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About 10,000 papers for “data preprocessing”

preprocessing-ice-data

Moss, Guy, Višnjević, Vjeran, Eisen, Olaf et al. · Zenodo (CERN European Organization for Nuclear Research) · 2024

First release with manuscript submission

Cited by 0Open access

Data Preprocessing Toolbox

Dybeck, Jan Lutz · Zenodo (CERN European Organization for Nuclear Research) · 2024

This repository is a collection of functions for data preprocessing. The Toolbox is based on pandas and dask. All Python files are independend of the other. One modul (Python file with functions) could be used without other moduls. This work was is a part of a master proposal at TU Darmstadt, Laser- and Plasmaphysics. The repository saves the state of the project at the Date of the master proposal. The project is maintained at the GitLab repository (see README).

Cited by 0Open access

Preprocessing Microarray Data

Amaratunga, Dhammika, Cabrera, Javier, Shkedy, Ziv · Document Server@UHasselt (UHasselt) · 2014

[Amaratunga, Dhammika] Johnson & Johnson, Janssen Pharmaceut Co, New Brunswick, NJ 08901 USA. [Cabrera, Javier] Rutgers State Univ, Piscataway, NJ 08855 USA. [Shkedy, Ziv] Hasselt Univ, Hasselt, Belgium.

Cited by 0

Data preprocessing pipeline

Ronald Moore (3549086), Daniela Chanci (21693247), Stephanie R Brown (21693250) et al. · Figshare · 2025

SHAP = Shapley values indicating features of importance in the model (TIF)

Cited by 0Open access

preprocessing-ice-data

Moss, Guy, Višnjević, Vjeran, Eisen, Olaf et al. · Zenodo (CERN European Organization for Nuclear Research) · 2024

First release with manuscript submission

Cited by 1Open access

Preprocessing the Data

Patricia B. Cerrito, John Cerrito · Advances in medical technologies and clinical practice book series · 2010

In this book, we provide tools that are needed to investigate administrative and clinical databases that are routinely collected in the support of patient treatment. Often, these databases are large and require non-traditional methodology to investigate. In addition, as they are collected for purposes other than research, there is considerable preprocessing that is required in order to use the data for the purpose of analysis in order to find important results that can improve the quality of patient care. Therefore, we will show by example just how to preprocess the data, and how non-traditional statistical methods can be used to investigate the data and to extract meaning from the databases. We will show details and programming code necessary to complete the preprocessing, and we will discuss the type of preprocessing necessary to use each statistical method and data mining technique.

Cited by 1

Preprocessing Microarray Data

Dhammika Amaratunga, Javier Cabrera · Wiley series in probability and statistics · 2003

The prelims comprise: Logarithmic Transformation Variance Stabilizing Transformations Sources of Bias Normalization Intensity-Dependent Normalization Outlier Identification Assessing Replicate Array Quality

Cited by 0

Functional data preprocessing.

Jürgen Fuchshuber (10066525), Karl Koschutnig (160765), Andreas Fink (160741) et al. · Figshare · 2025

<div> Background Insecure adult attachment has previously been linked to more severe psychopathology and to alterations within neuronal connectivity on a structural as well as functional level. Little is known about the resting state functional connectivity (rs-FC) of the attachment system in patients suffering from poly-drug use disorder (PUD). Methods The present study investigated rs-FC at two measuring points (t1: ROI-to-ROI; t2: seed-to-voxel) in a sample of PUD patients (n = 33; Age: M = 30y; SD = 8y; Female = 15%). Adult attachment was measured with the German version of the Experiences in Close Relationships Scale (ECR-RD8). Furthermore, insecure attachment was correlated with depressive symptoms (ADS), trait anxiety (STAI) and general psychopathology (BSI-53). Results More insecure attachment was associated with increased trait anxiety, depressive and general psychiatric symptom burden in patients. Furthermore, we observed time-stable links between insecure adult attachment and increased rs-FC between the left lateral parietal default mode network (DMN LP) and bilateral parts of the salience network, as well as decreased rs-FC between DMN LP and medial parts of the DMN. Discussion Implications of the present study are highlighting the association between attachment security and brain areas related to affect regulation. </div>

Cited by 0Open access

Climate data preprocessing.

Jouni Takalo (353446), Irina Ignatova (353448), Matti Weckström (57381) et al. · Figshare · 2015

&lt;p&gt;&lt;b&gt;A&lt;/b&gt;, MI and information rate (dashed line) over four years of data; inset shows the fraction of missing data points occurring on either one (grey) or on both data series simultaneously (black). &lt;b&gt;B&lt;/b&gt;, MI (black), information rate per sample (dark grey) or per minute (light grey) of 100,000 data points with different sample intervals.&lt;/p&gt;

Cited by 0Open access