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

Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
Applications of Molecular Taxonomy01:20

Applications of Molecular Taxonomy

Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...
Genome Annotation and Assembly03:36

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.

You might also read

Related Articles

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

Sort by
Same author

Safe Fairness Guarantees Without Demographics in Classification: Spectral Uncertainty Set Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Derivation of an intrinsic brain activity biomarker for the earliest prediction of cognitive decline.

Scientific reports·2026
Same author

Multimodal fusion strategies for survival prediction in breast cancer: A comparative deep learning study.

Computational and structural biotechnology journal·2025
Same author

RNACOREX - RNA coregulatory network explorer and classifier.

PLoS computational biology·2025
Same author

Conformal inference for reliable single cell RNA-seq annotation.

Bioinformatics (Oxford, England)·2025
Same author

Discovering Genetic Variants in Hypertrophic Cardiomyopathy With Multiple Machine Learning Techniques.

IEEE transactions on computational biology and bioinformatics·2025
Same journal

Tracking Synthetic Adhesins on Bacterial Surfaces with Immunofluorescence Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Post-Selection Methods for Analyzing mRNA Display Selections and Optimization of Hits.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

High-Performance Computing in Tandem Mass Spectrometry (MS/MS) Peptide Identification.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Engineering and Adapting Disulfide-Containing Proteins to Enable Intracellular Functionality.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

AI-Driven Protein Research: From Prediction to Design.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Methods for the In Vitro Selection of Protein and Peptide Libraries Using mRNA Display.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: Jun 18, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Machine learning: an indispensable tool in bioinformatics.

Iñaki Inza1, Borja Calvo, Rubén Armañanzas

  • 1Intelligent Systems Group, Donostia - San Sebastián, Basque Country, Spain.

Methods in Molecular Biology (Clifton, N.J.)
|December 4, 2009
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) is crucial for analyzing complex biological data. This chapter details ML algorithms, data preprocessing, classification, and clustering for bioinformatics and biomarker discovery.

More Related Videos

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Related Experiment Videos

Last Updated: Jun 18, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Increasing biological data complexity necessitates advanced analytical tools.
  • Machine learning (ML) is now integral to bio-laboratories.
  • ML applications span disease mechanism investigation and biomarker discovery.

Purpose of the Study:

  • To provide a foundational understanding of machine learning algorithms.
  • To detail data preprocessing, supervised classification, and clustering techniques.
  • To highlight feature selection, classifier evaluation, and key bioinformatics applications.

Main Methods:

  • Taxonomy of machine learning algorithms.
  • Description of data preprocessing techniques.
  • Explanation of supervised classification and clustering methods.

Main Results:

  • Overview of feature selection and classifier evaluation.
  • Presentation of impactful supervised classification topics in bioinformatics.
  • Identification of popular ML resources, software, and data repositories.

Conclusions:

  • Machine learning is essential for modern bioinformatics.
  • The chapter equips readers with knowledge of ML techniques and resources.
  • It supports biomarker discovery and understanding of biological mechanisms.