Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Quantitative Analysis01:12

Quantitative Analysis

Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the method...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

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 Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka 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...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...

You might also read

Related Articles

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

Sort by
Same author

[OBLIQUE ILLUMINATION].

Rinsho ganka. Japanese journal of clinical ophthalmology·1963
See all related articles

Related Experiment Video

Updated: May 22, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Robust smoothing of quantitative genomic data using second-generation wavelets and bivariate shrinkage.

H Hatsuda1

  • 1Department of Statistics, The University of Warwick,Coventry, CV4 7AL, U.K. H.Hatsuda@warwick.ac.uk

IEEE Transactions on Bio-Medical Engineering
|May 15, 2012
PubMed
Summary

Biologists can now process large genomic datasets more effectively using a new smoothing method. This approach utilizes second-generation wavelets for improved noise reduction in biomedical data analysis.

Related Experiment Videos

Last Updated: May 22, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

  • Genomics
  • Bioinformatics
  • Signal Processing

Background:

  • High-throughput sequencing generates vast quantitative genomic data requiring processing.
  • Data smoothing is crucial for removing noise in biomedical datasets.
  • Classical wavelet transforms are common, but second-generation wavelets offer advantages for irregular biomedical data.

Purpose of the Study:

  • To introduce a novel smoothing method for genomic data using second-generation wavelets.
  • To improve upon classical wavelet-based smoothing techniques for biomedical applications.
  • To enable robust threshold determination for wavelet-based smoothing.

Main Methods:

  • Development of a novel smoothing method based on second-generation wavelets.
  • Integration of bivariate shrinkage for robust threshold determination.
  • Application and testing on synthetic and real genomic datasets.

Main Results:

  • The proposed method demonstrates effective smoothing of genomic data.
  • Second-generation wavelets prove more suitable for irregular biomedical data than classical wavelets.
  • Experimental results validate the effectiveness of the novel smoothing approach.

Conclusions:

  • The novel second-generation wavelet-based smoothing method offers superior performance for genomic data.
  • This technique provides a more effective approach to noise reduction in biomedical data analysis.
  • The method facilitates robust thresholding, enhancing the reliability of genomic data processing.