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About 10,000 papers for “data preprocessing”
Christos Maniatis (12902735), Catalina A. Vallejos (12902738), Guido Sanguinetti (343362) · Figshare · 2022
<p>Here we discuss the preprocessing and quality control steps taken in mESC and bESC datasets. This includes aggregating raw epigenetic data from multiple cells, normalizing single cell transcriptomic data, integrating epigenomic and trascriptomic layers and removing low quality data.</p> <p>(PDF)</p>
Cited by 0Open access
Usman Qamar, Muhammad Summair Raza · 2020
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Ran Hu (4989065), Benjamin Tran (423986), Shuo Li (143148) et al. · Figshare · 2025
<div> Background The current noninvasive prognostic evaluation methods for hepatocellular carcinoma (HCC), which are largely reliant on radiographic imaging features and serum biomarkers such as alpha-fetoprotein (AFP), have limited effectiveness in discriminating patient outcomes. Identification of new prognostic biomarkers is a critical unmet need to improve treatment decision-making. Epigenetic changes in cell-free DNA (cfDNA) have shown promise in early cancer diagnosis and prognosis. Thus, we aim to evaluate the potential of cfDNA methylation as a noninvasive predictor for prognostication in patients with active, radiographically viable HCC. Methods Using Illumina HumanMethylation450 array data of 377 HCC tumors and 50 adjacent normal tissues obtained from The Cancer Genome Atlas (TCGA), we identified 158 HCC-related DNA methylation markers associated with overall survival (OS). This signature was further validated in 29 HCC tumor tissue samples. Subsequently, we applied the signature to an independent cohort of 52 patients with plasma cfDNA samples by calculating the cfDNA methylation-based risk score (methRisk) via random survival forest models with 10-fold cross-validation for the prognostication of OS. Results The cfDNA-based methRisk showed strong discriminatory power when evaluated as a single predictor for OS (3-year AUC = 0.81, 95% CI: 0.68–0.94). Integrating the methRisk with existing risk indices like Barcelona clinic liver cancer (BCLC) staging significantly improved the noninvasive prognostic assessments for OS (3-year AUC = 0.91, 95% CI: 0.80–1), and methRisk remained an independent predictor of survival in the multivariate Cox model (P = 0.007). Conclusions Our study serves as a pilot study demonstrating that cfDNA methylation biomarkers assessed from a peripheral blood draw can stratify HCC patients into clinically meaningful risk groups. These findings indicate that cfDNA methylation is a promising noninvasive prognostic biomarker for HCC, providing a proof-of-concept for its potential clinical utility and laying the groundwork for broader applications. </div>
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Huijun Wang (288634), Ying Ding (152708), Jie Tang (46802) et al. · Figshare · 2015
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Di Wu · 2024
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Daniel T. Larose · 2004
Chapter Two begins by explaining why data preprocessing is needed. Much of the raw data contained in databases is unpreprocessed, incomplete, and noisy. Therefore, in order to be useful for data mining purposes, the databases need to undergo preprocessing, in the form of data cleaning and data transformation. The overriding objective is to minimize GIGO, to minimize the Garbage that gets Into our model, so that we can minimize the amount of Garbage that our models give Out. Data preparation alone accounts for 60% of all the time and effort for the entire data mining process. Much of the data contains field values that have expired, are no longer relevant, or are simply missing. Examples of data cleaning are provided. Different methods of handling missing values are provided, including replacing missing field values with user-defined constants, means, or random draws from the variable distribution. Identifying misclassifications is discussed, as well as graphical methods for identifying outliers. Methods of transforming data are provided, including min–max normalization, and Z-score standardization. Finally, numerical methods for identifying outliers are examined, including the IQR method. The hands-on analysis problems include challenging the reader to preprocess the large churn data set, so that it will be ready for further analysis downstream.
Cited by 1
Usman Qamar, Muhammad Summair Raza · 2023
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Hongxing Li, C.L. Philip Chen, Han‐Pang Huang · 2018
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Guillaume Coqueret, Tony Guida · 2020
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Thomas A. Runkler · Data analytics · 2020
Cited by 2
Encyclopedia of Machine Learning · 2010
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Paul Andrew Thompson, Dorothy Bishop, Zoe Woodhead et al. · OSF Preprints (OSF Preprints)
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Brandon-Joe Biba, Brian O'Shea · OSF Preprints (OSF Preprints) · 2024
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Radek Vašíček · Digital Repository (National Repository of Grey Literature) · 2008
This thesis surveys on problems preprocessing data. Forepart deal with view and description characteristic tests for description attributes, methods for work with data and attributes. Second part work describes work with program Rapidminer. It pays pay attention to single functions preprocessing in this programme describes their function. Third part equate to results with using methods preprocessing and without using data preprocessing.
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Khalid K. Al-jabery, Tayo Obafemi-Ajayi, Gayla R. Olbricht et al. · Elsevier eBooks · 2019
Cited by 24
Paul Andrew Thompson, Dorothy Bishop, Zoe Woodhead et al. · OSF Preprints (OSF Preprints) · 2022
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Yuhao Gong (4788654), Fei Wang (19534), Yuchen Zhang (524816) et al. · Figshare · 2025
<div> With the integration of educational technology and artificial intelligence, personalized learning has become increasingly important. However, traditional educational data mining methods struggle to effectively integrate heterogeneous feature data and represent complex learning interaction processes, while existing deep learning models lack educational theory guidance, resulting in insufficient interpretability. To address these challenges, this study proposes the TGEL-Transformer (Theory-Guided Educational Learning Transformer) framework, which integrates multiple intelligence theory and social cognitive theory, featuring three innovations: a dual-channel feature processing module that integrates cognitive, affective, and environmental dimension features; a theory-guided four-head attention mechanism that models educational interaction dynamics; and an interpretable prediction layer that provides theoretical support for educational interventions. Using a dataset of 6,608 students, TGEL-Transformer achieved RMSE = 1.87 and R<sup>2</sup> = 0.75, outperforming existing methods with statistically significant improvements (p < 0.001) ranging from 1.1% against recent state-of-the-art models to 5.6% against transformer baselines. External validation on cross-cultural data (n = 480) demonstrated strong generalizability with R<sup>2</sup> = 0.683. Attention weight analysis revealed that teacher support (0.15), prior knowledge (0.15), and peer interaction (0.13) are key factors influencing learning outcomes. This study provides a theory-guided framework for educational data mining, offering data-driven support for personalized education and advancing intelligent education development. </div>
Cited by 0Open access
Qiran, Wang · Figshare · 2025
The hyperspectral data underwent a multi-step preprocessing pipeline to enhance spectral quality and ensure consistency across samples. First, Principal Component Analysis (PCA) was applied in ENVI to reduce dimensionality while retaining over 99.95% of the total variance. Subsequently, Standard Normal Variate (SNV) transformation was performed to eliminate scattering effects and baseline drift, followed by Savitzky–Golay (SG) filtering and first-order derivative processing to smooth spectra and highlight subtle features. Finally, Z-score normalization was applied to standardize feature magnitudes, improving model stability and convergence in subsequent classification tasks.
Cited by 0Open access
Linda Ficco, Anna Schroeger · OSF Preprints (OSF Preprints) · 2024
Data files aggregated by test and R markdown script which cleans and restructures the data for further analyses.
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Radek Vašíček · Digital Repository (National Repository of Grey Literature) · 2008
This thesis surveys on problems preprocessing data. Forepart deal with view and description characteristic tests for description attributes, methods for work with data and attributes. Second part work describes work with program Rapidminer. It pays pay attention to single functions preprocessing in this programme describes their function. Third part equate to results with using methods preprocessing and without using data preprocessing.
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