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

Genomics

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...

Evolutionary Relationships through Genome Comparisons

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...

Genome-wide Association Studies-GWAS

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...

You might also read

Related Articles

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

Sort by

Same author

MORC3 represses a tandem repeat enhancer to regulate interferon.

The EMBO journal·2026

Same author

SCOT+: a comprehensive software suite for single-cell alignment using optimal transport.

Bioinformatics advances·2026

Same author

An snRNA-seq aging clock for the fruit fly head sheds light on sex-biased aging.

Scientific reports·2026

Same author

Cell type-specific gene regulatory network inference from single cell transcriptomics with ctOTVelo.

bioRxiv : the preprint server for biology·2026

Same author

Systematic clustering alignment and feature characterization for single-cell omics using ACE-OF-Clust.

bioRxiv : the preprint server for biology·2026

Same author

Evaluation of antioxidant enzyme activity among Indian patients with type 2 diabetes mellitus.

Bioinformation·2026

Same journal

Function through shape: An overview of DNA G-quadruplexes in transcriptional regulation.

Current opinion in chemical biology·2026

Same journal

Advances in tools and technologies for multiplexed bioluminescence imaging.

Current opinion in chemical biology·2026

Same journal

High-resolution molecular mapping by expansion-coupled label-free and multimodal imaging.

Current opinion in chemical biology·2026

Same journal

Recent advances in glycoconjugate-based therapeutics.

Current opinion in chemical biology·2026

Same journal

Towards better red emitters for bioimaging: Innovations in rhodamine and cyanine chemistry.

Current opinion in chemical biology·2026

Same journal

Chemigenetic fluorescent biosensors in biological imaging - New trends and advances.

Current opinion in chemical biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 2, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Machine learning for profile prediction in genomics.

Jacob Schreiber¹, Ritambhara Singh²

¹Department of Genetics, Stanford University, United States.

Current Opinion in Chemical Biology

|June 9, 2021

Summary

This summary is machine-generated.

Machine learning methods are advancing genomics by predicting biochemical activities like histone modification and chromatin accessibility from multi-omics data. This review categorizes these profile prediction tasks and their applications.

Keywords:

Imputation Interpretation Motif detection Neural networks Prediction tasks Profile prediction Regulatory mechanisms

More Related Videos

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

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

Published on: July 22, 2025

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

Related Experiment Videos

Last Updated: Nov 2, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

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

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

Published on: July 22, 2025

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Publicly available multi-omics data has surged, enabling advanced computational approaches.
Machine learning (ML) is increasingly applied to address complex genomic questions.

Purpose of the Study:

To review ML methods for genomic profile prediction.
To categorize profile prediction tasks and discuss their scientific applications.

Main Methods:

Overview of existing research on ML-based genomic profile prediction.
Definition of two primary categories for profile prediction tasks.

Main Results:

Identification of common profile prediction tasks, such as histone modification and chromatin accessibility prediction.
Discussion of the scientific inquiries addressable by different prediction categories.

Conclusions:

ML-driven profile prediction is a key application area for multi-omics data analysis in genomics.
Understanding these tasks aids in leveraging genomic data for biological discovery.