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

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Machines

Machines

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...

Network Covalent Solids

Network Covalent Solids

Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...

Machines: Problem Solving II

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.

What is Conservation Biology?

What is Conservation Biology?

Conservation biology is a scientific field that focuses on the preservation of biodiversity in order to protect ecosystems while meeting the needs of the human population. Humans require properly functioning ecosystems to maintain our supply of natural resources, including food, medicines, and building materials.

You might also read

Related Articles

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

Sort by

Same author

An AI-Powered Trisomy 21 Research Assistant.

bioRxiv : the preprint server for biology·2026

Same author

Deep learning-enabled discovery of antibiotics effective against <i>Neisseria gonorrhoeae</i>.

Science translational medicine·2026

Same author

Perceived authenticity drives gaze behavior when watching AI-generated videos of physical scenes.

Scientific reports·2026

Same author

General scales unlock AI evaluation with explanatory and predictive power.

Nature·2026

Same author

Generative AI for synthetic biology: Designing biological parts, circuits, and genomes.

Cell systems·2026

Same author

FATE-MAP predicts teratogenicity and human gastrulation failure modes by integrating deep learning and mechanistic modeling.

Nature communications·2026

Same journal

Whole-cell particle-based digital twin simulations from 4D lattice light-sheet microscopy data.

Cell·2026

Same journal

Systematic discovery of pathogen effector functions across human pathogens and pathways.

Cell·2026

Same journal

Structural basis for host membrane binding and remodeling by invading malaria parasites.

Cell·2026

Same journal

Multiscale integration of tissue and chromatin context converts cell heterogeneity into stable intestinal patterning.

Cell·2026

Same journal

Arc mediates intercellular tau transmission via extracellular vesicles.

Cell·2026

Same journal

Electromagnetic field-inducible in vivo gene switch for remote spatiotemporal control of gene expression.

Cell·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 9, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Next-Generation Machine Learning for Biological Networks.

Diogo M Camacho¹, Katherine M Collins², Rani K Powers³

¹Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.

|June 12, 2018

Summary

This summary is machine-generated.

Machine learning (ML) advances biological research by building predictive models from complex data. This primer introduces ML and deep learning for network biology, impacting disease, drug discovery, and synthetic biology.

Keywords:

Machine leaning deep learning network biology neural networks synthetic biology systems biology

More Related Videos

A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

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

Related Experiment Videos

Last Updated: Feb 9, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

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

Area of Science:

Computational Biology
Bioinformatics
Systems Biology

Background:

Machine learning (ML) is increasingly vital in biological research.
ML techniques build predictive models from large, multi-dimensional datasets.
ML enables the study of complex biological systems, including cellular networks.

Purpose of the Study:

To provide a primer on machine learning (ML) for life scientists.
To introduce deep learning (DL) concepts within a biological context.
To explore the intersection of ML and network biology.

Main Methods:

Review of machine learning principles.
Introduction to deep learning algorithms.
Discussion of applications in network biology.

Main Results:

Machine learning offers powerful tools for analyzing biological data.
Deep learning presents new avenues for biological modeling.
The integration of ML with network biology holds significant potential.

Conclusions:

ML and DL are transformative for understanding complex biological systems.
This approach can accelerate progress in disease biology and drug discovery.
Future research directions include microbiome and synthetic biology applications.