Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Quantifying and Rejecting Outliers: The Grubbs Test

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for k_a Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

Truncation in Survival Analysis

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

The need for cybersecurity self-evaluation in healthcare.

BMC medical informatics and decision making·2024

Same author

Rapid health data repository allocation using predictive machine learning.

Health informatics journal·2020

Same author

A Patient Agent to Manage Blockchains for Remote Patient Monitoring.

Studies in health technology and informatics·2018

Same author

A Cultural Competence Organizational Review for Community Health Services: Insights From a Participatory Approach.

Health promotion practice·2017

Same author

Teeth Tales: a community-based child oral health promotion trial with migrant families in Australia.

BMJ open·2015

Same author

An exploratory trial implementing a community-based child oral health promotion intervention for Australian families from refugee and migrant backgrounds: a protocol paper for Teeth Tales.

BMJ open·2014

Search research articles

Related Experiment Video

Updated: Jul 8, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data.

Muhammad Shoaib B Sehgal¹, Iqbal Gondal, Laurence S Dooley

¹Gippsland School of Computing and Information Technology, Monash University, VIC 3842, Australia. Shoaib.Sehgal@infotech.monash.edu.au

Bioinformatics (Oxford, England)

|February 26, 2005

Summary

Collateral Missing Value Estimation (CMVE) offers superior imputation for biological data. This novel method accurately estimates missing values in microarray datasets, improving downstream analysis.

More Related Videos

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Published on: August 21, 2019

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

Related Experiment Videos

Last Updated: Jul 8, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Published on: August 21, 2019

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Microarray data frequently contains missing values, impacting statistical and machine learning analyses.
Accurate imputation of missing data is crucial for reliable biological data analysis.
Existing imputation methods require further development for robust performance.

Purpose of the Study:

To introduce an innovative missing value imputation algorithm, Collateral Missing Value Estimation (CMVE).
To address the need for more robust techniques in biological data analysis.
To improve the accuracy of missing value estimation in microarray datasets.

Main Methods:

CMVE utilizes multiple covariance-based imputation matrices for missing value prediction.
Matrices are computed and optimized using least square regression and linear programming.
The algorithm was tested against Bayesian Principal Component Analysis Imputation (BPCA), LSImpute, and K-nearest neighbour (KNN).

Main Results:

CMVE demonstrated superior and robust missing value estimation compared to BPCA, LSImpute, and KNN.
Performance was validated across ovarian cancer and yeast sporulation datasets, including real-world missing values.
CMVE achieved better accuracy across various missing value probabilities (0.01-0.2) with similar computational complexity.

Conclusions:

CMVE provides a significant advancement in missing value imputation for biological data.
The algorithm offers improved accuracy and robustness for both time-series and non-time-series data.
CMVE enhances the reliability of subsequent statistical and machine learning analyses on microarray data.