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Related Concept Videos

Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Synthetic Biology02:55

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
Purposive Learning01:22

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a bonus...
Introduction to Learning01:18

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
Associative Learning01:27

Associative Learning

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...

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Related Experiment Video

Updated: Jul 16, 2026

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

VITAGRAPH: building a knowledge graph for biologically relevant learning tasks.

Francesco Madeddu1,2, Lucia Testa1, Gianluca De Carlo1,3

  • 1Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185, Rome, Italy.

Scientific Data
|July 14, 2026
PubMed
Summary

VITAGRAPH is a new biological knowledge graph that integrates and refines public data. This resource enhances machine learning for computational biology tasks like drug repurposing and disease prediction.

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

Published on: June 13, 2025

Related Experiment Videos

Last Updated: Jul 16, 2026

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

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

Area of Science:

  • Computational Biology
  • Network Medicine
  • Bioinformatics

Background:

  • Human biology's complexity drives interdisciplinary research.
  • Artificial intelligence (AI) is crucial in computational biology, utilizing graph structures for biological networks.
  • Network medicine relies on these networks for gene-disease association, drug repurposing, and side-effect analysis.

Purpose of the Study:

  • To introduce VITAGRAPH, a comprehensive biological knowledge graph.
  • To integrate and refine multiple public datasets for improved data quality.
  • To enhance machine learning models for computational biology and precision medicine.

Main Methods:

  • Developed a pipeline extending the Drug Repurposing Knowledge Graph.
  • Resolved inconsistencies and redundancies in public biological datasets.
  • Enriched graph nodes with molecular fingerprints and gene ontologies.

Main Results:

  • Created VITAGRAPH, a multi-purpose biological knowledge graph.
  • Integrated information from leading public sources, ensuring coherence and reliability.
  • Incorporated biologically and chemically meaningful features to improve ML model embeddings.

Conclusions:

  • VITAGRAPH provides a reliable platform for advancing computational biology and precision medicine.
  • The knowledge graph facilitates benchmarking of graph-based models.
  • Enables tackling complex tasks like drug repurposing, protein-protein interaction prediction, and side-effect prediction.