Suchergebnisse - imputation and normalization algorithms

LFQ-Based Peptide and Protein Intensity Differential Expression Analysis von Bai, Mingze, Deng, Jingwen, Dai, Chengxin, Pfeuffer, Julianus, Sachsenberg, Timo, Perez-Riverol, Yasset

“… Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform the final tasks of imputation, summarization …”
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Normalization and outlier removal in class center-based firefly algorithm for missing value imputation von Nugroho, Heru, Utama, Nugraha Priya, Surendro, Kridanto

“… Hence, data normalization and missing value handling are considered the major problems in the data pre-processing stage, while classification algorithms are adopted to handle numerical features …”
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DAnTE: a statistical tool for quantitative analysis of -omics data von Polpitiya, Ashoka D., Qian, Wei-Jun, Jaitly, Navdeep, Petyuk, Vladislav A., Adkins, Joshua N., Camp, David G., Anderson, Gordon A., Smith, Richard D.

“… DAnTE features selected normalization methods, missing value imputation algorithms, peptide-to-protein rollup methods, an extensive array of plotting functions and a comprehensive hypothesis-testing …”
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MISSING VALUE IMPUTATION AND NORMALIZATION TECHNIQUES IN MYOCARDIAL INFARCTION von K Manimekalai, A Kavitha

“… The experiment is done with standard bench mark data and real time collected data. KNBP imputation method and Decimal Scaling Algorithm for Normalization got lower error rate …”
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Efficient heart disease prediction-based on optimal feature selection using DFCSS and classification by improved Elman-SFO von Wankhede, Jaishri, Kumar, Magesh, Sambandam, Palaniappan

“… Initially, the data pre-processing process is performed using data cleaning, data transformation, missing values imputation, and data normalisation …”
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Normalization and missing value imputation for label-free LC-MS analysis von Karpievitch, Yuliya V, Dabney, Alan R, Smith, Richard D

“… Typically, normalization is performed in an attempt to remove systematic biases from the data before statistical inference, sometimes followed by missing value imputation to obtain a complete matrix of intensities …”
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Dex-Benchmark: datasets and code to evaluate algorithms for transcriptomics data analysis von Xie, Zhuorui, Chen, Clara, Ma’ayan, Avi

“… Many tools and algorithms are available for analyzing transcriptomics data. These include algorithms for performing sequence alignment, data normalization …”
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A Statistical Approach for Identifying the Best Combination of Normalization and Imputation Methods for Label-Free Proteomics Expression Data von Sakthivel, Kabilan, Lal, Shashi Bhushan, Srivastava, Sudhir, Chaturvedi, Krishna Kumar, Khan, Yasin Jeshima, Mishra, Dwijesh Chandra, Madival, Sharanbasappa D, Vaidhyanathan, Ramasubramanian, Jha, Girish Kumar

“… Label-free proteomics expression data sets often exhibit data heterogeneity and missing values, necessitating the development of effective normalization and imputation methods …”
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CN-GAIN: Classification and Normalization-Denormalization-Based Generative Adversarial Imputation Network for Missing SMES Data Imputation von Sudrajat, Antonius Wahyu, -, Ermatita, -, Samsuryadi

“… This study proposes a new model for missing data imputation called the Classification and Normalization-Denormalization-based Generative Adversarial Imputation Network (CN-GAIN …”
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GridWaveLoc: A Fault Location Algorithm Integrating Fault Traveling Wave Distribution and Network Dependency Graphs for Transmission Grids von Han, Dong, Wang, Ke, Wang, Xiaoguang, Li, Yue, Yang, Zhihao

“… Accurate fault localization in transmission grids is essential for reducing downtime and maintaining power system stability. Conventional fault location …”
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Reservoir temperature prediction based on characterization of water chemistry data—case study of western Anatolia, Turkey von Shi, Haoxin, Zhang, Yanjun, Yu, Ziwang, Yang, Yunxing

“… These models considered various input factors and underwent data preprocessing steps like null value imputation, normalization, and Pearson coefficient calculation …”
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BiGRU and recursive feature elimination with Walrus optimization algorithm for IoT device identification in trajectory data von Nandan, R. Raghu, Nalini, N.

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A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation von Välikangas, Tommi, Suomi, Tomi, Elo, Laura L

“… Each software includes a set of unique algorithms for different tasks of the MS data processing workflow …”
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Enhanced early chronic kidney disease prediction using hybrid waterwheel plant algorithm for deep neural network optimization von Khafaga, Doaa Sami, Khodadadi, Nima, Khodadadi, Ehsaneh, Ali Alhussan, Amel, Eid, Marwa M, El-Kenawy, El-Sayed M

“… ) with Grey Wolf Optimization (GWO). Using the UCI CKD dataset, rigorous preprocessing techniques-including data imputation, normalization, and synthetic oversampling-were employed to enhance data quality and mitigate class imbalance …”
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Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics von Webb-Robertson, Bobbie-Jo M, Wiberg, Holli K, Matzke, Melissa M, Brown, Joseph N, Wang, Jing, McDermott, Jason E, Smith, Richard D, Rodland, Karin D, Metz, Thomas O, Pounds, Joel G, Waters, Katrina M

“… of accuracy and robustness. However, no single algorithm consistently outperforms the remaining approaches, and in some cases, performing classification without imputation sometimes yielded the most accurate classification …”
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Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring von Zhang, Runchi, Qiu, Zhiyi

“… This framework incorporates three procedures. Step 1, pre-processing, including imputation, normalization, and re-ordering of the samples …”
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Development and evaluation of interpretable machine learning regressors for predicting femoral neck bone mineral density in elderly men using NHANES data von He, Wen, Chen, Song, Fu, Xianghong, Xu, Licong, Xie, Jun, Wan, Jinxing

“… ) algorithm, and data normalization and encoding. The dataset was split into training and test sets with a 7 …”
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Data-Driven Insights: Boosting Algorithms to Uncover Electricity Theft Patterns in AMI von Khan, Inam Ullah, Ali, Arshid, Taylor, C. James, Ma, Xiandong

“… Comprehensive preprocessing, including imputation, normalization, outlier management, and resampling, ensures that the time-series data are accurately prepared for analysis …”
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Optimized Extreme Gradient Boosting with Remora Algorithm for Congestion Prediction in Transport Layer von Kumar, Ajay, Hemrajani, Naveen

“… The most popular transport protocol in use today is TCP that cannot fully utilize the ability of the network because of the constraints of its conservative congestion control algorithm …”
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Comparing preprocessing strategies for 3D-Gene microarray data of extracellular vesicle-derived miRNAs von Takemoto, Yuto, Ito, Daisuke, Komori, Shota, Kishimoto, Yoshiyuki, Yamada, Shinichiro, Hashizume, Atsushi, Katsuno, Masahisa, Nakatochi, Masahiro

“… studied for Toray’s 3D-Gene chip, a widely used measurement method. We aimed to evaluate batch effect, missing value imputation accuracy, and the influence of preprocessing …”
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