule mining based on user-defi ned minimum support and confi dence can result in either too many or too few association rules. This could lead to valuable information being missed or redundant rules being generated, which is not practical and can be costly to implement.
7p
vijaychest
24-04-2024
3
2
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Conventional differential expression (DE) testing compares the grouped mean value of tumour samples to the grouped mean value of the normal samples, and may miss out dysregulated genes in small subgroup of patients. This is especially so for highly heterogeneous cancer like Hepatocellular Carcinoma (HCC).
16p
vischultz
20-10-2023
5
1
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In this paper, a model combining recurrent neural networks and convolutional neural networks is proposed to build a model that can simultaneously estimate missing values and classify time series data. The experimental results demonstrate that the proposed model performs better than the existing methods for time series classification with incomplete data.
16p
viengels
25-08-2023
5
5
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Ebook All the mathematics you missed: but need to know for graduate school - Part 1 presents the following chapters: Chapter 1 linear algebra, chapter 2 ϵ and δ real analysis, chapter 3 calculus for vector-valued functions, chapter 4 point set topology, chapter 5 classical Stokes' Theorems, chapter 6 differential forms and Stokes' theorem.
173p
runthenight04
04-01-2023
20
5
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In this paper, we present a new model called Service-based Autoencoder (S-Au-Co). The model is constructed based on an Auto en Coder and the back-propagation algorithm is used for training the model to predict the missing QoS values in the data sets.
4p
vispyker
16-11-2022
14
2
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The experimental results showed that the proposed method functioned efficiently by showcasing the highest accuracy rate as compared to the one without imputations, with imputation by Fuzzy C-Means (FCM), and imputation by Fuzzy C-Means with Majority Vote (FCMMV).
24p
spiritedaway36
28-11-2021
7
1
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Cancer progression reconstruction is an important development stemming from the phylogenetics field. In this context, the reconstruction of the phylogeny representing the evolutionary history presents some peculiar aspects that depend on the technology used to obtain the data to analyze: Single Cell DNA Sequencing data have great specificity, but are affected by moderate false negative and missing value rates.
16p
viwyoming2711
16-12-2020
14
0
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In modern biomedical research of complex diseases, a large number of demographic and clinical variables, herein called phenomic data, are often collected and missing values (MVs) are inevitable in the data collection process. Since many downstream statistical and bioinformatics methods require complete data matrix, imputation is a common and practical solution.
12p
vikentucky2711
26-11-2020
14
0
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Several missing value imputation methods for gene expression data have been proposed in the literature. In the past few years, researchers have been putting a great deal of effort into presenting systematic evaluations of the different imputation algorithms.
9p
vikentucky2711
26-11-2020
47
1
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: Data generated from metabolomics experiments are different from other types of “-omics” data. For example, a common phenomenon in mass spectrometry (MS)-based metabolomics data is that the data matrix frequently contains missing values, which complicates some quantitative analyses.
13p
vikentucky2711
24-11-2020
18
1
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Missing values are commonly present in microarray data profiles. Instead of discarding genes or samples with incomplete expression level, missing values need to be properly imputed for accurate data analysis. The imputation methods can be roughly categorized as expression level-based and domain knowledge-based.
8p
vioklahoma2711
19-11-2020
12
2
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Non-Negative Matrix factorization has become an essential tool for feature extraction in a wide spectrum of applications. In the present work, our objective is to extend the applicability of the method to the case of missing and/or corrupted data due to outliers.
9p
vioklahoma2711
19-11-2020
15
1
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Integrative analysis of multi-omics data is becoming increasingly important to unravel functional mechanisms of complex diseases. However, the currently available multi-omics datasets inevitably suffer from missing values due to technical limitations and various constrains in experiments.
12p
vioklahoma2711
19-11-2020
26
1
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In omics data integration studies, it is common, for a variety of reasons, for some individuals to not be present in all data tables. Missing row values are challenging to deal with because most statistical methods cannot be directly applied to incomplete datasets.
16p
vioklahoma2711
19-11-2020
13
0
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Missing value imputation is important for microarray data analyses because microarray data with missing values would significantly degrade the performance of the downstream analyses. Although many microarray missing value imputation algorithms have been developed, an objective and comprehensive performance comparison framework is still lacking.
10p
vioklahoma2711
19-11-2020
7
0
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High throughput metabolomics makes it possible to measure the relative abundances of numerous metabolites in biological samples, which is useful to many areas of biomedical research. However, missing values (MVs) in metabolomics datasets are common and can arise due to both technical and biological reasons.
13p
vioklahoma2711
19-11-2020
8
0
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Data from discovery proteomic and phosphoproteomic experiments typically include missing values that correspond to proteins that have not been identified in the analyzed sample. Replacing the missing values with random numbers, a process known as “imputation”, avoids apparent infinite fold-change values.
12p
vicolorado2711
23-10-2020
4
0
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High-throughput technologies enable the cost-effective collection and analysis of DNA methylation data throughout the human genome. This naturally entails missing values management that can complicate the analysis of the data.
22p
vicolorado2711
22-10-2020
8
0
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Gene Regulatory Networks (GRNs) have been previously studied by using Boolean/multi-state logics. While the gene expression values are usually scaled into the range [0, 1], these GRN inference methods apply a threshold to discretize the data, resulting in missing information.
21p
vicolorado2711
22-10-2020
6
1
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The Institute of Medicine (IOM) has endorsed six dimensions of patient-centredness as crucial to providing quality healthcare. These dimensions outline that care must be: 1) respectful to patients’ values, preferences, and expressed needs; 2) coordinated and integrated; 3) provide information, communication, and education; 4) ensure physical comfort; 5) provide emotional support—relieving fear and anxiety; and 6) involve family and friends.
32p
vijennie2711
25-09-2020
86
0
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