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

Synaptic Signaling01:09

Synaptic Signaling

6.9K
Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
The presynaptic neuron fires an action potential that...
6.9K
Associative Learning01:27

Associative Learning

1.6K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.6K
Neural Circuits01:25

Neural Circuits

3.0K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
3.0K
Neurons as Communicators of the Brain01:22

Neurons as Communicators of the Brain

3.8K
Neurons, the fundamental units of the brain and nervous system, function as the primary transmitters of information throughout the body. Their ability to communicate through electrical and chemical signals is vital for every bodily function, from regulating the heartbeat to processing complex thoughts. Each neuron has three main components: the cell body (soma), dendrites, and an axon, each specialized to facilitate swift and efficient neural communication.
Cell Body
The cell body, also known...
3.8K
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

2.2K
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
2.2K
Functional Brain Systems: Limbic System01:15

Functional Brain Systems: Limbic System

8.3K
The limbic system, often called the "emotional brain," is a complex set of structures located deep within the brain. The intricate network of the limbic system supports a wide range of psychological functions, from emotional regulation to memory formation and sensory processing. This functional brain region encompasses specific parts of the diencephalon and the cerebrum, integrating the higher mental functions of the cerebral cortex with the primitive emotional responses of the deep brain...
8.3K

You might also read

Related Articles

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

Sort by
Same author

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026
Same author

FAIR in practice: minimum metadata schema for bioinformatics analytics by machines.

Journal of biomedical semantics·2026
Same author

INDIGENA: inductive prediction of disease-gene associations using phenotype ontologies.

Bioinformatics (Oxford, England)·2026
Same author

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026
Same author

LAFA: A Framework for Reproducible Longitudinal Assessment of Protein Function Annotation Models.

ArXiv·2026
Same author

Sexual plasticity of Hippolyte inermis Leach (Crustacea, Decapoda): Gene expression of vitellogenin and insulin-like androgenic gland hormone.

Animal reproduction science·2026

Related Experiment Video

Updated: Mar 3, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.7K

Neuro-symbolic representation learning on biological knowledge graphs.

Mona Alshahrani1, Mohammad Asif Khan1, Omar Maddouri1,2

  • 1Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.

Bioinformatics (Oxford, England)
|April 28, 2017
PubMed
Summary

We developed a new method for feature learning on biological knowledge graphs, combining symbolic logic and neural networks. This approach enhances machine learning applications for biological data integration and analysis.

More Related Videos

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.2K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.9K

Related Experiment Videos

Last Updated: Mar 3, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.7K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.2K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.9K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Biological data integration relies on Semantic Web technologies and knowledge graphs.
  • Feature learning on graph-structured data is emerging but underexplored in biology.
  • Existing methods lack the ability to fully leverage structured biological knowledge.

Purpose of the Study:

  • To develop a novel feature learning method for biological knowledge graphs.
  • To integrate symbolic logic and neural networks for enhanced biological data representation.
  • To improve machine learning applications in biology using knowledge graphs.

Main Methods:

  • Developed a hybrid approach combining symbolic logic (knowledge representation, automated reasoning) with neural networks.
  • Generated node embeddings that capture explicit and implicit information from knowledge graphs.
  • Applied embeddings to predict graph edges for function prediction, gene-disease associations, protein-protein interactions, and drug target relations.

Main Results:

  • The developed method generates embeddings encoding rich information from biological knowledge graphs.
  • Performance in predicting graph edges matches or surpasses traditional methods using manually crafted features.
  • The approach is applicable to any biological knowledge graph.

Conclusions:

  • The novel feature learning method effectively utilizes biological knowledge graphs for machine learning.
  • This work facilitates the application of machine learning and data analytics to Semantic Web-based biological knowledge bases.
  • The method offers a powerful tool for advancing biological data discovery and interpretation.