Results 1 to 10 of about 1,218,599 (117)

Optimizing imputation strategies for mass spectrometry-based proteomics considering intensity and missing value rates [PDF]

Computational and Structural Biotechnology Journal
Missing values (MVs) in omic datasets affect the power, accuracy, and consistency of statistical and functional analyses. In mass spectrometry (MS)-based proteomics, MVs can arise due to several reasons: peptides could be below instrumental detection ...
Yuming Shi, Huan Zhong, Jason C. Rogalski, Leonard J. Foster   +3 more
doaj   +2 more sources

Missing value imputation in a data matrix using the regularised singular value decomposition [PDF]

MethodsX, 2023
Some statistical analysis techniques may require complete data matrices, but a frequent problem in the construction of databases is the incomplete collection of information for different reasons. One option to tackle the problem is to estimate and impute
Sergio Arciniegas-Alarcón, Marisol García-Peña, Wojtek J. Krzanowski, Camilo Rengifo   +3 more
doaj   +2 more sources

Logging Data Completion Based on an MC-GAN-BiLSTM Model

IEEE Access, 2022
Due to environmental interference and operational errors, problems such as incomplete and random missing logging data have occurred during the geophysical logging data collection process.
Liang Guo, Luo Renze, Li Xingyu, Tuo Juanjuan, Canru Lei, Zhou Yang   +5 more
doaj   +1 more source

K Nearest Neighbor Imputation Performance on Missing Value Data Graduate User Satisfaction

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2022
A missing value is a common problem of most data processing in scientific research, which results in a lack of accuracy of research results. Several methods have been applied as a missing value solution, such as deleting all data that have a missing ...
Abdul Fadlil, Herman, Dikky Praseptian M
doaj   +1 more source

Tobacco smoking and depressive symptoms in Chinese middle-aged and older adults: Handling missing values in panel data with multiple imputation

Frontiers in Public Health, 2022
IntroductionThe high co-occurrence of tobacco smoking and depression is a major public health concern during the novel coronavirus disease-2019 pandemic. However, no studies have dealt with missing values when assessing depression. Therefore, the present
Xiahua Du, Rina Wu, Lili Kang, Longlong Zhao, Changle Li   +4 more
doaj   +1 more source

ST-CRMF: Compensated Residual Matrix Factorization with Spatial-Temporal Regularization for Graph-Based Time Series Forecasting

Sensors, 2022
Despite the extensive efforts, accurate traffic time series forecasting remains challenging. By taking into account the non-linear nature of traffic in-depth, we propose a novel ST-CRMF model consisting of the Compensated Residual Matrix Factorization ...
Jinlong Li, Pan Wu, Ruonan Li, Yuzhuang Pian, Zilin Huang, Lunhui Xu, Xiaochen Li   +6 more
doaj   +1 more source

SurvNet: A Novel Deep Neural Network for Lung Cancer Survival Analysis With Missing Values

Frontiers in Oncology, 2021
Survival analysis is important for guiding further treatment and improving lung cancer prognosis. It is a challenging task because of the poor distinguishability of features and the missing values in practice.
Jianyong Wang, Nan Chen, Jixiang Guo, Xiuyuan Xu, Lunxu Liu, Zhang Yi   +5 more
doaj   +1 more source

MULTIPLE IMPUTATION FOR ORDINARY COUNT DATA BY NORMAL DISTRIBUTION APPROXIMATION

Media Statistika, 2021
Missing values are a problem that is often encountered in various fields and must be addressed to obtain good statistical inference such as parameter estimation.
Titin Siswantining, Muhammad Ihsan, Saskya Mary Soemartojo, Devvi Sarwinda, Herley Shaori Al-Ash, Ika Marta Sari   +5 more
doaj   +1 more source

Improving accuracy of missing data imputation in data mining

Kurdistan Journal of Applied Research, 2017
In fact, raw data in the real world is dirty. Each large data repository contains various types of anomalous values that influence the result of the analysis, since in data mining, good models usually need good data, databases in the world are not always
Nzar A. Ali, Zhyan M. Omer
doaj   +1 more source

Recurrent Neural Network-Based Multimodal Deep Learning for Estimating Missing Values in Healthcare

Applied Sciences, 2022
This estimation method operates by integrating the input values that are redundantly collected from heterogeneous devices through the selection of a representative value and estimating missing values by using a multimodal RNN.
Joo-Chang Kim, Kyungyong Chung
doaj   +1 more source