Jove
Visualize
Contact Us

Related Concept Videos

Cognitive Learning01:21

Cognitive Learning

696
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...
696
Machines: Problem Solving I01:22

Machines: Problem Solving I

461
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
461
Machines: Problem Solving II01:30

Machines: Problem Solving II

428
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.
428
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

817
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
817
Associative Learning01:27

Associative Learning

658
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...
658
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

117
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
117

You might also read

Related Articles

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

Sort by
Same author

Drug response profile-based machine learning enables strategic cell line and compound selection for drug development.

Bioinformatics (Oxford, England)·2026
Same author

A case study of the application of AI to early stage drug discovery.

Scientific reports·2025
Same author

The influence of sex in diagnostic modelling of knee osteoarthritis.

PloS one·2025
Same author

Predicting gene expression using millions of yeast promoters reveals <i>cis</i>-regulatory logic.

Bioinformatics advances·2025
Same author

Scientific hypothesis generation by large language models: laboratory validation in breast cancer treatment.

Journal of the Royal Society, Interface·2025
Same author

Residual risk prediction in anticoagulated patients with atrial fibrillation using machine learning: A report from the GLORIA-AF registry phase II/III.

European journal of clinical investigation·2024
Same journal

In This Issue.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Long-term cultural continuity across the Neanderthal-modern human sequence at Üçağızlı II Cave, northern Levant.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Dolphins use names to remember whom to avoid.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Retraction for Shaked and Frenkel, Curiouser and curiouser: Meningeal lymphoid structures in the aging brain.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Small but mighty: The outsized role of small water bodies in the global carbon cycle.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Functional traits produce conditional outcomes in different community contexts.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles
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 Experiment Video

Updated: Oct 11, 2025

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

1.1K

Transformational machine learning: Learning how to learn from many related scientific problems.

Ivan Olier1, Oghenejokpeme I Orhobor2, Tirtharaj Dash3

  • 1School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool L3 5UX, United Kingdom.

Proceedings of the National Academy of Sciences of the United States of America
|November 30, 2021
PubMed
Summary
This summary is machine-generated.

Transformational machine learning (TML) creates new features from existing models, significantly boosting predictive performance across diverse scientific domains. This approach enhances machine learning (ML) models and provides deeper scientific insights.

Keywords:
AIdrug designmultitask learningstackingtransfer learning

More Related Videos

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.5K

Related Experiment Videos

Last Updated: Oct 11, 2025

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

1.1K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.5K

Area of Science:

  • Machine Learning
  • Computational Biology
  • Cheminformatics

Background:

  • Traditional machine learning relies on intrinsic features for representing data.
  • Related machine learning tasks offer potential for feature enhancement.
  • Transformational ML (TML) leverages predictions from other tasks to create novel representations.

Purpose of the Study:

  • To introduce and evaluate Transformational Machine Learning (TML).
  • To assess TML's impact on predictive performance across various machine learning methods and scientific domains.
  • To explore TML's contribution to scientific understanding via explainable ML.

Main Methods:

  • TML was applied to nonlinear machine learning models including random forests, gradient boosting machines, support vector machines, k-nearest neighbors, and neural networks.
  • Thousands of machine learning problems were utilized across drug design, gene expression prediction, and ML algorithm selection.
  • Extrinsic features generated by TML were compared against intrinsic features.

Main Results:

  • TML significantly improved predictive performance for all tested machine learning methods, with average gains ranging from 4% to 50%.
  • The extrinsic features generated by TML generally outperformed intrinsic features.
  • TML provided valuable insights in drug design, including drug target specificity and relationships between drugs and proteins.

Conclusions:

  • TML offers a synergistic approach to machine learning, enhancing model performance and interpretability.
  • TML fosters an ecosystem where tasks and predictions interact to improve overall ML capabilities.
  • Openly published data, code, and models support the TML ecosystem and its principles.