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Predicting Protein Corona Formation on Polylactic Acid Microplastics Pre- and Post-Photoaging: The Importance of Optimal imputation methods

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ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS 2025年第4期12卷 413-418页

作者： Wu, Xuri Huang, Liping Zhou, Lina Fang, Yan Tan, Feng Dalian Univ Technol Sch Environm Sci & Technol Key Lab Ind Ecol & Environm Engn MOE Dalian 116024 Peoples R China Dalian Univ Technol Instrumental Anal Ctr Dalian 116024 Peoples R China Dalian Univ Technol Frontier Sci Ctr Smart Mat Sch Chem Engn State Key Lab Fine Chem Dalian 116024 Peoples R China

Micro-nanoplastics (MNPs) enter biological systems, forming a protein corona (PC) by adsorbing proteins from bodily fluids, influencing their biological effects. Mass spectrometry-based proteomics characterizes PC composition, and recent advances have leveraged protein amino acid sequence-derived features to predict PC formation using a supervised random forest (RF) classifier. However, mass spectrometry often generates substantial missing values (MVs), which may hinder the model's predictive performance and the understanding of protein-particle interactions. This study assessed the impact of 20 imputation methods on RF classifier performance in predicting human plasma PC formation on polylactic acid (PLA) and photoaged PLA microplastics (MPs), considering their rising ecological and health concerns. The results showed that five left-censored imputation methods (Zero, Half-min, Min, QRILC, GSimp) achieved the best performance, with high accuracy (0.80-0.82), AUC (0.78-0.84), precision (0.78-0.80), and recall (0.97-0.98). Protein spatial features, including secondary sheet structure (negative) and absolute solvent-accessible area (positive), were identified as key factors influencing protein adsorption onto MPs. Additionally, UV aging increased the importance ranking of features frac_aa_S and fraction_exposed_exposed_S, highlighting altered protein-MPs interactions, likely through hydrogen bonding and electrostatic forces. This study demonstrates the potential of left-censored imputation methods in enhancing RF classifier performance for predicting PC formation.

关键词： Protein corona Microplastics Missing values imputation methods Machine learning

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Enhanced imputation methods Using Bivariate Auxiliary Information in Stratified Sampling: An application on Road Accident Data

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ALEXANDRIA ENGINEERING JOURNAL 2025年 121卷 492-503页

作者： Danish, Faizan Triveni, G. R. V. Lone, Umer Mushtaq VIT AP Univ Sch Adv Sci Dept Math Amaravati 522237 India VIT AP Univ VIT AP Sch Business VSB Amravati 522237 Andhra Prades India

Non-response is a substantial obstacle to data quality and estimation precision in survey sampling, requiring appropriate ways to mitigate this problem. However, studies on imputation techniques utilizing bivariate auxiliary information within the framework of stratified simple random sampling (StSRS) are limited. We present novel combined imputation techniques that leverage bivariate auxiliary information through three strategies in StSRS to enhance population mean estimation. We formulate mean squared error (MSE) equations for both existing and novel methodologies and assess their efficacy via empirical and simulation analyses. Utilizing factory production and road accident data, we illustrate that the suggested estimators consistently attain reduced MSE and enhanced relative efficiency in comparison to existing methodologies. A simulation of 20,000 iterations further validates how well they perform across diverse non-response rates. These findings underscore the capability of dual auxiliary information to improve population parameter estimation, representing a notable development in survey sampling methodologies. Mathematical classification: 62D05, 62D10.

关键词： Non-response imputation methods Bivariate auxiliary information and Mean squared error

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Improved imputation methods for missing data in two-occasion successive sampling

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COMMUNICATIONS IN STATISTICS-THEORY AND methods 2023年第6期52卷 2010-2029页

作者： Singh, Garib Nath Jaiswal, Ashok Kumar Pandey, Awadhesh K. Indian Inst Technol ISM Dept Math & Comp Dhanbad Bihar India

Missing data often complicates survey practitioners to construct reliable estimates of the required population parameters. Remembering this fact and motivated with the recent work, this article deals with some imputation methods to handle the missing data problem at the beginning of the analysis and some estimation procedures of the current population mean have been proposed in two-occasion successive sampling. The expressions for the bias, mean squared error and optimum mean squared error are derived using the concept of large sample approximations. The empirical performances are shown over the similar type of estimators designated for the complete response situation and over recently developed estimator which are defined for the situation when missing observations are observed in the sample data. Suitable recommendations have been made for survey researchers.

关键词： Missing data imputation methods two-occasions successive sampling mean squared error optimum replacement policy efficiency

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Some imputation methods to deal with the issue of missing data problems due to random non-response in two-occasion successive sampling

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2022年第12期51卷 7266-7286页

作者： Khalid, Mohd Singh, Garib Nath Indian Inst Technol ISM Dept Math & Comp Dhanbad Bihar India AMU Stat Sect Dept Stat & Operat Res Womens Coll Aligarh Uttar Pradesh India

Missingness or incompleteness of data is a challenge to the survey statisticians in producing the reliable estimates of the desired population parameters. If the missingness pattern is unidentifiable, this is the case of random non-response and to deal with such situations, this work proposes some alternative imputations methods to cope with the missing data. The proposed imputation methods result in some efficient estimation procedures of the current population mean in two-occasion successive sampling. The properties of the resultant estimation procedures have been examined and supplemented with empirical studies. Results have been critically analyzed, and recommendations are made to the survey practitioners.

关键词： Auxiliary variable Bias imputation methods Mean square error Random non-response Successive sampling

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Some imputation methods to compensate with non-response for estimation of population mean in two-occasion successive sampling

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COMMUNICATIONS IN STATISTICS-THEORY AND methods 2020年第14期49卷 3329-3351页

作者： Singh, Garib Nath Khalid, Mohd Indian Inst Technol ISM Dept Appl Math Dhanbad 826004 Bihar India

Non-response is an unavoidable phenomenon in almost all sample survey, and if it is not dealt carefully at the very starting of the analysis, results may lead to significant biases in the survey estimates. Keeping these facts in mind and inspired by the work of Singh (2009), this article suggests some alternative efficient methods to deal with the missing data problems at the beginning of the analysis and proposes estimation procedures of the current population mean in two-occasion successive sampling. The properties of the resultant estimation procedures have been examined and supplemented with empirical studies. Results have been critically analyzed, and recommendations are made to the survey practitioners.

关键词： imputation methods successive sampling auxiliary variable bias mean square error optimum replacement policy

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A study on the use of imputation methods for experimentation with Radial Basis Function Network classifiers handling missing attribute values: The good synergy between RBFNs and Event Covering method

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NEURAL NETWORKS 2010年第3期23卷 406-418页

作者： Luengo, Julian Garcia, Salvador Herrera, Francisco Univ Granada CITIC Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Univ Jaen Dept Comp Sci Jaen 23071 Spain

The presence of Missing Values in a data set can affect the performance of a classifier constructed using that data set as a training sample. Several methods have been proposed to treat missing data and the one used more frequently is the imputation of the Missing Values of an instance. In this paper, we analyze the improvement of performance on Radial Basis Function Networks by means of the use of several imputation methods in the classification task with missing values. The study has been conducted using data sets with real Missing Values, and data sets with artificial Missing Values. The results obtained show that EventCovering offers a very good synergy with Radial Basis Function Networks. It allows us to overcome the negative impact of the presence of Missing Values to a certain degree. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Classification imputation methods Missing values Radial Basis Function Networks

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An extensive analysis of the interaction between missing data types, imputation methods, and supervised classifiers

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 89卷 52-65页

作者： Garciarena, Unai Santana, Roberto Univ Basque Country Fac Informat Paseo Manuel Lardizabal 1 Donostia San Sebastian 20018 Gipuzkoa Spain

When applying data-mining techniques to real-world data, we often find ourselves facing observations that have no value recorded for some attributes. This can be caused by several phenomena, such as a machine's incapability to record certain characteristics or a person refusing to answer a question in a poll. Depending on that motivation, values gone missing may follow one kind of pattern or another, or describe no regularity at all. One approach to palliate the effect of missing data on machine learning tasks is to replace the missing observations. imputation algorithms attempt to calculate a value for a missing gap, using information associated with it, i.e., the attribute and/or other values in the same observation. While several imputation methods have been proposed in the literature, few works have addressed the question of the relationship between the type of missing data, the choice of the imputation method, and the effectiveness of classification algorithms that used the imputed data. In this paper we address the relationship among these three factors. By constructing a benchmark of hundreds of databases containing different types of missing data, and applying several imputation methods and classification algorithms, we empirically show that an interaction between imputation methods and supervised classification can be deduced. Besides, differences in terms of classification performance for the same imputation method in different missing data patterns have been found. This points to the convenience of considering the combined choice of the imputation method and the classifier algorithm according to the missing data type. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Missing data imputation methods Supervised classifiers Machine learning

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Adaptive pairing of classifier and imputation methods based on the characteristics of missing values in data sets

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 46卷 485-493页

作者： Sim, Jaemun Kwon, Ohbyung Lee, Kun Chang Sungkyunkwan Univ SKKU Business Sch Seoul 110745 South Korea Kyung Hee Univ Sch Management Seoul 130701 South Korea

Classifiers and imputation methods have played crucial parts in the field of big data analytics. Especially, when using data sets characterized by horizontal scattering, vertical scattering, level of spread, compound metric, imbalance ratio and missing ratio, how to combine those classifiers and imputation methods will lead to significantly different performance. Therefore, it is essential that the characteristics of data sets must be identified in advance to facilitate selection of the optimal combination of imputation methods and classifiers. However, this is a very costly process. The purpose of this paper is to propose a novel method of automatic, adaptive selection of the optimal combination of classifier and imputation method on the basis of features of a given data set. The proposed method turned out to successfully demonstrate the superiority in performance evaluations with multiple data sets. The decision makers in big data analytics could greatly benefit from the proposed method when it comes to dealing with data set in which the distribution of missing data varies in real time. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Classification algorithms imputation methods Case-based reasoning Experiments

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Performance of standard imputation methods for missing quality of life data as covariate in survival analysis based on simulations from the International Breast Cancer Study Group Trials VI and VII*

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2019年第10期48卷 3063-3077页

作者： Procter, Marion Robertson, Chris Frontier Sci Scotland Ltd Kincraig PH21 1NA Kingussie Scotland Univ Strathclyde Dept Math & Stat Glasgow Lanark Scotland

imputation methods for missing data on a time-dependent variable within time-dependent Cox models are investigated in a simulation study. Quality of life (QoL) assessments were removed from the complete simulated datasets, which have a positive relationship between QoL and disease-free survival (DFS) and delayed chemotherapy and DFS, by missing at random and missing not at random (MNAR) mechanisms. Standard imputation methods were applied before analysis. Method performance was influenced by missing data mechanism, with one exception for simple imputation. The greatest bias occurred under MNAR and large effect sizes. It is important to carefully investigate the missing data mechanism.

关键词： imputation methods Missing data mechanism Quality of life Simulation study Time-dependent Cox model

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Analyzing data sets with missing data: An empirical evaluation of imputation methods and likelihood-based methods

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2001年第11期27卷 999-1013页

作者： Myrtveit, I Stensrud, E Olsson, UH Norwegian Sch Management N-1301 Sandvika Norway

Missing data are often encountered in data sets used to construct effort prediction models. Thus far, the common practice has been to ignore observations with missing data. This may result in biased prediction models. In this paper, we evaluate four missing data techniques (MDTs) in the context of software cost modeling: listwise deletion (LID), mean imputation (MI), similar response pattern imputation (SRPI), and full information maximum likelihood (FIML). We apply the MDTs to an ERP data set, and thereafter construct regression-based prediction models using the resulting data sets. The evaluation suggests that only FIML is appropriate when the data are not missing completely at random (MCAR). Unlike FIML, prediction models constructed on LD, MI and SRPI data sets will be biased unless the data are MCAR. Furthermore, compared to LID, MI and SRPI seem appropriate only if the resulting LID data set is too small to enable the construction of a meaningful regression-based prediction model.

关键词： software effort prediction cost estimation missing data imputation methods listwise deletion mean imputation similar response pattern imputation full information maximum likelihood log-log regression ERP

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