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Related Concept Videos

In-vitro Mutagenesis01:16

In-vitro Mutagenesis

To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.
Experimental RNAi02:15

Experimental RNAi

RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...
What is Gene Expression?01:42

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...

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Related Experiment Video

Updated: Jun 6, 2026

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

Missing value imputation for gene expression data: computational techniques to recover missing data from available

Alan Wee-Chung Liew1, Ngai-Fong Law, Hong Yan

  • 1School of Information and Communication Technology, Gold Coast Campus, Griffith University, QLD4222, Australia. a.liew@griffith.edu.au

Briefings in Bioinformatics
|December 16, 2010
PubMed
Summary
This summary is machine-generated.

Missing values in gene expression data hinder analysis. This review surveys imputation algorithms, their methods, validation, and future directions for improved microarray data analysis.

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Last Updated: Jun 6, 2026

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07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray gene expression data frequently contains missing values due to experimental issues.
  • Missing data can negatively impact subsequent analyses, necessitating imputation methods.

Purpose of the Study:

  • To provide a comprehensive review of existing missing value imputation algorithms for microarray data.
  • To categorize algorithms based on information utilization (local, global, domain knowledge) and discuss validation strategies.

Main Methods:

  • Systematic review of imputation algorithms.
  • Analysis of algorithmic techniques, information sources, and validation metrics.
  • Discussion of performance assessment and future research avenues.

Main Results:

  • Categorization of imputation algorithms based on their approaches to handling missing data.
  • Overview of methods for validating imputation results and assessing algorithm performance.
  • Identification of current trends and potential future research directions in the field.

Conclusions:

  • A thorough understanding of current imputation techniques is crucial for advancing microarray data analysis.
  • This review aims to guide researchers in selecting appropriate imputation methods and inspire the development of novel algorithms.