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...

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...

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.

Mismatch Repair

Mismatch Repair

Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

Assumptions of Survival Analysis

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.

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

Data-Independent-Acquisition-Based Proteomic Approach towards Understanding the Acclimation Strategy of Oleaginous Microalga <i>Microchloropsis gaditana</i> CCMP526 in Hypersaline Conditions.

ACS omega·2021

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

Optimization of incubation conditions of Plasmodium falciparum antibody multiplex assays to measure IgG, IgG<sub>1-4</sub>, IgM and IgE using standard and customized reference pools for sero-epidemiological and vaccine studies.

Malaria journal·2018

Same author

Temporal acclimation of Microchloropsis gaditana CCMP526 in response to hypersalinity.

Bioresource technology·2018

Search research articles

Related Experiment Video

Updated: Jul 6, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Ameliorative missing value imputation for robust biological knowledge inference.

Muhammad Shoaib B Sehgal¹, Iqbal Gondal, Laurence S Dooley

¹Faculty of Information Technology, Monash University, Northways Road, Churchill, Vic. 3842, Australia. Shoaib.Sehgal@infotech.monash.edu.au

Journal of Biomedical Informatics

|March 13, 2008

Summary

This study introduces Ameliorative Missing Value Imputation (AMVI), a novel technique for gene expression data. AMVI effectively imputes missing values, improving biological analysis accuracy and overcoming limitations of existing methods.

More Related Videos

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Related Experiment Videos

Last Updated: Jul 6, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Gene expression data from microarrays is crucial for post-genomic analyses.
Missing values in this data can significantly hinder subsequent biological interpretations.
Existing imputation methods often rely on either global or local data structures, leading to estimation errors.

Purpose of the Study:

To develop an improved missing value imputation technique for gene expression data.
To address the limitations of current methods by exploiting both global/local and positive/negative correlations.
To introduce a robust method for automatically selecting the optimal number of predictor genes.

Main Methods:

Proposed Ameliorative Missing Value Imputation (AMVI) technique.
AMVI utilizes a core Collateral Missing Value Estimation (CMVE) strategy.
Employs a wrapper non-parametric method with Monte Carlo simulations for optimal predictor gene selection (k).

Main Results:

AMVI demonstrated superior performance compared to CMVE, BPCA, LLSImpute, and KNN across multiple datasets.
Achieved lower Normalized Root Mean Square Error (NRMS) and improved True Positive (TP) rates.
Showcased enhanced biological significance and statistical validity of selected genes post-imputation.

Conclusions:

AMVI effectively imputes missing values in microarray data, adapting to latent data structures.
The method offers a robust and accurate alternative to trial-and-error approaches for parameter selection.
AMVI significantly enhances the reliability of downstream biological analyses using gene expression data.