Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Machines01:19

Machines

581
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...
581
Machines: Problem Solving II01:30

Machines: Problem Solving II

674
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.
674
Machines: Problem Solving I01:22

Machines: Problem Solving I

719
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...
719
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
Associative Learning01:27

Associative Learning

1.4K
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...
1.4K
Purposive Learning01:22

Purposive Learning

513
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...
513

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

LAML-Pro: joint maximum likelihood inference of cell genotypes and cell lineage trees.

Bioinformatics (Oxford, England)·2026
Same author

Riemannian metric learning for alignment of spatial multiomics.

Bioinformatics (Oxford, England)·2026
Same author

Virtual Tumors Enable Prediction of Personalized Therapeutic Combinations for Non-Small Cell Lung Cancer.

Cancer research·2026
Same author

The tree labeling polytope: A unified approach to ancestral reconstruction problems.

Cell systems·2026
Same author

Dango: Predicting higher-order genetic interactions.

Cell systems·2026
Same author

Spatial Mapping of the Precancer-to-Cancer Transition in Breast and Prostate.

Cancer discovery·2026

相关实验视频

Updated: Feb 8, 2026

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

2.6K

生物医学中的可见机器学习

Michael K Yu1, Jianzhu Ma2, Jasmin Fisher3

  • 1Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA; Cancer Cell Map Initiative, University of California San Diego, La Jolla, CA, USA; UCSD Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA, USA.

Cell
|June 16, 2018
PubMed
概括

人工智能可以通过分析患者数据来改善治疗方法. 以实验生物学为指导的可见机器学习方法解决了诸如数据多样性和生物医学预测的洞察力不足等挑战.

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.1K

相关实验视频

Last Updated: Feb 8, 2026

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

2.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.1K

科学领域:

  • 生物医学研究
  • 人工智能
  • 机器学习

背景情况:

  • 将患者数据转化为有效的疗法是人工智能 (AI) 的关键目标.
  • 生物医学中的机器学习 (ML) 模型与高度多样化的数据以及对其预测的生物学基础的理解进行斗争.
  • 缺乏机械洞察力阻碍了人工智能的临床应用.

研究的目的:

  • 倡导生物医学中的"可见"人工智能方法.
  • 建议将实验生物学原则纳入机器学习模型设计.
  • 提高人工智能驱动的治疗策略的可解释性和可靠性.

主要方法:

  • 开发结合生物知识的机器学习框架.
  • 通过实验生物学的结构来设计AI模型.
  • 专注于"可见"的方法,以提高预测的透明度.

主要成果:

  • 拟议的可见人工智能方法为克服数据异质性挑战提供了途径.
  • 整合实验生物学增强了对人工智能预测的机制性理解.
  • 可见方法可以更好地将患者数据转化为疗法.

结论:

  • 以实验生物学为指导的可见人工智能对于推进生物医学应用至关重要.
  • 这种方法解决了当前医学机器学习的主要局限性.
  • 它促进了人工智能驱动的更可靠和可解释的治疗开发.